ai research engineer

NXP Semiconductors

<div><p>Senior Quantization Engineer - Edge AI Model Optimization<br>
</p><p><br>
We at NXP have an environment that fosters innovation. Our team has technology experts who understand the big picture and mentors who coach passionate professionals to work on the most exciting challenges. We share responsibilities in everything we do, where every point of view is valued. Join us!
</p><p>Job Summary
</p><p>We are seeking a highly skilled Edge AI Engineer/Scientist with a strong theoretical foundation in AI and solid software engineering expertise to contribute to our Edge AI Model Optimization program. While the primary focus of this role is on model quantization, the scope also includes complementary optimization strategies such as speculative decoding, pruning, and other methods for ensuring highly efficient on-device deployment.
</p><p>You will work at the forefront of innovation, bridging the gap between research and practice, focusing on CNNs, Large Language Model (LLM) and Vision Language Model (VLM) optimization to bring advanced capabilities to NXP&rsquo;s Ara2 family of NPUs, directly supporting the future of on‑device intelligence.
</p><p>If you want to the future of efficient on‑device AI, this is the place to be.
</p><p>Job Responsibilities
</p><p>Research: Actively survey the latest research (NeurIPS, ICLR, CVPR) on model optimization/compression, focusing particularly on neural network quantization, but also including other techniques like speculative decoding, pruning, etc.
<br>
Prototyping: Develop and adapt state-of-the-art methods to NXP&rsquo;s hardware constraints, building POCs to showcase the effectiveness of these techniques on NXP HW.
<br>
Production Implementation: Translate research prototypes into robust, optimized production code (C++/Python), ensuring strict memory and compute efficiency standards.
<br>
Systems Integration: Document algorithmic tradeoffs, derive deployment recipes, and mentor the engineering team on numerical methods and optimization.
<br>
Cross-Functional Leadership: Act as the technical bridge between AI Research, Hardware Engineering and other teams, providing quantified guidance on how choices impact model accuracy and performance.
<br>
IP Generation: Contribute to NXP&rsquo;s intellectual property portfolio through patents and technical publications.
</p><p>Job Qualifications
</p><p>Required Background
</p><p>Education: MSc or Ph.D (is a plus) in Computer Science, Electrical Engineering, or Mathematics with a focus on Machine Learning or Deep Learning.
<br>
AI Expertise: Proven practical experience in AI/ML with a deep understanding of CNN architectures and Generative AI (Transformers, LLMs, VLMs, etc.).
<br>
Technical Stack: Strong hands-on experience with PyTorch, ONNX, and model conversion/optimization pipelines.
<br>
Software Engineering: Proficient in Python and C++ and best development practices.
<br>
Embedded Mindset: Familiarity with the constraints of embedded systems (latency, power, memory bandwidth) and how code interacts with underlying hardware.
</p><p>Preferred
</p><p>Advanced AI: Experience with state-of-the-art quantization techniques for discriminative and generative AI (e.g., GPTQ, SpinQuant, etc).
<br>
Hardware Acceleration: Experience with NPUs, device-level profiling, and diagnosing memory bottlenecks.
<br>
Kernel Development: Experience with custom kernel development is a plus.
<br>
Compilers: Knowledge of MLIR or TVM is a significant plus.<br>
</p><p><br>
We at NXP have an environment that fosters innovation. Our team has technology experts who understand the big picture and mentors who coach passionate professionals to work on the most exciting challenges. We share responsibilities in everything we do, where every point of view is valued. Join us!
</p><p>Job Summary
</p><p>We are seeking a highly skilled Edge AI Engineer/Scientist with a strong theoretical foundation in AI and solid software engineering expertise to contribute to our Edge AI Model Optimization program. While the primary focus of this role is on model quantization, the scope also includes complementary optimization strategies such as speculative decoding, pruning, and other methods for ensuring highly efficient on-device deployment.
</p><p>You will work at the forefront of innovation, bridging the gap between research and practice, focusing on CNNs, Large Language Model (LLM) and Vision Language Model (VLM) optimization to bring advanced capabilities to NXP&rsquo;s Ara2 family of NPUs, directly supporting the future of on‑device intelligence.
</p><p>If you want to the future of efficient on‑device AI, this is the place to be.
</p><p>Job Responsibilities
</p><p>Research: Actively survey the latest research (NeurIPS, ICLR, CVPR) on model optimization/compression, focusing particularly on neural network quantization, but also including other techniques like speculative decoding, pruning, etc.
<br>
Prototyping: Develop and adapt state-of-the-art methods to NXP&rsquo;s hardware constraints, building POCs to showcase the effectiveness of these techniques on NXP HW.
<br>
Production Implementation: Translate research prototypes into robust, optimized production code (C++/Python), ensuring strict memory and compute efficiency standards.
<br>
Systems Integration: Document algorithmic tradeoffs, derive deployment recipes, and mentor the engineering team on numerical methods and optimization.
<br>
Cross-Functional Leadership: Act as the technical bridge between AI Research, Hardware Engineering and other teams, providing quantified guidance on how choices impact model accuracy and performance.
<br>
IP Generation: Contribute to NXP&rsquo;s intellectual property portfolio through patents and technical publications.
</p><p>Job Qualifications
</p><p>Required Background
</p><p>Education: MSc or Ph.D (is a plus) in Computer Science, Electrical Engineering, or Mathematics with a focus on Machine Learning or Deep Learning.
<br>
AI Expertise: Proven practical experience in AI/ML with a deep understanding of CNN architectures and Generative AI (Transformers, LLMs, VLMs, etc.).
<br>
Technical Stack: Strong hands-on experience with PyTorch, ONNX, and model conversion/optimization pipelines.
<br>
Software Engineering: Proficient in Python and C++ and best development practices.
<br>
Embedded Mindset: Familiarity with the constraints of embedded systems (latency, power, memory bandwidth) and how code interacts with underlying hardware.
</p><p>Preferred
</p><p>Advanced AI: Experience with state-of-the-art quantization techniques for discriminative and generative AI (e.g., GPTQ, SpinQuant, etc).
<br>
Hardware Acceleration: Experience with NPUs, device-level profiling, and diagnosing memory bottlenecks.
<br>
Kernel Development: Experience with custom kernel development is a plus.
<br>
Compilers: Knowledge of MLIR or TVM is a significant plus.<br>
</p><p><br>
More information about NXP in India...
</p><p></p>#LI-29f4</div>

