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Research Scientist Optimization Jobs in California

Stealth Startup

Research Scientist

Sunnyvale, CA · On-site

Research Scientist We are looking for a Research Scientist to help build next-generation foundation ... GPU performance optimization experience, including FlashAttention-style kernels, sparse or linear ...

Stealth Startup

Research Scientist

Alameda, CA · On-site

Research Scientist We are looking for a Research Scientist to help build next-generation foundation ... GPU performance optimization experience, including FlashAttention-style kernels, sparse or linear ...

Stealth Startup

Research Scientist

Santa Rosa, CA · On-site

Research Scientist We are looking for a Research Scientist to help build next-generation foundation ... GPU performance optimization experience, including FlashAttention-style kernels, sparse or linear ...

Stealth Startup

Research Scientist

Sonoma, CA · On-site

Research Scientist We are looking for a Research Scientist to help build next-generation foundation ... GPU performance optimization experience, including FlashAttention-style kernels, sparse or linear ...

Stealth Startup

Research Scientist

San Jose, CA · On-site

Research Scientist We are looking for a Research Scientist to help build next-generation foundation ... GPU performance optimization experience, including FlashAttention-style kernels, sparse or linear ...

Stealth Startup

Research Scientist

San Mateo, CA · On-site

Research Scientist We are looking for a Research Scientist to help build next-generation foundation ... GPU performance optimization experience, including FlashAttention-style kernels, sparse or linear ...

Stealth Startup

Research Scientist

Santa Clara, CA · On-site

Research Scientist We are looking for a Research Scientist to help build next-generation foundation ... GPU performance optimization experience, including FlashAttention-style kernels, sparse or linear ...

Stealth Startup

Research Scientist

San Francisco, CA · On-site

Research Scientist We are looking for a Research Scientist to help build next-generation foundation ... GPU performance optimization experience, including FlashAttention-style kernels, sparse or linear ...

Stealth Startup

Research Scientist

Hayward, CA · On-site

Research Scientist We are looking for a Research Scientist to help build next-generation foundation ... GPU performance optimization experience, including FlashAttention-style kernels, sparse or linear ...

Stealth Startup

Research Scientist

Fremont, CA · On-site

Research Scientist We are looking for a Research Scientist to help build next-generation foundation ... GPU performance optimization experience, including FlashAttention-style kernels, sparse or linear ...

Collate

AI Research Scientist

San Francisco, CA · On-site

What You'll DoExplore new LLM prompt optimization, robustness of LLM applications and modeling ... Motivation to apply AI research in Life sciences , where rigor, safety, and impact matter deeply.

Collate

AI Research Scientist

San Francisco, CA · On-site

What You'll DoExplore new LLM prompt optimization, robustness of LLM applications and modeling ... Motivation to apply AI research in Life sciences , where rigor, safety, and impact matter deeply.

Filtrous

Research Scientist

Poway, CA · On-site

$90/hr

Research Scientist Turn Innovation into Products That Labs Depend On Why This Role Matters The next ... Design and execute experimental workflows for the discovery, optimization, and validation of new ...

Phonic

Research Scientist

San Francisco, CA · On-site

About The Role As a Research Scientist at Phonic, you'll drive original research that pushes the ... Fluency in the math, probability, optimization, linear algebra, and the ability to reason about ...

Hedra

Research Scientist

San Francisco, CA · On-site

$200K - $325K/yr

Contribute to Hedra's research culture and external scientific reputation Qualifications: * PhD in ... Experience with RLHF, DPO, or preference optimization for model alignment is a plus * Strong ...

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All JobsResearch Scientist Optimization JobsResearch Scientist Optimization Jobs in California

Research Scientist Optimization information

What does a research scientist in optimization do?

A Research Scientist in Optimization specializes in developing and applying mathematical techniques to improve processes, systems, or algorithms. Their work often involves formulating optimization problems, designing solutions, and collaborating with engineers or data scientists to implement and test their models. These scientists may work in various industries, such as technology, logistics, finance, or manufacturing, to help organizations make better decisions, save resources, or improve performance. Their daily tasks include conducting experiments, analyzing large datasets, and publishing findings in scientific journals.

