research engineer

Syngenta

<div>Company Description 
About Syngenta
At Syngenta Seeds Field Crops, we're shaping the future of agriculture and empowering farmers to meet the ever-growing demand for food and fuel. We&rsquo;re a global Ag Tech powerhouse, headquartered in the United States, with passionate, local experts collaborating with farmers to deliver solutions that create market opportunities. We unite precision breeding, advanced biotechnology trait choice, and digital platforms for unmatched in-field performance. Our seeds help mitigate risks such as disease, insect, weed, and extreme weather pressures, all while promoting sustainable farming practices that protect and enhance our planet. Join our mission of revolutionizing food security and transforming agriculture. 
Job Description 
At Syngenta, our goal is to build the most collaborative and trustworthy team in agriculture, providing top-quality seeds and innovative crop protection solutions that improve farmers' success. To support this mission, Syngenta&rsquo;s Analytics &amp; Data Sciences Team is seeking an Operations Research Engineer in Malta, IL. This role will drive operational efficiency and decision quality across Seeds Research &amp; Development by designing, building, and scaling algorithmic decision-support solutions.
The role applies Operations Research and Mathematical Optimization to complex end-to-end business processes&mdash;translating business objectives and constraints into robust models, integrating them into Syngenta&rsquo;s digital ecosystem, and enabling measurable improvements in service, cost, speed, and resource utilization.
Accountabilities:<ul><li>
Design, build, and deploy mathematical optimization models using Linear Programming (LP), Mixed-Integer Programming (MIP), Nonlinear Programming (NLP), Constraint Programming (CP), and hybrid approaches as appropriate.</li><li>
Translate business objectives, constraints, and operating policies into robust mathematical formulations with clear assumptions and acceptance criteria.</li><li>
Integrate optimization solutions into Syngenta&rsquo;s digital ecosystem.</li><li>
Identify, collect, cleanse, and structure data from multiple sources; define input/output specifications, data quality requirements, and data lineage for optimization models.</li><li>
Define and align business KPIs and metrics that drive optimization objectives and enable value tracking.</li><li>
Validate models through testing, back-testing, and scenario analysis; run simulations and what-if analyses to assess robustness and quantify trade-offs.</li><li>
Lead and manage projects delivering optimization solutions for strategic decisions and day-to-day operations, such as scope, timeline, risks, and stakeholder alignment.</li><li>
Collaborate with cross-functional teams and line-of-business users to review current operational systems and processes, identify pain points, and prioritize optimization use cases.</li><li>
Communicate clearly on bottlenecks, insights, and recommendations to enable informed decisions by management and operational teams.</li><li>
Work in an agile way and adhere to engineering best practices.</li><li>
Contribute to capability building by developing technical training materials and curricula to support adoption and awareness of Operations Research solutions.</li><li>
PhD in Operations Research, Applied Mathematics, Industrial Engineering, Data Science, Management Science, Computer Science, or a related field.</li><li>
5+ years of applied experience designing, building, and deploying Operations Research / Mathematical Optimization solutions in an industrial context.</li><li>
Strong understanding of Operations Research techniques with emphasis on mathematical programming.</li><li>
Proficiency in Python for optimization, data processing, and automation; ability to write production-quality, maintainable code.</li><li>
Hands-on experience with optimization solvers and modeling tools (e.g., Gurobi, SCIP, FICO Xpress, COPT) and building end-to-end optimization applications.</li><li>
Strong problem-framing and structured problem-solving skills: ability to identify complex business problems, assess fit for OR techniques, and deliver scalable decision-support solutions.</li><li>
Proven track record delivering implemented business process improvement solutions with measurable impact.</li><li>
Ability to identify, access, and translate relevant data into actionable, evidence-based decisions; ensure data quality, consistency, and traceability across sources.</li></ul> 

Qualifications 
<ul><li>
Experience in Manufacturing, Supply Chain, or Logistics environments.</li><li>
Knowledge of combinatorial optimization techniques.</li><li>
Familiarity with modern development practices and tooling (Git/GitHub, code reviews, testing).</li><li>
Experience using AI-assisted coding tools (e.g., GitHub Copilot).</li><li>
Experience building lightweight decision-support interfaces and visualizations (e.g., Streamlit or similar) to support user adoption.</li><li>
Ability to operate effectively in a matrix organization by building strong partnerships and influencing stakeholders at multiple levels without direct authority.</li><li>
Ability to generate strategic insights from complex analyses and communicate them clearly to drive measurable improvements in operational efficiency and effectiveness.</li><li>
Strong analytical mindset with the ability to communicate complex concepts clearly to technical and non-technical audiences.</li></ul> 

