ai research engineer

NXP Semiconductors

<div>Senior Quantization Engineer - Edge AI Model Optimization 
 
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!
Job Summary
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.
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.
If you want to the future of efficient on‑device AI, this is the place to be.
Job Responsibilities
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.
 
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.
 
Production Implementation: Translate research prototypes into robust, optimized production code (C++/Python), ensuring strict memory and compute efficiency standards.
 
Systems Integration: Document algorithmic tradeoffs, derive deployment recipes, and mentor the engineering team on numerical methods and optimization.
 
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.
 
IP Generation: Contribute to NXP&rsquo;s intellectual property portfolio through patents and technical publications.
Job Qualifications
Required Background
Education: MSc or Ph.D (is a plus) in Computer Science, Electrical Engineering, or Mathematics with a focus on Machine Learning or Deep Learning.
 
AI Expertise: Proven practical experience in AI/ML with a deep understanding of CNN architectures and Generative AI (Transformers, LLMs, VLMs, etc.).
 
Technical Stack: Strong hands-on experience with PyTorch, ONNX, and model conversion/optimization pipelines.
 
Software Engineering: Proficient in Python and C++ and best development practices.
 
Embedded Mindset: Familiarity with the constraints of embedded systems (latency, power, memory bandwidth) and how code interacts with underlying hardware.
Preferred
Advanced AI: Experience with state-of-the-art quantization techniques for discriminative and generative AI (e.g., GPTQ, SpinQuant, etc).
 
Hardware Acceleration: Experience with NPUs, device-level profiling, and diagnosing memory bottlenecks.
 
Kernel Development: Experience with custom kernel development is a plus.
 
Compilers: Knowledge of MLIR or TVM is a significant plus. 
 
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!
Job Summary
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.
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.
If you want to the future of efficient on‑device AI, this is the place to be.
Job Responsibilities
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.
 
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.
 
Production Implementation: Translate research prototypes into robust, optimized production code (C++/Python), ensuring strict memory and compute efficiency standards.
 
Systems Integration: Document algorithmic tradeoffs, derive deployment recipes, and mentor the engineering team on numerical methods and optimization.
 
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.
 
IP Generation: Contribute to NXP&rsquo;s intellectual property portfolio through patents and technical publications.
Job Qualifications
Required Background
Education: MSc or Ph.D (is a plus) in Computer Science, Electrical Engineering, or Mathematics with a focus on Machine Learning or Deep Learning.
 
AI Expertise: Proven practical experience in AI/ML with a deep understanding of CNN architectures and Generative AI (Transformers, LLMs, VLMs, etc.).
 
Technical Stack: Strong hands-on experience with PyTorch, ONNX, and model conversion/optimization pipelines.
 
Software Engineering: Proficient in Python and C++ and best development practices.
 
Embedded Mindset: Familiarity with the constraints of embedded systems (latency, power, memory bandwidth) and how code interacts with underlying hardware.
Preferred
Advanced AI: Experience with state-of-the-art quantization techniques for discriminative and generative AI (e.g., GPTQ, SpinQuant, etc).
 
Hardware Acceleration: Experience with NPUs, device-level profiling, and diagnosing memory bottlenecks.
 
Kernel Development: Experience with custom kernel development is a plus.
 
Compilers: Knowledge of MLIR or TVM is a significant plus. 
 
More information about NXP in India...
#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.
 
 
 This is resourced to do real post-training at scale - committed investment in GPU compute and training infrastructure, not toy fine-tunes.
 
 
 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.
 
 
 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.
 
 
 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.
 
 
 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.
 
 
 Work you'll do
 
 
 Post-training &amp; alignment
 
 
 <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.
 
 
 </li>
 </ul>
 Reward modeling &amp; data
 
 
 <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.
 
 
 </li>
 </ul>
 Efficient fine-tuning, training &amp; inference infrastructure
 
 
 <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.
 
 
 </li>
 </ul>
 Small language models &amp; open-weight models
 
 
 <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>
 Evaluation, safety &amp; red teaming
 
 
 <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.
 
 
 </li>
 </ul>
 The team
 
 
 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.
 
 
 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.
 
 
 
 Required Qualifications
 
 
 
 <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.
 
 
 </li>
 </ul>
 
 Preferred Qualifications
 
 
 
 <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.
 
 
 </li>
 </ul>
 
 Compensation
 
 
 
 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. 
 
This is resourced to do real post-training at scale - committed investment in GPU compute and training infrastructure, not toy fine-tunes. 
 
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. 
 
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. 
 
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. 
 
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. 
 