Senior Quantization Engineer - Edge AI

Deloitte

<div>
 Three hundred fifty million Americans rely on a healthcare system whose decision-making has become slow, costly, and adversarial - care delayed by prior authorization and paperwork, claims that misfire, clinical decisions made without the right information at the right moment, and patients who struggle to navigate or afford the care they need. Deloitte has a new AI-first effort,, backed by $1B in committed investment, building the reasoning models and agentic systems to rebuild how that system decides - across payers, providers, and life sciences, and for the patients they serve - so that care is faster, fairer, and far less wasteful. This is not AI applied at the margins. It is a ground-up rebuild of the decision-making machinery behind American healthcare, at national scale.
 <br/>
 <br/>
 This is resourced to do real post-training at scale - committed investment in GPU compute and training infrastructure, not toy fine-tunes.
 <br/>
 <br/>
 As a Research Engineer on our post-training team, you will design, train, evaluate, and align the models that reason about healthcare - working across the full post-training lifecycle to shape model behavior for clinical and operational decisioning across the industry. Healthcare decisioning is one of the cleanest verifiable-reward domains outside math and code: the problems are hard. We ground that reward in real signals - clinical policy and criteria, adjudicated outcomes, and clinical-expert judgment - so correctness is checkable rather than asserted.
 <br/>
 <br/>
 You will own the post-training stack for our clinical reasoning models end to end - from data and reward design through trained, evaluated models that ship. This is not a prompt-engineering role. We are looking for people who understand not just how to use LLMs, but how to improve and shape model behavior through advanced post-training.
 <br/>
 <br/>
 You do not need a healthcare background. We pair every engineer with clinical and domain experts and teach you the domain - you bring the modeling depth.
 <br/>
 <br/>
 We hire on demonstrated depth, not years - the level you join at is determined through our interview process, based on the depth and judgment you demonstrate, not your years in a title.
 <br/>
 <br/>
 Work you'll do
 <br/>
 <br/>
 Post-training &amp; alignment
 <br/>
 <br/>
 <ul>
  <li>
   Design and execute post-training pipelines: supervised fine-tuning (SFT), preference optimization, and reinforcement learning / alignment workflows.
  </li>
  <li>
   Build and optimize training using techniques such as SFT, RLHF, PPO, DPO, GRPO, RLAIF, and Constitutional AI, and understand how each affects reasoning quality, safety, latency, cost, and reliability.
  </li>
  <li>
   Train reasoning models for healthcare decisioning using verifiable-reward RL - designing reward signals and verifiers grounded in clinical guidelines, policy and criteria, and adjudicated outcomes.
   <br/>
   <br/>
  </li>
 </ul>
 Reward modeling &amp; data
 <br/>
 <br/>
 <ul>
  <li>
   Develop reward models and preference datasets to improve reasoning quality, factuality, safety, policy adherence, and task performance.
  </li>
  <li>
   Curate, clean, synthesize, and evaluate large-scale instruction, preference, and domain-specific datasets, with rigorous filtering, deduplication, and quality control.
  </li>
  <li>
   Build verification and reward pipelines from our proprietary clinical, claims, and operational data and from clinical-expert labeling - turning guidelines, policy, and adjudicated outcomes into checkable reward signals at scale.
   <br/>
   <br/>
  </li>
 </ul>
 Efficient fine-tuning, training &amp; inference infrastructure
 <br/>
 <br/>
 <ul>
  <li>
   Implement efficient fine-tuning strategies including LoRA, QLoRA, PEFT, and adapter-based approaches; build scalable distributed training using DeepSpeed, FSDP, Megatron-LM, Ray, or equivalent.
  </li>
  <li>
   Optimize inference performance - latency, throughput, quantization, and deployment efficiency - for production, including frameworks such as vLLM, TensorRT-LLM, or TGI.
   <br/>
   <br/>
  </li>
 </ul>
 Small language models &amp; open-weight models
 <br/>
 <br/>
 <ul>
  <li>
   Train and optimize open-weight models such as Llama, Qwen, Mistral, or DeepSeek; build specialized small language models (SLMs) for on-premise and cloud-hybrid deployment with strong performance-per-dollar.
   <br/>
   <br/>
  </li>
 </ul>
 Evaluation, safety &amp; red teaming
 <br/>
 <br/>
 <ul>
  <li>
   Design evaluation frameworks covering reasoning, hallucination detection, factuality, instruction following, structured outputs, and domain-specific metrics.
  </li>
  <li>
   Build healthcare-grade evaluation - held-out clinical benchmarks, deployment regression gates, calibration and uncertainty, factuality against ground truth, and bias/fairness evaluation across patient populations and subgroups - co-designed with clinical experts.