What are the key skills and qualifications needed to thrive as a research scientist in optimization?

To excel as a Research Scientist in Optimization, you need a strong background in mathematics, computer science, and optimization theory, often supported by a PhD in a related field. Familiarity with programming languages like Python or MATLAB, optimization libraries (e.g., Gurobi, CPLEX), and experience with data analysis tools are typically required. Critical thinking, creativity, and strong communication skills help in formulating novel approaches and presenting complex findings clearly. These skills drive the development of efficient algorithms and solutions, advancing research impact and innovation in the field.

What types of projects and collaborations can a research scientist in optimization expect to be involved in?

As a Research Scientist specializing in Optimization, you can expect to work on projects that involve developing and improving algorithms to solve complex real-world problems in areas such as logistics, supply chain, or machine learning. Collaboration is common, often involving cross-functional teams with data scientists, software engineers, and domain experts to implement and test optimization solutions. You may also contribute to academic publications, attend conferences, and sometimes mentor junior researchers, all while staying current with the latest advancements in optimization techniques.

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

AspectResearch Scientist OptimizationData Scientist
Required CredentialsMaster's or PhD in Operations Research, Mathematics, or related fieldsBachelor's or Master's in Data Science, Statistics, or related fields
Work EnvironmentResearch labs, R&D departments, academiaBusiness analytics, tech companies, consulting firms
Industry UsageOptimization problems, algorithm development, mathematical modelingData analysis, predictive modeling, data visualization

Research Scientist Optimization focuses on developing mathematical models and algorithms to solve complex optimization problems, often in research or academic settings. Data Scientists analyze large datasets to extract insights and build predictive models for business decisions. While both roles require strong analytical skills, Research Scientist Optimization emphasizes mathematical and algorithmic development, whereas Data Scientists focus on data analysis and interpretation.

What are popular job titles related to Research Scientist Optimization jobs in California?

For Research Scientist Optimization jobs in California, the most frequently searched job titles are:

What job categories do people searching Research Scientist Optimization jobs in California look for?

The top searched job categories for Research Scientist Optimization jobs in California are:

What cities in California are hiring for Research Scientist Optimization jobs?

Cities in California with the most Research Scientist Optimization job openings:

Research Scientist Optimization jobs near you
Infographic showing various Research Scientist Optimization job openings in California as of August 2026, with employment types broken down into 1% Internship, 1% As Needed, 82% Full Time, 13% Part Time, 1% Temporary, and 2% Contract. Highlights an 83% Physical, 4% Hybrid, and 13% Remote job distribution.