Additional Information 
Salary for this position ranges between $106,000 - $145000 annually.
What We Offer:<ul><li>
A culture that celebrates belonging and collaboration, promotes professional development and strives for a work-life balance that supports the team members. Offers flexible work options to support your work and personal needs.</li><li>
Full Benefit Package (Medical, Dental &amp; Vision) that starts your first day.</li><li>
401k plan with company match, Profit Sharing &amp; Retirement Savings Contribution.</li><li>
Paid Vacation, Paid Holidays, Maternity and Paternity Leave, Education Assistance, Wellness Programs, Corporate Discounts, among other benefits.</li></ul>
Syngenta has been ranked as a top employer by Science Journal. Learn more about our team and our mission here: https://www.youtube.com/watch?v=OVCN_51GbNI
Syngenta is an Equal Opportunity Employer and does not discriminate in recruitment, hiring, training, promotion or any other employment practices for reasons of race, color, religion, gender, national origin, age, sexual orientation, marital or veteran status, disability, or any other legally protected status.
WL: 5B
#LI-REMOTE</div>

Operation research engineer

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>

Analytical Labs Karachi (Public Sector)

Job Title: Laboratory Engineer (Materials Testing &amp; Research)Location: KarachiEmployment Type: Full-TimeAbout the PositionWe are seeking a motivated and detail-oriented Laboratory Engineer to support materials characterization, testing, and research activities. The ideal candidate should possess hands-on experience with metallurgical and corrosion testing equipment, strong analytical skills, and an aptitude for technical report writing and research documentation.Key Responsibilities<ul><li>Operate and maintain laboratory equipment including:</li><li>Metallurgical Microscope</li><li>Potentiostat/Galvanostat System</li><li>Hardness Testers (Micro/Macro Hardness)</li><li>Sample Preparation Equipment</li><li>Conduct metallurgical examinations and microstructural analysis.</li><li>Perform corrosion testing and electrochemical evaluations.</li><li>Carry out hardness testing and materials characterization activities.</li><li>Analyze test results and prepare technical reports.</li><li>Assist in research projects related to materials engineering, corrosion, failure analysis, and quality assurance.</li><li>Review scientific literature and contribute to research papers, technical publications, and project proposals.</li><li>Ensure compliance with laboratory safety procedures and quality standards.</li><li>Maintain testing records, calibration logs, and laboratory documentation.</li></ul>Qualifications &amp; Experience<ul><li>Bachelor's Degree in Materials Engineering, Metallurgical Engineering, Mechanical Engineering, Chemistry, Physics, or a related discipline.</li><li>Atleast 1 years of relevant laboratory experience preferred.</li><li>Hands-on experience with metallurgical microscopes, potentiostats, hardness testers, and related testing equipment.</li><li>Knowledge of international testing standards (ASTM, ISO, etc.) will be an advantage.</li><li>Proficiency in MS Office and technical report preparation.</li></ul>Desired Skills<ul><li>Strong analytical and problem-solving abilities.</li><li>Excellent technical writing and documentation skills.</li><li>Ability to conduct literature reviews and support research publications.</li><li>Attention to detail and commitment to quality.</li><li>Ability to work independently as well as within multidisciplinary teams.</li></ul>What We Offer<ul><li>Opportunity to work on advanced materials and corrosion research projects and to attain working experience for further career progression.</li><li>Exposure to modern testing and characterization equipment.</li><li>Professional growth and research publication opportunities.</li><li>Competitive salary and benefits package, opportunity leading to permanent government job</li></ul>Interested candidates may submit their CV along with a brief cover letter highlighting relevant laboratory and research experience to: szaidi1983@gmail.comApplication Deadline: 3 Jun 2026Pay: From Rs55,000.00 per monthWork Location: In person

Lab Engineer

Technology Innovation Institute

<div>Senior Electrical Engineer - Quatum Research Center
 
 
We are a young, dynamic team developing the next generation of quantum sensing devices. Our mission is to deliver market-ready quantum sensors with significant impact on both international and emerging local high-tech ecosystems. As part of the Technology Innovation Institute&rsquo;s Quantum Research Center, we are well-resourced and equipped with the necessary infrastructure. We offer globally and locally competitive salaries, along with relocation assistance and social and family support. 
KEY ACTIVITIES
<ul><li>Design and prototype electrical subsystems/components for advanced sensing systems in full-cycle hands-on R&amp;D projects.</li>
<li>Collaborate closely with researchers and engineers to translate scientific and technical requirements into electronic design specifications.</li>
<li>Supervise outsourced development processes.</li>
<li>Participate in laboratory and field tests.</li>
<li>Prepare high-quality technical documentation.</li>
</ul>Skills Requierd:
<ul><li>Independent and creative problem-solving abilities.</li>
<li>System-level thinking and the ability to analyze subsystems/components.</li>
<li>Experience with prototyping, integration, and testing/validation processes.</li>
<li>Proficiency in schematic/layout PCB design software.</li>
<li>Expertise in analog signal acquisition, processing, and control.</li>
<li>Experience in digital signal acquisition, processing, and control.</li>
 <li>Proficiency in precision and low-noise signal circuit design.</li>
<li>Knowledge of EMC and EMI guidelines and mitigation techniques.</li>
<li>Experience with high-speed digital signals, RF, and FPGA programming experience is a plus.</li></ul> 
Education
<ul><li>Bachelor/Master or Ph.D. degree in Electrical Engineering.</li>
 <li>4+ years of relevant experience, preferably in sensing-related R&amp;D.v</li></ul></div>