Work you'll do 
 
Post-training &amp; alignment 
 
<ul><li>Design and execute post-training pipelines: supervised fine-tuning (SFT), preference optimization, and reinforcement learning / alignment workflows.</li></ul> 
<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> 
<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> 
Reward modeling &amp; data 
 
<ul><li>Develop reward models and preference datasets to improve reasoning quality, factuality, safety, policy adherence, and task performance.</li></ul> 
<ul><li>Curate, clean, synthesize, and evaluate large-scale instruction, preference, and domain-specific datasets, with rigorous filtering, deduplication, and quality control.</li></ul> 
<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> 
Efficient fine-tuning, training &amp; inference infrastructure 
 
<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> 
<ul><li>Optimize inference performance - latency, throughput, quantization, and deployment efficiency - for production, including frameworks such as vLLM, TensorRT-LLM, or TGI.</li></ul> 
Small language models &amp; open-weight models 
 
<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> 
Evaluation, safety &amp; red teaming 
 
<ul><li>Design evaluation frameworks covering reasoning, hallucination detection, factuality, instruction following, structured outputs, and domain-specific metrics.</li></ul> 
<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> 
<ul><li>Apply PHI/HIPAA-aware data handling and produce model documentation suitable for regulated clinical use.</li></ul> 
<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> 
The team 
 
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. 
 
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. 
 
Required qualifications 
 
<ul><li>Bachelor's degree in Computer Science, Machine Learning, Artificial Intelligence, Applied Mathematics, Computational Linguistics, or a related field.</li></ul> 
<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> 
<ul><li>Hands-on experience with reasoning-model training and/or verifiable-reward (RLVR) workflows.</li></ul> 
<ul><li>Strong understanding of modern post-training techniques: SFT, RLHF, PPO, DPO, GRPO, RLAIF, and preference optimization workflows.</li></ul> 
<ul><li>Experience with open-weight foundation models such as Llama, Qwen, Mistral, DeepSeek, or equivalent architectures.</li></ul> 
<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> 
<ul><li>Experience implementing efficient fine-tuning techniques such as LoRA, QLoRA, PEFT, and quantization-aware workflows.</li></ul> 
<ul><li>Deep understanding of transformer architectures, tokenization, attention mechanisms, decoding strategies, and model scaling trade-offs.</li></ul> 
<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> 
<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> 
<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> 
<ul><li>Limited immigration sponsorship may be available.</li></ul> 
Preferred qualifications 
 
<ul><li>Experience building or optimizing reasoning models, agentic models, or tool-using LLM systems.</li></ul> 
<ul><li>Familiarity with inference optimization frameworks such as vLLM, TensorRT-LLM, TGI, or Ollama.</li></ul> 
<ul><li>Experience with multimodal models, speech models, or domain-specific foundation models; experience using large-scale GPU clusters and distributed compute.</li></ul> 
<ul><li>Contributions to open-source AI projects, research publications, benchmark development, or model releases.</li></ul> 
<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> 
Compensation 
 