Applied Scientist, Optimization & Logistics

Sprinter Health

San Francisco, CA • On-site

$160K - $220K/yr

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Re-posted 17 days ago

Job description

About Sprinter Health:
At Sprinter Health, our mission is reimagining how people access care by bringing it directly to their homes. Nearly 30% of patients in the U.S. skip preventive or chronic care simply because they can't get to a doctor's office. For many, the ER becomes their first touchpoint with the healthcare system-driving over $300B in avoidable costs every year.
By using the same technologies that power leading marketplace and last-mile platforms, we deliver care where people are, especially those who need it most. So far, we've supported more than 2 million patients across 22 states, completed 130,000+ in-home visits, and maintained a 92 NPS. Our team of clinicians, technologists, and operators have raised over $125M to date investors like a16z, General Catalyst, GV, and Accel and enjoy multi-year runway.
About the Role
We're looking for an Applied Scientist to turn Sprinter's hardest logistics problems into optimization models and decision systems that get the right clinician to the right patient at the right time. Sprinter runs a two-sided operation - clinicians on one side, patients who need care at home on the other - and we must match supply to demand across large regions under complex constraints.
As an Applied Scientist, you will take ambiguous operational problems and shape them into well-posed tasks, strong baselines, and honest evaluations. The algorithms you build will answer questions like which clinician sees which patient, in what order, given drive time, appointment windows, and clinical constraints; how many clinicians to staff in each region next month; and how long a visit will take or whether a patient is likely to cancel.
This role sits at the intersection of research and engineering, blending scientific rigor with a deployment-oriented mindset. It also requires close cross-functional partnership with operations, product, and engineering stakeholders. The ideal candidate is a scientist-engineer who reasons from first principles about uncertainty and constraints, reaches for the simplest model that works, and can move from a formulation on the whiteboard to a decision that runs in production.
Hybrid & Office Experience
We operate on a hybrid schedule, working from the office Monday through Thursday, with Fridays designated as work-from-anywhere days.
We care deeply about work-life balance and are happy to provide flexibility when life happens. We ask that employees be in the office Monday through Thursday to collaborate with their teams while maintaining flexibility where it matters most.
Lunch is provided every day, and the entire team takes an hour to eat together. It's one of the ways we stay connected outside of meetings. You'll usually find us playing a board game before getting back to work.
What you will do:
Modeling & Optimization
  • Turn ambiguous operational problems into well-posed optimization, forecasting, or simulation tasks.
  • Build strong baselines and improve on them efficiently, adding complexity only when the value justifies it.
  • Develop solutions across operations research, optimization, and machine learning, choosing the right tool for the problem.
  • Run careful analysis and iterate toward decisions that improve real operational outcomes - cost per visit, clinician utilization, patient access, and visits completed.
Evaluation & Scientific Rigor
  • Design offline evaluations, simulated backtests, and live experiments that predict real-world operational impact.
  • Find the gaps between a model's assumptions and messy operational reality before they reach production.
  • Choose metrics suited to stochastic, constrained, and partially observed operational systems.
  • Interpret and communicate results effectively to cross-functional stakeholders.
Collaboration & Delivery
  • Partner with Engineering to productionize optimization and decision systems reliably.
  • Work with operations partners and SMEs to validate assumptions and review where decisions break down.
  • Explain tradeoffs, uncertainty, and limitations clearly to product and leadership.

What you have done:
  • Strong foundations in operations research or optimization: modeling, algorithms, experimental design, and honest evaluation.
  • Strong Python and SQL, the standard optimization and ML libraries, and the ability to run your own experiments end to end.
  • Fluency with AI coding assistants (e.g., Claude Code, Cursor) in your day-to-day development workflow.
  • Ability to turn an ambiguous problem into a well-posed optimization or forecasting task, discover and analyze related literature, and adapt/apply those methods to our tasks.
  • Judgment about how uncertainty, constraints, and edge cases behave in real-world operational data.
  • Interest in operations collaboration and applied healthcare impact.

What gives you an edge:
  • MS or PhD in operations research, industrial engineering, computer science, applied math, statistics, machine learning, or a related quantitative field; exceptional applied experience can substitute.
  • Depth in a relevant area such as vehicle routing, scheduling, stochastic optimization, discrete-event simulation, queueing, or demand forecasting.
  • Experience shipping optimization or decision systems that reached production and had material real-world impact.
  • Hands-on experience with supply-and-demand matching in a marketplace, dispatch, or field-operations setting.
  • Fluency deciding when an exact optimization approach beats a heuristic or learned one, and vice versa.

Interview Process:
  • We aim to complete the interview process between 2-3 weeks. It will usually consist of:
    • Recruiter Screen (30 minutes)
    • Hiring Manager Introduction (30 minutes)
    • Hands-on-Keys Technical Assessment (1 hour)
    • Onsite Interview: Systems Design / Technical Case Study + Research Presentation + Behavioral Interview + Lunch with the Team (4 hours)
    • References

What we offer:
  • Meaningful pre-IPO equity
  • Medical, dental, and vision plans 100% paid for you and your dependents
  • Flexible PTO + 10 paid holidays per year
  • 401(k) with match
  • 16-week parental leave policy for birthing parent, 8 weeks for all other parents
  • HSA + FSA contributions
  • Life insurance, plus short and long-term disability coverage
  • Free daily lunch in-office
  • Annual learning stipend

Apply