  </li>
  <li>
   Apply PHI/HIPAA-aware data handling and produce model documentation suitable for regulated clinical use.
  </li>
  <li>
   Perform red teaming and adversarial testing to identify alignment failures, unsafe behaviors, jailbreak vulnerabilities, and regression risks; collaborate with agentic and application teams to improve tool use, grounding, and long-horizon reasoning.
   <br/>
   <br/>
  </li>
 </ul>
 The team
 <br/>
 <br/>
 Deloitte brings together AI researchers, modeling and platform engineers, architects, clinical and domain specialists, and product leaders to build, deploy, and operate verticalized AI systems across software, data, models, and cloud infrastructure - engineered for one of the most complex operating environments in the world. The work spans the healthcare industry - payers, providers, and life sciences - and involves genuinely hard reasoning problems, nuanced operational workflows, and a high bar for reliability, with little tolerance for shallow or unreliable outputs. We pair frontier AI research with production-grade engineering, and we ship into real clinical and operational settings rather than leaving models in the lab.
 <br/>
 <br/>
 You can go deep. The team sub-specializes across post-training research, data and reward engineering, and training and inference infrastructure - you won't be expected to own all of it alone.
 <br/>
 <br/>
 <strong>
  Required Qualifications
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Bachelor's degree in Computer Science, Machine Learning, Artificial Intelligence, Applied Mathematics, Computational Linguistics, or a related field.
  </li>
  <li>
   Demonstrated depth training and post-training large transformer-based language models in production or research - this is your craft, not coursework or a one-off fine-tune. Genuine depth including SFT and at least one preference-optimization or RL method, evidenced by shipped models, releases, or research.
  </li>
  <li>
   Hands-on experience with reasoning-model training and/or verifiable-reward (RLVR) workflows.
  </li>
  <li>
   Strong understanding of modern post-training techniques: SFT, RLHF, PPO, DPO, GRPO, RLAIF, and preference optimization workflows.
  </li>
  <li>
   Experience with open-weight foundation models such as Llama, Qwen, Mistral, DeepSeek, or equivalent architectures.
  </li>
  <li>
   Strong expertise in PyTorch and modern deep-learning tooling; experience with distributed training frameworks such as DeepSpeed, FSDP, Megatron-LM, or Ray.
  </li>
  <li>
   Experience implementing efficient fine-tuning techniques such as LoRA, QLoRA, PEFT, and quantization-aware workflows.
  </li>
  <li>
   Deep understanding of transformer architectures, tokenization, attention mechanisms, decoding strategies, and model scaling trade-offs.
  </li>
  <li>
   Strong grasp of LLM evaluation methodologies, benchmarking, reward modeling, and alignment trade-offs; experience with large-scale and synthetic datasets, filtering, deduplication, and quality-control pipelines.
  </li>
  <li>
   Strong Python engineering skills and production-grade software practices; ability to work through ambiguous, highly complex technical problems in fast-moving environments.
  </li>
  <li>
   Ability to travel 0-50%, on average, based on the work you do and the clients and industries/sectors you serve.
  </li>
  <li>
   Limited immigration sponsorship may be available.
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Preferred Qualifications
  <br/>
  <br/>
 </strong>
 <ul>
  <li>
   Experience building or optimizing reasoning models, agentic models, or tool-using LLM systems.
  </li>
  <li>
   Familiarity with inference optimization frameworks such as vLLM, TensorRT-LLM, TGI, or Ollama.
  </li>
  <li>
   Experience with multimodal models, speech models, or domain-specific foundation models; experience using large-scale GPU clusters and distributed compute.
  </li>
  <li>
   Contributions to open-source AI projects, research publications, benchmark development, or model releases.
  </li>
  <li>
   Familiarity with safety, governance, and responsible-AI practices; experience in regulated or high-stakes industries such as healthcare, finance, insurance, or public sector.
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  Compensation
  <br/>
  <br/>
 </strong>
 Base salary is benchmarked to leading technology companies rather than traditional consulting scales, and the role carries a substantial performance-based incentive opportunity designed to grow with the value you help create - startup-style upside, with the backing of a committed, well-capitalized platform. The estimated base salary range is $134,500-$265,100 (not adjusted for geographic differential); actual base pay depends on your skills, experience, and level, and you may also be eligible for a discretionary annual incentive based on individual and organizational performance.
</div>


