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Phd Optimization Research Jobs in Seattle, WA (NOW HIRING)

Chewy

Research Scientist III

Bellevue, WA

$148K - $225K/yr

S., PhD (preferred), or equivalent experience) in Data Science, Operations Research, Applied Mathematics, or related fields. * Strong background in Optimization and machine learning with practical ...

Chewy

Research Scientist III

Bellevue, WA · On-site

$148K - $225K/yr

S., PhD (preferred), or equivalent experience) in Data Science, Operations Research, Applied Mathematics, or related fields. * Strong background in Optimization and machine learning with practical ...

Trunk Tools

Research Intern

Redmond, WA · On-site +1

Currently pursuing a PhD in Computer Science, Artificial Intelligence, Computational Biology ... optimization problems. * Multimodal Experience: Proven ability to work with unstructured data ...

Centific

Research Intern

Redmond, WA · On-site

Currently pursuing a PhD in Computer Science, Artificial Intelligence, Computational Biology ... optimization problems. * Multimodal Experience: Proven ability to work with unstructured data ...

AMD

Fellow, AI Workload Optimization

Bellevue, WA · On-site

... PhD or Master's degree in Computer Science, Electrical Engineering, or a related field, or ... research or applied experience in AI/ML, including areas such as deep learning, model training ...

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Phd Optimization Research information

See Seattle, WA salary details

$33.6K

$108.5K

$176.4K

How much do phd optimization research jobs pay per year?

As of Aug 25, 2026, the average yearly pay for phd optimization research in Seattle, WA is $108,471.00, according to ZipRecruiter salary data. Most workers in this role earn between $89,900.00 and $124,000.00 per year, depending on experience, location, and employer.

What is a PhD in optimization research?

A PhD in Optimization Research is an advanced academic degree focused on developing and analyzing mathematical models and algorithms to find the best possible solutions to complex problems. This field often involves linear and nonlinear programming, combinatorial optimization, and stochastic processes, and is applied in areas such as operations research, machine learning, logistics, and engineering. Graduates are prepared for careers in academia, industry, or research institutions, where they work on improving decision-making processes and resource allocation. The program typically involves coursework, comprehensive exams, and original research leading to a dissertation.

What are the typical collaborative projects that a PhD optimization researcher might work on within a multidisciplinary team?

PhD Optimization Researchers often collaborate on projects that integrate expertise from fields such as data science, engineering, computer science, and business analytics. These projects may involve developing and implementing advanced optimization algorithms to solve complex, real-world problems like supply chain management, resource allocation, or energy systems modeling. Team members typically contribute domain knowledge, data, and problem requirements, while the optimization researcher focuses on model formulation, algorithm selection, and solution analysis. Effective communication and adaptability are essential, as researchers must translate technical findings into actionable insights for stakeholders.

What are the key skills and qualifications needed to thrive as a PhD optimization researcher, and why are they important?

To excel as a PhD Optimization Researcher, you typically need a doctorate in applied mathematics, computer science, operations research, or a related field, along with expertise in mathematical modeling and algorithm development. Proficiency with programming languages such as Python, MATLAB, or C++, and familiarity with optimization libraries and tools like Gurobi or CPLEX are commonly required. Strong analytical thinking, creativity, and effective communication skills help in formulating novel solutions and collaborating with interdisciplinary teams. These competencies are crucial for advancing research, solving complex optimization problems, and effectively disseminating findings within both academic and industry settings.

What is the difference between Phd Optimization Research vs Data Scientist?

AspectPhd Optimization ResearchData Scientist
Required CredentialsPhD in Operations Research, Applied Mathematics, or related fieldBachelor's or Master's in Data Science, Computer Science, or related field; some roles prefer PhD
Work EnvironmentResearch labs, academia, R&D departments in industryTech companies, finance, healthcare, consulting firms
Industry UsageFocus on developing optimization algorithms, mathematical modelingFocus on data analysis, machine learning, predictive modeling
Common Search/ComparisonYesYes

While both roles involve advanced analytical skills, Phd Optimization Research primarily focuses on developing and refining optimization algorithms and mathematical models, often in research or academic settings. Data Scientists analyze large datasets to extract insights and build predictive models, often applying machine learning techniques. The roles overlap in data analysis and quantitative skills but differ in their core focus and typical work environments.