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>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.
Learn more about our work with Microsoft: Valence Brings AI Coaching More Deeply Into the Flow of Work with Microsoft 365
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.
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.<div></div> 
The Role
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.
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.
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.<div></div> 
About Valence
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.<div></div> 
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.<div></div> 
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.
What You'll Do<ul><li>
Build the paved roads. 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.</li><li>
Make change safe by default. 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.</li><li>
Enforce security at the platform layer. Ship secure defaults, policy enforcement frameworks, auditability, and data-access guard libraries so the safe path is the easy path.</li><li>
Run global delivery controls. Implement single-domain strategy, region pinning and routing, residency-aware behavior, and cross-region config consistency checks for enterprise customers worldwide.</li><li>
Raise the operational bar. Establish SLOs, synthetic checks, alerting standards, and incident runbooks and drills that drive faster detection and recovery.</li><li>
Build the internal developer platform. Create service templates, blessed internal-tool paths, and standard deploy, monitoring, and auth patterns that make the right thing the default thing.</li><li>
Operate an AI-native engineering plane. Build issue-to-PR automation infrastructure with governance, human-in-control workflows, and audit logs that convert signals into auditable, human-approved execution outcomes.</li></ul>
What We're Looking For<ul><li>
Technical foundation: 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.</li><li>
Platform mindset: You build reusable primitives, not one-off features. You think in terms of paved roads, secure defaults, and standards that scale across many teams.</li><li>
Reliability and change-safety expertise: Hands-on experience with CI/CD, progressive delivery, rollback and migration safety, observability, SLOs, and incident response.</li><li>
Cloud &amp; deployment experience: Comfortable developing and operating services in cloud environments (AWS, GCP, Azure) and working with containerization and orchestration (Docker, Kubernetes).</li><li>
Security &amp; compliance awareness: Familiarity with implementing platform-level guardrails (authorization, auditability, data-access controls, and policy enforcement) in a privacy- and residency-conscious way.</li><li>
Engineering excellence: Strong software engineering skills, including writing maintainable code, debugging distributed systems, and collaborating in cross-functional teams.</li><li>
Growth orientation: Eagerness to tackle unfamiliar problems, learn new technologies, and shape our platform and culture.</li><li>
Communication: Ability to explain technical ideas clearly and work effectively with both technical and non-technical stakeholders.</li></ul>
What You'll Get<ul><li>
Ownership &amp; Rapid Growth</li><li>
Outsized missions from day one, with direct responsibility for company-defining projects</li><li>
Work alongside the executive team with transparency into strategy and decision-making</li><li>
Influence on direction through real-time customer feedback and market insights</li></ul><div></div><ul><li> 
AI-First Operator</li><li>
Work directly with cutting-edge AI models and next-generation platforms</li><li>
Build expertise in enterprise AI implementation across Fortune 500 companies and multiple industries</li><li>
Establish yourself as a recognized leader among peers in shaping how AI transforms work at a global scale</li></ul><div></div><ul><li> 
Compensation</li><li>
Competitive salary including base + bonuses</li><li>
Comprehensive health coverage (medical, dental, vision) from day one</li><li>
Generous PTO, company-wide R&amp;R shutdowns, and paid parental leave</li><li>
Retirement plan support for US and global employees</li></ul><div></div><ul><li> 
Equity</li><li>
Meaningful ownership in a venture-backed company at a growth inflection point</li><li>
Financial upside that comes from scaling fast</li><li>
Top-up grants as we scale and you deliver exceptional performance &mdash; your compensation grows alongside your impact</li></ul><div></div><ul><li> 
Top-Performing Culture</li><li>
A culture built for top talent: intensity to win, growth without limits, and a team that solves hard problems and celebrates big wins together</li></ul><div></div> 
Learn more about us and meet our team here
Location and Work Environment
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.<div></div> 
Diversity and Inclusion
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.<div></div> 
Employment Verification &amp; Commitment
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.
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 Candidate Privacy Notice.<div></div> 
#LI-HYBRID</div>

Staff Software Engineer, Platform

Inherent Technologies

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

Python Developer/AI Engineer

Aqovia

<div>
 
 
 Why Join Aqovia?
 
 
 
 
 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.
 
 
 
 
 The Role
 
 
 
 
 We are looking for a dynamic, detail-oriented
 
 UI/UX Engineer
 
 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.
 
 
 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.
 
 
 
 
 What You Will Do
 
 
 
 
 <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.
 
 
 </li>
 </ul>
 
 
 What We Are Looking For
 
 
 
 
 
 
 Essential Criteria:
 
 
 
 
 <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.
 
 
 </li>
 </ul>
 
 
 Nice-to-Haves:
 
 
 
 
 <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).
 
 
 </li>
 </ul>
 
 
 What We Offer
 
 
 
 
 <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.
 
 
 </li>
 </ul>
 
 
 Equal Opportunity Employer
 
 
 
 
 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.
 
 
 
 Ready to design the future of AI-driven education? Apply today.
 
</div>

Product Designer / UX Engineer (AI & EdTech)

LCG Inc.

<div>Location: Rockville, MD (Onsite, 3 days per week) - No exceptions

Required Clearance: Ability to obtain Public Trust

Sponsorship: No sponsorship assistance is available for this position now or in the future.

Position Description:

LCG is seeking an AI Engineer to support our client at NIH in advancing AI-driven modernization initiatives across software development, research enablement, and enterprise operations.

This role will focus on implementing AI-assisted development capabilities, enabling codebase intelligence, accelerating modernization of legacy applications, and integrating AI into enterprise workflows. The AI Engineer will play a key role in leveraging tools such as GitHub Copilot, large language models (LLMs), and emerging AI frameworks to improve developer productivity, reduce technical debt, and enhance system understanding across a complex application portfolio.

The position will collaborate with development teams, DevSecOps engineers, and business stakeholders to identify, implement, and scale AI use cases that align with Client's modernization roadmap.