Research Engineer — Post-Training & Small Language Models (SLMs), Healthcare AI

<div>Three hundred fifty million Americans rely on a healthcare system whose decision-making has become slow, costly, and adversarial - care delayed by prior authorization and paperwork, claims that misfire, clinical decisions made without the right information at the right moment, and patients who struggle to navigate or afford the care they need. Deloitte has a new AI-first effort,, backed by $1B in committed investment, building the reasoning models and agentic systems to rebuild how that system decides - across payers, providers, and life sciences, and for the patients they serve - so that care is faster, fairer, and far less wasteful. This is not AI applied at the margins. It is a ground-up rebuild of the decision-making machinery behind American healthcare, at national scale.<br>
<br>
This is resourced to do real post-training at scale - committed investment in GPU compute and training infrastructure, not toy fine-tunes.<br>
<br>
As a Research Engineer on our post-training team, you will design, train, evaluate, and align the models that reason about healthcare - working across the full post-training lifecycle to shape model behavior for clinical and operational decisioning across the industry. Healthcare decisioning is one of the cleanest verifiable-reward domains outside math and code: the problems are hard. We ground that reward in real signals - clinical policy and criteria, adjudicated outcomes, and clinical-expert judgment - so correctness is checkable rather than asserted.<br>
<br>
You will own the post-training stack for our clinical reasoning models end to end - from data and reward design through trained, evaluated models that ship. This is not a prompt-engineering role. We are looking for people who understand not just how to use LLMs, but how to improve and shape model behavior through advanced post-training.<br>
<br>
You do not need a healthcare background. We pair every engineer with clinical and domain experts and teach you the domain - you bring the modeling depth.<br>
<br>
We hire on demonstrated depth, not years - the level you join at is determined through our interview process, based on the depth and judgment you demonstrate, not your years in a title.<br>
<br>
Work you'll do<br>
<br>
Post-training &amp; alignment<br>
<br>
<ul><li>Design and execute post-training pipelines: supervised fine-tuning (SFT), preference optimization, and reinforcement learning / alignment workflows.</li></ul><br>
<ul><li>Build and optimize training using techniques such as SFT, RLHF, PPO, DPO, GRPO, RLAIF, and Constitutional AI, and understand how each affects reasoning quality, safety, latency, cost, and reliability.</li></ul><br>
<ul><li>Train reasoning models for healthcare decisioning using verifiable-reward RL - designing reward signals and verifiers grounded in clinical guidelines, policy and criteria, and adjudicated outcomes.</li></ul><br>
Reward modeling &amp; data<br>
<br>
<ul><li>Develop reward models and preference datasets to improve reasoning quality, factuality, safety, policy adherence, and task performance.</li></ul><br>
<ul><li>Curate, clean, synthesize, and evaluate large-scale instruction, preference, and domain-specific datasets, with rigorous filtering, deduplication, and quality control.</li></ul><br>
<ul><li>Build verification and reward pipelines from our proprietary clinical, claims, and operational data and from clinical-expert labeling - turning guidelines, policy, and adjudicated outcomes into checkable reward signals at scale.</li></ul><br>
Efficient fine-tuning, training &amp; inference infrastructure<br>
<br>
<ul><li>Implement efficient fine-tuning strategies including LoRA, QLoRA, PEFT, and adapter-based approaches; build scalable distributed training using DeepSpeed, FSDP, Megatron-LM, Ray, or equivalent.</li></ul><br>
<ul><li>Optimize inference performance - latency, throughput, quantization, and deployment efficiency - for production, including frameworks such as vLLM, TensorRT-LLM, or TGI.</li></ul><br>
Small language models &amp; open-weight models<br>
<br>
<ul><li>Train and optimize open-weight models such as Llama, Qwen, Mistral, or DeepSeek; build specialized small language models (SLMs) for on-premise and cloud-hybrid deployment with strong performance-per-dollar.</li></ul><br>
Evaluation, safety &amp; red teaming<br>
<br>
<ul><li>Design evaluation frameworks covering reasoning, hallucination detection, factuality, instruction following, structured outputs, and domain-specific metrics.</li></ul><br>
<ul><li>Build healthcare-grade evaluation - held-out clinical benchmarks, deployment regression gates, calibration and uncertainty, factuality against ground truth, and bias/fairness evaluation across patient populations and subgroups - co-designed with clinical experts.</li></ul><br>
<ul><li>Apply PHI/HIPAA-aware data handling and produce model documentation suitable for regulated clinical use.</li></ul><br>
<ul><li>Perform red teaming and adversarial testing to identify alignment failures, unsafe behaviors, jailbreak vulnerabilities, and regression risks; collaborate with agentic and application teams to improve tool use, grounding, and long-horizon reasoning.</li></ul><br>
The team<br>
<br>
Deloitte brings together AI researchers, modeling and platform engineers, architects, clinical and domain specialists, and product leaders to build, deploy, and operate verticalized AI systems across software, data, models, and cloud infrastructure - engineered for one of the most complex operating environments in the world. The work spans the healthcare industry - payers, providers, and life sciences - and involves genuinely hard reasoning problems, nuanced operational workflows, and a high bar for reliability, with little tolerance for shallow or unreliable outputs. We pair frontier AI research with production-grade engineering, and we ship into real clinical and operational settings rather than leaving models in the lab.<br>
<br>
You can go deep. The team sub-specializes across post-training research, data and reward engineering, and training and inference infrastructure - you won't be expected to own all of it alone.<br>
<br>
Required qualifications<br>
<br>
<ul><li>Bachelor's degree in Computer Science, Machine Learning, Artificial Intelligence, Applied Mathematics, Computational Linguistics, or a related field.</li></ul><br>
<ul><li>Demonstrated depth training and post-training large transformer-based language models in production or research - this is your craft, not coursework or a one-off fine-tune. Genuine depth including SFT and at least one preference-optimization or RL method, evidenced by shipped models, releases, or research.</li></ul><br>
<ul><li>Hands-on experience with reasoning-model training and/or verifiable-reward (RLVR) workflows.</li></ul><br>
<ul><li>Strong understanding of modern post-training techniques: SFT, RLHF, PPO, DPO, GRPO, RLAIF, and preference optimization workflows.</li></ul><br>
<ul><li>Experience with open-weight foundation models such as Llama, Qwen, Mistral, DeepSeek, or equivalent architectures.</li></ul><br>
<ul><li>Strong expertise in PyTorch and modern deep-learning tooling; experience with distributed training frameworks such as DeepSpeed, FSDP, Megatron-LM, or Ray.</li></ul><br>
<ul><li>Experience implementing efficient fine-tuning techniques such as LoRA, QLoRA, PEFT, and quantization-aware workflows.</li></ul><br>
<ul><li>Deep understanding of transformer architectures, tokenization, attention mechanisms, decoding strategies, and model scaling trade-offs.</li></ul><br>
<ul><li>Strong grasp of LLM evaluation methodologies, benchmarking, reward modeling, and alignment trade-offs; experience with large-scale and synthetic datasets, filtering, deduplication, and quality-control pipelines.</li></ul><br>
<ul><li>Strong Python engineering skills and production-grade software practices; ability to work through ambiguous, highly complex technical problems in fast-moving environments.</li></ul><br>
<ul><li>Ability to travel 0-50%, on average, based on the work you do and the clients and industries/sectors you serve.</li></ul><br>
<ul><li>Limited immigration sponsorship may be available.</li></ul><br>
Preferred qualifications<br>
<br>
<ul><li>Experience building or optimizing reasoning models, agentic models, or tool-using LLM systems.</li></ul><br>
<ul><li>Familiarity with inference optimization frameworks such as vLLM, TensorRT-LLM, TGI, or Ollama.</li></ul><br>
<ul><li>Experience with multimodal models, speech models, or domain-specific foundation models; experience using large-scale GPU clusters and distributed compute.</li></ul><br>
<ul><li>Contributions to open-source AI projects, research publications, benchmark development, or model releases.</li></ul><br>
<ul><li>Familiarity with safety, governance, and responsible-AI practices; experience in regulated or high-stakes industries such as healthcare, finance, insurance, or public sector.</li></ul><br>
Compensation<br>
<br>
Base salary is benchmarked to leading technology companies rather than traditional consulting scales, and the role carries a substantial performance-based incentive opportunity designed to grow with the value you help create - startup-style upside, with the backing of a committed, well-capitalized platform. The estimated base salary range is $134,500-$265,100 (not adjusted for geographic differential); actual base pay depends on your skills, experience, and level, and you may also be eligible for a discretionary annual incentive based on individual and organizational performance.</div>