Senior Electrical 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

Prolific

<div>
 
 AI Trainer &ndash; Machine Learning Specialists
 
 
 
 
 About Prolific
 
 
 
 Prolific is not just another player in the AI space &ndash; we are building the biggest pool of quality human data in the world.
 
 
 Over 35,000 AI developers, researchers, and organizations use Prolific to gather data from paid study participants with a wide variety of experiences, knowledge, and skills.
 
 
 
 The role
 
 
 
 We&rsquo;re looking for
 
 AI Trainer &ndash; Machine Learning Specialists
 
 to help train and evaluate cutting-edge AI models using real ML expertise. If you have the necessary experience, we&rsquo;ll send you a quick
 
 10- to 15-minute test
 
 to assess your skills and suitability for AI tasks. If successful, you&rsquo;ll be invited to join Prolific as a participant, where you&rsquo;ll get paid to train and evaluate powerful AI models.
 
 
 Researchers looking for your skills tend to pay up to
 
 $150/hr per AI task completed
 
 . You must be prepared to complete paid tasks that require
 
 one hour of uninterrupted work
 
 , though many are shorter.
 
 
 What you&rsquo;ll bring
 
 
 <ul>
 <li>
 AI Training task skills and verifiable professional experience as a Machine Learning Specialist (e.g., ML engineer, data scientist, applied scientist, research engineer)
 </li>
 <li>
 A willingness to take our skills verification test to assess your suitability for our participant pool
 </li>
 <li>
 Strong attention to detail and the ability to concentrate on complex tasks for up to one hour at a time
 </li>
 <li>
 A reliable and fast internet connection and access to a computer
 </li>
 <li>
 A willingness to self-declare your earnings, as our participants are self-employed
 </li>
 <li>
 A Paypal account to receive payment from our clients
 
 
 </li>
 </ul>
 
 What you&rsquo;ll be doing in the role
 
 
 
 <ul>
 <li>
 Completing AI training tasks such as analyzing, editing, and writing annotations (including technical reasoning and structured evaluation)
 </li>
 <li>
 Judging the performance of AI in performing ML-relevant tasks (e.g., model/experiment critique, data leakage detection, metrics interpretation, debugging approach, methodology review)
 </li>
 <li>
 Improving cutting-edge AI models by providing expert feedback on correctness, robustness, clarity, and technical depth
 
 
 </li>
 </ul>
 
 Key Technologies
 
 
 
 <ul>
 <li>
 General AI Training
 </li>
 <li>
 Model Evaluation and Evals
 </li>
 <li>
 Trust and Safety
 </li>
 <li>
 Red Teaming
 </li>
 <li>
 Quality Analytics
 </li>
 <li>
 Data/ML Concepts (e.g., supervised learning, deep learning, NLP, CV, statistics, experimentation)
 
 
 </li>
 </ul>
 
 Why Prolific is a great platform to join as a Participant
 
 
 
 Joining our platform as a Prolific participant will give you the chance to influence the AI models of the future using professional machine learning expertise. Once you pass our assessment, you can join Prolific in just 15 minutes, and start enjoying competitive pay rates, flexible hours, and the ability to work from home.
 
 
 We've built a unique platform that connects researchers and companies with a global pool of participants, enabling the collection of high-quality, ethically sourced human behavioural data and feedback. This data is the cornerstone of developing more accurate, nuanced, and aligned AI systems.
 
 
 We believe that the next leap in AI capabilities won't come solely from scaling existing models, but from integrating diverse human perspectives and behaviours into AI development. By providing this crucial human data infrastructure, Prolific is positioning itself at the forefront of the next wave of AI innovation &ndash; one that reflects the breath and the best of humanity.
 
 
 
 Links to more information on Prolific
 
 
 
 Website
 
 
 Youtube
 
 
 Privacy Statement
 
 
 By submitting your application, you agree that Prolific may collect your personal data for recruiting and global organisation planning. Prolific's Candidate Privacy Notice explains what personal information Prolific may process, where Prolific may process your personal information, its purposes for processing your personal information, and the rights you can exercise over Prolific use of your personal personal information.
</div>

AI Training - Machine Learning Specialist (EST)

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