What are popular job titles related to Phd Optimization Research jobs in Seattle, WA?

For Phd Optimization Research jobs in Seattle, WA, the most frequently searched job titles are:

What job categories do people searching Phd Optimization Research jobs in Seattle, WA look for?

The top searched job categories for Phd Optimization Research jobs in Seattle, WA are:

Phd Optimization Research jobs near you

Member of Technical Staff -- RL Research (New PhD Grad)

Seattle, WA • On-site

Nuance Labs
See more jobs at Nuance Labsexternal

Full-time

Re-posted 15 days ago

Job description

Job Summary:
Nuance Labs is a pioneering company focused on building photorealistic, real-time AI avatars with emotional intelligence. They are seeking a deeply technical Member of Technical Staff to lead reinforcement learning and post-training for large-scale omni models, requiring a PhD graduate who can develop and scale their RL/post-training stack.
Responsibilities:
• Build Nuance’s RL/post-training stack from 0→1: rollout generation, policy optimization, reward/reference model serving, data feedback loops, evaluation, checkpointing, observability, and debugging.
• Develop and scale post-training methods such as PPO, GRPO, DPO, rejection sampling, RLHF/RLAIF, online RL, and model-based data improvement.
• Design the systems abstractions that connect research ideas to production-scale RL runs: trainers, rollout workers, reward models, evaluators, data queues, experience buffers, and checkpoint promotion.
• Build evaluation and feedback loops for omni behavior: turn-taking, interruption, timing, emotional response, audiovisual coherence, instruction following, and real-time interaction quality.
• Optimize the end-to-end post-training loop across rollout throughput, serving latency, GPU utilization, policy update efficiency, queueing, checkpoint overhead, and research iteration speed.
• Evolve the platform as algorithms, model architectures, reward definitions, data sources, and evaluation methods change.
Qualifications:
Required:
• A PhD — completed, or in its final stretch — in ML, RL, or a related field, with research depth shown through publications, a strong lab/advisor, or substantial open-source work.
• Solid understanding of RL/post-training methods: policy optimization, reward modeling, preference optimization, rejection sampling, KL control, evaluation, and data feedback loops.
• Ability to reason about model behavior and training dynamics: reward hacking, unstable rewards, distribution shift, stale policies, mode collapse, over-optimization, noisy preferences, and evaluation mismatch.
• Exposure to RL/post-training pipelines through research, internships, or open-source — with frameworks such as verl, ms-swift, OpenRLHF, or equivalent, and familiarity with rollout serving systems such as vLLM. You don’t need to have run these at production scale yet; you need to learn fast and go deep.
• Strong software engineering fundamentals and the appetite to build real systems, not just prototypes.
• Curiosity and adaptability toward new RL algorithms, model architectures, serving systems, evaluation methods, and research ideas.
Preferred:
• Hands-on experience with omni or multimodal post-training for audio-video-language models, especially long-context or real-time interactive systems.
• Experience with PPO, GRPO, DPO, online RL, RLHF/RLAIF, reward modeling, preference data, synthetic data generation, or model-based data improvement.
• Prior 0→1 experience building post-training systems, RL pipelines, agent training systems, evaluation platforms, or model improvement loops.
• Experience with adjacent areas such as distributed pretraining, data infrastructure, inference serving, simulation, human/AI feedback collection, or evaluation infrastructure.
• Publications or substantial open-source contributions in RL, post-training, alignment, evaluation, ML systems, or model behavior.
Company:
Nuance Labs an AI research company is developing the first human foundation model that understands and displays emotion in real time. Founded in 2024, the company is headquartered in Seattle, USA, with a team of 11-50 employees. The company is currently Early Stage.

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