Valence

<div><p>Valence has built the only first-to-market AI native coaching platform for enterprise, offering personalized, expert, and human-like guidance and support to any leader or employee. We&rsquo;re not just talking about the future of work &mdash; we&rsquo;re building it now, with the most innovative Fortune 500 companies across healthcare, financial services, manufacturing, and technology.</p><p>
Learn more about our work with Microsoft: Valence Brings AI Coaching More Deeply Into the Flow of Work with Microsoft 365</p><p>
Our focus is on the problems that actually decide whether AI changes how organizations operate &mdash; the ones with no playbook, no obvious answers, and no guarantee of success. If you want to be part of the small group that defines how AI transforms the future at a global scale, this is your chance.</p><p>
And this isn&rsquo;t for everyone. We&rsquo;re not looking for people who want predictability or incremental progress. We only want those who are restless at the edge of what&rsquo;s possible, who get bored when things feel &ldquo;done,&rdquo; and who are driven to redefine what AI can mean for leaders, companies, and the world. Because at Valence, the work worth doing is the kind that redefines work itself.</p><div></div><p><b><br>
The Role</b></p><p>
As a Platform Engineer at Valence, you will build and operate the secure, reliable, and scalable foundation that lets every product and AI team ship Nadia innovations faster and with lower risk. Platform Engineering is Valence's force multiplier: we own the shared control planes for trust and safety, global access and routing, LLM governance, deployment automation, workload isolation, observability and SLOs, and AI-native engineering execution.</p><p>
Your mandate is simple: increase shipping velocity and customer trust at the same time, by making the safe path the default path. You'll build the paved roads every team uses in production, and enforce minimum production standards through templates, CI/CD gates, scorecards, and time-bound exceptions, so every service is observable, reversible, and secure by default.</p><p>
We don't expect any one candidate to check every technical box, but we do look for engineers who are eager to learn, iterate rapidly, and contribute across challenges that span reliability, security, global delivery, and developer experience.</p><div></div><p><b><br>
About Valence</b></p><p>
We're the only company pioneering leadership coaching for large enterprises in an AI-first way. Our mission is to transform how the world's biggest companies approach learning and development, helping teams work better together through AI-powered personalization that adapts to individual goals and organizational culture using the latest advances in machine learning and natural language processing.</p><div></div><p><br>
We've been featured in Harvard Business Review, TIME, World Economic Forum, Financial Times, Forbes and an Inc. 5000 fastest-growing private companies in America. Our clients represent the most diverse and sophisticated enterprise AI implementations globally, including Coca-Cola, Delta, Nestl&eacute;, General Mills, Schneider Electric, Deutsche Telekom, AstraZeneca, Prudential, CVS and Bristol Myers Squibb.</p><div></div><p><br>
Working at Valence means you'll work directly with Fortune 500 technology leaders, building expertise through the most complex enterprise deployments while gaining insight into diverse organizational approaches to AI transformation. These aren't just any enterprise clients - they're the companies defining what AI-first business transformation looks like across every major industry.</p><p></p><p><b>
What You'll Do</b></p><ul><li><p><b>
Build the paved roads.</b> Design and operate shared platform primitives used by multiple teams (authorization and routing guardrails, secure logging, rollout controls, observability, queue infrastructure, and the centralized LLM gateway) so teams build on standards instead of reinventing them.</p></li><li><p><b>
Make change safe by default.</b> Own the reliability and change-safety systems that let the org move fast without breaking trust: CI/CD gates, migration safety patterns, progressive delivery, rollback automation, and kill switches.</p></li><li><p><b>
Enforce security at the platform layer.</b> Ship secure defaults, policy enforcement frameworks, auditability, and data-access guard libraries so the safe path is the easy path.</p></li><li><p><b>
Run global delivery controls.</b> Implement single-domain strategy, region pinning and routing, residency-aware behavior, and cross-region config consistency checks for enterprise customers worldwide.</p></li><li><p><b>
Raise the operational bar.</b> Establish SLOs, synthetic checks, alerting standards, and incident runbooks and drills that drive faster detection and recovery.</p></li><li><p><b>
Build the internal developer platform.</b> Create service templates, blessed internal-tool paths, and standard deploy, monitoring, and auth patterns that make the right thing the default thing.</p></li><li><p><b>
Operate an AI-native engineering plane.</b> Build issue-to-PR automation infrastructure with governance, human-in-control workflows, and audit logs that convert signals into auditable, human-approved execution outcomes.</p></li></ul><p></p><p><b>
