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Member of Technical Staff - Post Training

Warp Sciences

South San Francisco, CAFull-timePosted todayStill listed today

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At a glance

Compensation
No compensation found
Location
South San Francisco, CA
Schedule
Full-time
Work Authorization
Not specified

Olive lists jobs from US employers, including remote roles you can work from the United States.

Job overview

Warp Sciences seeks a post-training research engineer or scientist to improve AI systems spanning robotics, perception, and cloud foundation models. The role owns post-training work from capability-gap identification through evaluation, data development, training, deployment, and production feedback. It emphasizes rigorous experimentation, reinforcement learning, measurable product improvements, and shipping model gains into real-world products.

Skills & qualifications

RequiredNice to have

Skills

Artificial IntelligenceRoboticsLife SciencesReinforcement LearningDatabase DevelopmentModel ValidationPost-TrainingRigorous EvaluationReward DesignRollout SystemsTraining StabilityFailure DiagnosisData SelectionTraining SystemsExperimentation SystemsProduct DeploymentEmbodied AILearning From InteractionMultimodal ModelsPerception ModelsAction-Generating ModelsLatency OptimizationCompute OptimizationDeployment Constraints

Qualifications

Frontier Model ExperiencePost-Training Results OwnershipState-of-the-Art ResultsProduct Shipping Track Record

Full job description

About Warp At Warp Sciences, our mission is to help the world discover new medicines faster—and build the capacity to do it here in America. We bring AI, robotics, and life sciences together in automated labs that design, run, and learn from experiments, with each experiment making the next one better. We’re not developing drugs of our own. We’re building the infrastructure for the people who do.

Warp is looking for a post-training research engineer or scientist who has worked at the frontier and knows how to turn strong models into better products.

Our AI stack spans local robotics and perception models, cloud foundation models, and the runtime connecting them. You’ll own post-training work that improves these systems’ capabilities, reliability, and performance in real use.

The most important qualification is a track record of meaningful results: you’ve built systems that support rapid, rigorous experimentation, achieved convincing state-of-the-art improvements, and shipped those improvements into real products. You lead with evals, treat data as a core part of the work, and bring deep hands-on experience with reinforcement learning.

What you’ll do

  • Own the post-training loop from identifying a capability gap through evaluation, data development, training, deployment, and production feedback.

  • Build evals grounded in product needs, with clear baselines and held-out tests that distinguish real progress from benchmark overfitting.

  • Build experimentation systems that enable fast, reproducible hill climbing and make results easy to compare and diagnose.

  • Develop and improve RL training methods, reward signals, rollout generation, and supporting infrastructure.

  • Curate, generate, and refine training data based on model failures, evaluation results, and product feedback.

  • Improve model behavior across Warp’s robotics and foundation-model stack, working closely with robotics, infrastructure, and product teams.

  • Validate gains through ablations, robust comparisons, and testing beyond the training distribution.

  • Ship improvements, measure their impact in production, and use what you learn to drive the next iteration.

What we’re looking for

  • Experience working at the frontier of model capabilities, with meaningful personal ownership of post-training results.

  • A demonstrated ability to achieve state-of-the-art results and substantiate them with rigorous evaluation against strong baselines.

  • Deep practical experience with reinforcement learning, including reward design, rollout systems, training stability, and diagnosing failure modes.

  • An eval-led approach: you define what success means before optimizing and scrutinize whether measured gains reflect the intended behavior.

  • Strong data intuition, backed by experience identifying, creating, and selecting data that produces measurable improvements.

  • Strong engineering skills and experience building reliable training and experimentation systems.

  • A track record of shipping model improvements into real products and learning from deployment.

  • An outcome-driven mindset: you follow the evidence, move quickly, and take responsibility for whether your work improves the product.

Helpful, but not required

  • Experience with robotics, embodied AI, or learning from interaction.

  • Experience post-training multimodal, perception, or action-generating models.

  • Experience balancing model quality with latency, compute, and deployment constraints across local and cloud environments.

What success looks like

You establish a credible baseline, identify the failures that matter, and build a repeatable loop that improves them. Your results hold up under rigorous evaluation and translate into better behavior in Warp’s products. Each deployment teaches us something that makes the next iteration stronger. Equal Employment Opportunity Warp is an equal opportunity employer. We consider qualified applicants without regard to race, color, religion, national origin, ancestry, sex (including pregnancy, childbirth, breastfeeding, and related medical conditions), gender, gender identity or expression, sexual orientation, age, physical or mental disability, medical condition, genetic information, marital status, military or veteran status, reproductive health decision-making, or any other characteristic protected by applicable law.

Reasonable Accommodations Warp provides reasonable accommodations to qualified applicants with disabilities in accordance with applicable law. If you need an accommodation during the application or interview process, please contact [email protected]

Fair Chance Hiring Warp considers qualified applicants with arrest or conviction records in accordance with the California Fair Chance Act and other applicable laws.

General Notice This job posting describes the position generally and does not constitute an offer of employment or an employment contract.

How this job compares

Set against the 1,187 open Research Engineer jobs on Olive Jobs, counted October 31, 2026.

Pay
  • This postingNot listed
  • California median$184–339K/yr404 salary ranges
Openings
Olive Jobs lists 580 open Research Engineer jobs in California and 1,187 across the US. 6% of them nationwide are listed as remote or flexible.
Hiring the most in California
Experience
Research Engineer postings that state a minimum ask for 3 years of experience at the median; the middle half ask for 2 to 5 years (673 postings).
Education
432 of the 1,187 open Research Engineer jobs (36%) ask for a bachelor's degree or higher.

Counted across the open, public US postings on Olive Jobs on October 31, 2026. Pay medians are the middle values of the low ends and of the high ends of postings that list both ends of a range in the same cadence (a yearly salary or an hourly rate); a range's midpoint is halfway between its ends. Pay and experience figures resting on fewer than 20 postings are left out.

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