Key Responsibilities

AI-Assisted Development &amp; Engineering
<ul>
<li>Implement and operationalize AI-assisted development tools (e.g., GitHub Copilot, LLM-based assistants) across engineering teams.
</li><li>Enable full codebase understanding by indexing repositories and supporting natural language queries across legacy systems.
</li><li>Support AI-driven code refactoring, modernization, and technical debt reduction efforts across Java and .NET applications.
</li><li>Assist in legacy-to-modern transformation initiatives, including translating legacy code into modern APIs and cloud-native architectures.
</li></ul>AI Use Case Development &amp; Integration
<ul>
<li>Design and implement AI-driven solutions such as:
</li><li><ul><li>Code analysis and summarization tools
</li><li>Requirements extraction from legacy systems
</li><li>Test generation and code quality automation
</li><li>Knowledge bots leveraging enterprise data sources (ServiceNow, SharePoint, etc.)
</li></ul></li><li>Integrate AI capabilities with enterprise platforms using APIs and emerging standards (e.g., Model Context Protocol &ndash; MCP).
</li><li>Support development of AI-powered assistants for operational and research workflows.
</li></ul>AI Enablement &amp; Developer Productivity
<ul>
<li>Drive adoption of AI tools to reduce time spent on debugging, documentation, onboarding, and code reviews.
</li><li>Partner with development teams to identify bottlenecks and apply AI solutions to improve efficiency.
</li><li>Establish best practices for AI usage, including prompt engineering, validation, and responsible AI usage.
</li></ul>Data, Models, and Platform Support
<ul>
<li>Support AI/ML workloads leveraging cloud services (e.g., AWS SageMaker).
</li><li>Assist in enabling AI-driven analytics, reporting, and research workflows.
</li><li>Ensure secure handling of sensitive data in compliance with federal and NIH guidelines.
</li></ul>Training, Adoption, and Governance
<ul>
<li>Support AI literacy initiatives and role-based training for developers and business users.
</li><li>Define governance practices for AI usage, including data handling, validation, and compliance.
</li><li>Collaborate with stakeholders to identify and prioritize AI use cases across the portfolio.</li>
</ul>
Qualifications

Education

Bachelor&rsquo;s degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or related field.

Required Experience
<ul>
<li>5+ years of experience in software engineering, data engineering, or related technical roles.
</li><li>Hands-on experience with AI/ML or Generative AI tools (e.g., OpenAI, Copilot, Claude, etc.).
</li><li>Strong programming experience in languages such as Python, Java, or .NET.
</li><li>Experience working with APIs and integrating third-party platforms.
</li><li>Familiarity with modern software development practices and SDLC.
</li></ul>Preferred Experience
<ul>
<li>Experience with AI-assisted development tools (GitHub Copilot or similar).
</li><li>Experience working with cloud-based AI/ML platforms (AWS SageMaker preferred).
</li><li>Exposure to enterprise system integrations (ServiceNow, SharePoint, Power BI, etc.).
</li><li>Experience working in federal or healthcare research environments.
</li><li>Understanding of data security, compliance, and FedRAMP environments.</li>
</ul>
Compensation and Benefits

The projected compensation range for this position is $100,000 to $130,000 per year benchmarked in the Washington, D.C. metropolitan area. The salary range provided is a good faith estimate representative of all experience levels. Salary at LCG is determined by various factors, including but not limited to role, location, the combination of education/training, knowledge, skills, competencies, certifications, and work experience. 
 
LCG offers a competitive, comprehensive benefits package which includes health insurance options (medical, dental, vision), life and disability insurance, retirement plan contributions, as well as paid leave, federal holidays, professional development, and lifestyle benefits.

Devoted to Fair and Inclusive Practices
All qualified applicants will receive consideration for employment without regard to sex, race, ethnicity, age, national origin, citizenship, religion, physical or mental disability, medical condition, genetic information, pregnancy, family structure, marital status, ancestry, domestic partner status, sexual orientation, gender identity or expression, veteran or military status, or any other basis prohibited by law.

If you are interested in applying for employment with LCG and need special assistance or an accommodation to apply for a posted position, contact our Human Resources department by email at hr@lcginc.com.

Securing Your Data

Beware of fraudulent job offers using LCG's name. LCG will never request payment-related details or advancement of money during the application process. Legitimate communication will only come from lcginc.com or system@hirebridgemail.com emails, not free commercial services like Gmail or WhatsApp. If you receive suspicious emails asking for payment or personal information, contact us immediately at hr@lcginc.com.

If you believe you are the victim of a scam, contact your local law enforcement and report the incident to the U.S. Federal Trade Commission.</div>

AI Engineer, Senior (GenAI / AI Modernization)

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