What We're Looking For</b></p><ul><li><p><b>
Technical foundation:</b> 8+ years of experience in software engineering, distributed systems, infrastructure, or platform/SRE roles, with a track record of building systems other teams depend on.</p></li><li><p><b>
Platform mindset:</b> You build reusable primitives, not one-off features. You think in terms of paved roads, secure defaults, and standards that scale across many teams.</p></li><li><p><b>
Reliability and change-safety expertise:</b> Hands-on experience with CI/CD, progressive delivery, rollback and migration safety, observability, SLOs, and incident response.</p></li><li><p><b>
Cloud &amp; deployment experience:</b> Comfortable developing and operating services in cloud environments (AWS, GCP, Azure) and working with containerization and orchestration (Docker, Kubernetes).</p></li><li><p><b>
Security &amp; compliance awareness:</b> Familiarity with implementing platform-level guardrails (authorization, auditability, data-access controls, and policy enforcement) in a privacy- and residency-conscious way.</p></li><li><p><b>
Engineering excellence:</b> Strong software engineering skills, including writing maintainable code, debugging distributed systems, and collaborating in cross-functional teams.</p></li><li><p><b>
Growth orientation:</b> Eagerness to tackle unfamiliar problems, learn new technologies, and shape our platform and culture.</p></li><li><p><b>
Communication:</b> Ability to explain technical ideas clearly and work effectively with both technical and non-technical stakeholders.</p></li></ul><p></p><p><b>
What You'll Get</b></p><ul><li><p><b>
Ownership &amp; Rapid Growth</b></p></li><li><p>
Outsized missions from day one, with direct responsibility for company-defining projects</p></li><li><p>
Work alongside the executive team with transparency into strategy and decision-making</p></li><li><p>
Influence on direction through real-time customer feedback and market insights</p></li></ul><div></div><ul><li><p><b><br>
AI-First Operator</b></p></li><li><p>
Work directly with cutting-edge AI models and next-generation platforms</p></li><li><p>
Build expertise in enterprise AI implementation across Fortune 500 companies and multiple industries</p></li><li><p>
Establish yourself as a recognized leader among peers in shaping how AI transforms work at a global scale</p></li></ul><div></div><ul><li><p><b><br>
Compensation</b></p></li><li><p>
Competitive salary including base + bonuses</p></li><li><p>
Comprehensive health coverage (medical, dental, vision) from day one</p></li><li><p>
Generous PTO, company-wide R&amp;R shutdowns, and paid parental leave</p></li><li><p>
Retirement plan support for US and global employees</p></li></ul><div></div><ul><li><p><b><br>
Equity</b></p></li><li><p>
Meaningful ownership in a venture-backed company at a growth inflection point</p></li><li><p>
Financial upside that comes from scaling fast</p></li><li><p>
Top-up grants as we scale and you deliver exceptional performance &mdash; your compensation grows alongside your impact</p></li></ul><div></div><ul><li><p><b><br>
Top-Performing Culture</b></p></li><li><p>
A culture built for top talent: intensity to win, growth without limits, and a team that solves hard problems and celebrates big wins together</p></li></ul><div></div><p><b><br>
Learn more about us and meet our team </b><b>here</b></p><p></p><p><b>
Location and Work Environment</b></p><p>
If candidates are based in NYC or Toronto they can work hybrid in our offices, otherwise this role can be remote. Candidates must be comfortable working with colleagues in different time zones (UK), and have valid travel documents without work authorization restrictions in the US.</p><div></div><p><b><br>
Diversity and Inclusion</b></p><p>
We are dedicated to creating a diverse and inclusive environment where everyone feels valued and supported. We encourage applications from candidates of all backgrounds and offer accommodations upon request throughout the hiring process. If you have any questions, please reach out to Allison Langille, Head of People, at jobs@valence.co.</p><div></div><p><b><br>
Employment Verification &amp; Commitment</b></p><p>
We use third-party services to verify employment history, education, and other information relevant to your candidacy. Employment is contingent upon the successful completion of these verification checks. This is a full-time role that requires a high level of focus, availability, and commitment. Employees may not hold concurrent full-time employment with another organization while employed at Valence. Any outside consulting, advisory, freelance, or other professional work must be disclosed and approved in advance and must not interfere with job responsibilities, availability, performance, or create a conflict of interest - including risks related to confidentiality, intellectual property, or competition.</p><p></p><p><i>
Valence is committed to protecting the privacy of all applicants. Information you submit will be used for recruiting, evaluation, legal compliance, and recordkeeping purposes only. We do not require access to personal account credentials at any stage of the hiring process. For more information, see our </i><i>Candidate Privacy Notice</i><i>.</i></p><div></div><p><br>
#LI-HYBRID</p></div>

Staff Software Engineer, Platform

Spotify

<div><div><div><div><div><div>Engineering</div></div></div><div><div>
Machine Learning</div></div></div><div><div>
Permanent</div></div><div><div>
New York</div></div><div><div><div>
<div>We design Spotify&rsquo;s consumer experience&mdash;end to end, moment to moment, across every screen, platform, and partner integration. Our mission is to make listening feel effortless, personal, and joyful for billions of users around the world. That means turning complexity into clarity across hundreds of touchpoints&mdash;from our mobile and desktop apps to the smart speakers, TVs, cars, and integrations where Spotify shows up every day. If it touches a consumer, we shape it. We bring deep insight into human behavior, design, and technology to craft experiences that feel intuitive, expressive, and unmistakably Spotify.<br>
<br>
The Verbatim squad sits within the Enrichment &amp; Content Intelligence product area and is focused on helping Spotify better understand audio, text, and visual content through machine learning. The team develops technologies that power experiences across Spotify including content skipping, transcription, moderation, and visual understanding. Working at the intersection of large-scale machine learning and product innovation, the squad partners closely with Product, Engineering, and Data Science teams to build intelligent systems that improve how users experience content across the platform.</div>
</div></div></div></div><div><div><p>
What You'll Do</p><ul><div>
<ul>
<li>Lead end-to-end machine learning initiatives from ideation and prototyping through experimentation, deployment, and large-scale productionization.</li>
<li>Design, develop, and deploy machine learning systems that operate across hundreds of millions of content signals using both real-time and batch processing architectures.</li>
<li>Advance Spotify&rsquo;s capabilities in natural language understanding, multimodal AI, and content intelligence.<br>
Build and evaluate LLM-powered solutions using modern prompting techniques, retrieval systems, and advanced model orchestration approaches.</li>
<li>Define rigorous evaluation methodologies including golden datasets, precision and recall frameworks, offline benchmarking, and online experimentation.</li>
<li>Partner closely with Product Managers, Engineering Managers, Staff Engineers, and Data Scientists to influence technical strategy and roadmap decisions.</li>
<li>Mentor engineers across the organization and help elevate machine learning engineering standards and best practices.</li>
<li>Contribute to the adoption of AI-assisted development workflows and tooling that improve team productivity and engineering effectiveness.</li>
</ul>
</div></ul></div></div><div><div><p>Who You Are</p><ul><div>
<ul>
<li>You have solid experience developing and deploying machine learning systems in production environments.</li>
<li>You have successfully delivered large-scale machine learning architectures operating on substantial datasets and high-throughput production systems.</li>
<li>You have deep experience with machine learning, deep learning, and modern AI technologies.</li>
<li>You have hands-on experience working with large language models and understand how to evaluate, adapt, and deploy them effectively for real-world product challenges.</li>
<li>You have experience building evaluation frameworks and can quantify model performance through robust experimentation and measurement techniques.</li>
<li>You know how to navigate ambiguity and make thoughtful technical trade-offs that balance product impact, scalability, and engineering quality.</li>
<li>You have experience influencing technical direction across cross-functional teams and can communicate complex machine learning concepts to diverse audiences.</li>
<li>You care about developing others and enjoy mentoring engineers through technical guidance and collaboration.</li>
<li>You have experience working with NLP, prompt engineering, retrieval-augmented generation (RAG), vector databases, or multimodal machine learning systems.</li>
<li>You are curious about emerging AI technologies and excited about integrating tools such as Claude Code, Cursor, and other AI-assisted development capabilities into engineering workflows.</li>
</ul>
</div></ul></div></div><div><div><p>Where You'll Be</p><ul><div>
<ul>
<li>This role is based in New York City</li>
<li>We offer you the flexibility to work where you work best! There will be some in person meetings, but still allows for flexibility to work from home.</li>
</ul>
</div></ul></div></div><div><div><p>Learn about life at Spotify</p></div><div><div>
<div>
The United States base range for this position is $184,049&ndash;262,928 USD, plus equity. The benefits available for this position include health insurance, six-month paid parental leave, 401(k) retirement plan, monthly meal allowance, 23 paid days off, paid flexible holidays, and paid sick leave. These ranges may be modified in the future.</div>
</div></div></div><div><div><p>
Our global benefits</p></div><div></div><div><div>
Extensive learning opportunities, through our dedicated team, GreenHouse.</div></div><div></div><div><div>
Flexible share incentives letting you choose how you share in our success.</div></div><div></div><div><div>
Global parental leave, six months off - for all new parents.</div></div><div></div><div><div>
All The Feels, our employee assistance program and self-care hub.</div></div><div></div><div><div>
Flexible public holidays, swap days off according to your values and beliefs.</div></div></div></div><div></div>

Senior Machine Learning Engineer, Content Intelligence

Inherent Technologies

<div>
 <strong>
  Job Description
  <br/>
  <br/>
 </strong>
 Seeking a
 <strong>
  hands-on AI Engineer/Developer
 </strong>
 to rapidly prototype and deliver AI-driven solutions across investment, research, and trading functions. This role is ideal for someone who can bridge
 <strong>
  financial domain knowledge with applied AI/ML development
 </strong>
 , building proof-of-concepts that can evolve into production-grade tools supporting investment decision-making.
 <br/>
 <br/>
 <strong>
  Key Responsibilities
  <br/>
  <br/>
 </strong>
 Design and develop
 <strong>
  AI/ML prototypes
 </strong>
 using Python to support investment research, portfolio analytics, and trading insights
 <br/>
 <br/>
 Build and test
 <strong>
  LLM-based and data-driven use cases
 </strong>
 (e.g., research summarization, signal generation, document analysis, automation)
 <br/>
 <br/>
 Work closely with
 <strong>
  investment professionals, research teams, and data engineers
 </strong>
 to translate business needs into functional AI solutions
 <br/>
 <br/>
 Develop and integrate
 <strong>
  data pipelines, APIs, and analytical models
 </strong>
 to support rapid experimentation
 <br/>
 <br/>
 Leverage tools such as
 <strong>
  Jupyter, Pandas, NumPy, LangChain, and cloud AI services (AWS/Azure/OpenAI)
  <br/>
  <br/>
 </strong>
 Perform
 <strong>
  data exploration, feature engineering, and model validation
 </strong>
 on financial datasets
 <br/>
 <br/>
 Iterate quickly on prototypes, incorporating feedback to refine models and outputs
 <br/>
 <br/>
 Ensure solutions consider
 <strong>
  data security, governance, and compliance
 </strong>
 within a financial services environment
 <br/>
 <br/>
 <strong>
  Required Qualifications
  <br/>
  <br/>
 </strong>
 10+ years of experience in
 <strong>
  Python development
 </strong>
 with strong hands-on coding ability
 <br/>
 <br/>
 Experience building
 <strong>
  AI/ML or LLM-based applications and prototypes
  <br/>
  <br/>
 </strong>
 Exposure to
 <strong>
  financial services, asset management, trading, or investment research
  <br/>
  <br/>
 </strong>
 Strong experience with
 <strong>
  data analysis libraries
 </strong>
 (Pandas, NumPy) and working with structured/unstructured data
 <br/>
 <br/>
 Familiarity with
 <strong>
  APIs, microservices, and cloud platforms (AWS, Azure, or GCP)
  <br/>
  <br/>
 </strong>
 Ability to work in a
 <strong>
  fast-paced, iterative environment
 </strong>
 with direct interaction with business stakeholders
 <br/>
 <br/>
 <strong>
  Preferred Qualifications
  <br/>
  <br/>
 </strong>
 Experience with
 <strong>
  LLM frameworks
 </strong>
 (LangChain, RAG architectures, vector databases)
 <br/>
 <br/>
 Knowledge of
 <strong>
  financial data sets, market data, or portfolio analytics
  <br/>
  <br/>
 </strong>
 Experience prototyping tools for
 <strong>
  quant research, credit analysis, or trading workflows
  <br/>
  <br/>
 </strong>
 Familiarity with
 <strong>
  Snowflake, Databricks, or modern data platforms
  <br/>
  <br/>
 </strong>
 <strong>
  What Success Looks Like
  <br/>
  <br/>
 </strong>
 Rapid delivery of
 <strong>
  high-impact AI prototypes
 </strong>
 that demonstrate clear business value
 <br/>
 <br/>
 Strong collaboration with front-office teams to
 <strong>
  solve real investment and research problems
  <br/>
  <br/>
 </strong>
 Ability to evolve prototypes into scalable solutions in partnership with engineering teams
</div>


Python Developer/AI Engineer

Aqovia

<div>
 <strong>
  <strong>
   Why Join Aqovia?
   <br/>
   <br/>
  </strong>
 </strong>
 We don&rsquo;t just build software, we engineer impact. Aqovia is an international technology firm and strategic incubator creating real-world value through AI, data, and bespoke software solutions. By joining us, you&rsquo;ll work within an innovative ecosystem that shapes the industries of tomorrow.
 <br/>
 <br/>
 <strong>
  <strong>
   The Role
   <br/>
   <br/>
  </strong>
 </strong>
 We are looking for a dynamic, detail-oriented
 <strong>
  UI/UX Engineer
 </strong>
 to join our team in Lahore. In this role, you will collaborate with Data Scientists, Business Analysts, and Product Owners to design and deliver exceptional user journeys.
 <br/>
 <br/>
 You will lead the interface and experience design across our AI-powered EdTech platforms, Learning Management Systems (LMS), and Moodle-based ecosystems, making complex human-AI interactions seamless, accessible, and visually stunning.
 <br/>
 <br/>
 <strong>
  <strong>
   What You Will Do
   <br/>
   <br/>
  </strong>
 </strong>
 <ul>
  <li>
   UX Research &amp; Frameworks: Conduct user research within learner and educator demographics. Create personas, map user journeys, and work with user stories to optimise digital learning workflows.
  </li>
  <li>
   Human-AI Interaction Design: Shape the UX for cutting-edge features like AI tutor chat interfaces, smart nudges, and teacher copilots.
  </li>
  <li>
   Wireframing &amp; Prototyping: Build low-fidelity wireframes and high-fidelity mockups in Figma, Sketch, or Adobe XD, and develop interactive prototypes using HTML, CSS, and JavaScript.
  </li>
  <li>
   Data Visualisation: Design engaging dashboards and visualisations based on educational data, predictive learning analytics, and student retention models.
  </li>
  <li>
   LMS Customisation: Create custom graphics, assets, and themes to enhance wireframes and virtual learning environments.
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  <strong>
   What We Are Looking For
   <br/>
   <br/>
  </strong>
 </strong>
 <strong>
  <strong>
   Essential Criteria:
   <br/>
   <br/>
  </strong>
 </strong>
 <ul>
  <li>
   Proven Experience: Solid track record as a UI Developer or UX Designer within a technology-focused company.
  </li>
  <li>
   Strong Portfolio: A portfolio showcasing relevant UI/UX work, specifically in data visualisation, dashboard design, or AI-augmented user experiences (e.g., chat workflows, predictive dashboards).
  </li>
  <li>
   Technical Skills: Proficiency in HTML5, CSS3, and JavaScript, alongside expertise in prototyping tools (Figma, Sketch, XD, Axure, Zeplin).
  </li>
  <li>
   Communication: Exceptional professional English communication skills (written and verbal) to effectively collaborate with global teams and stakeholders.
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  <strong>
   Nice-to-Haves:
   <br/>
   <br/>
  </strong>
 </strong>
 <ul>
  <li>
   Familiarity with the Moodle ecosystem, including custom theme design and learner interface optimisation.
  </li>
  <li>
   Experience designing interfaces for Generative AI workflows, NLP features, or LLM-driven educational tools (including handling AI latency smoothly and building trust feedback loops).
  </li>
  <li>
   Proficiency in Adobe Creative Suite (Illustrator, InDesign, Photoshop).
  </li>
  <li>
   Experience with LESS/SASS, Bootstrap, Material UI, or JavaScript data libraries (D3.js, Chart.js).
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  <strong>
   What We Offer
   <br/>
   <br/>
  </strong>
 </strong>
 <ul>
  <li>
   Meaningful Impact: Engineering digital education solutions that empower educators and students nationwide.
  </li>
  <li>
   Growth Opportunities: Clear pathways for long-term career advancement and professional development.
  </li>
  <li>
   Collaborative Culture: A supportive environment built on teamwork, transparent communication, and innovation.
  </li>
  <li>
   Competitive Package: A rewarding salary and benefits package to be finalised during your initial talent acquisition consultation.
   <br/>
   <br/>
  </li>
 </ul>
 <strong>
  <strong>
   Equal Opportunity Employer
   <br/>
   <br/>
  </strong>
 </strong>
 At Aqovia, we believe engineering excellence is driven by diverse viewpoints. We welcome candidates from all backgrounds and are committed to maintaining an inclusive culture where everyone feels welcome, valued, and empowered to excel.
 <br/>
 <br/>
 <strong>
  Ready to design the future of AI-driven education? Apply today.
 </strong>
</div>


Product Designer / UX Engineer (AI & EdTech)

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