At a glance
Lead post-training research for LLMs by building RL pipelines, reward functions, agent environments, and evaluations. Turn customer data into production models that improve on specialized tasks and reach millions of users.
Summarized by AI from the original posting
What you'll do
- Design and run post-training pipelines using SFT, GRPO, DPO, RLVR, reward engineering, and synthetic data generation
- Build task-specific training environments and evaluations for customer domains
- Translate production data into training signals and design reward loops
- Run and analyze end-to-end training experiments
- Diagnose reward hacking, importance sampling drift, and advantage estimation instabilities
- Publish findings at top venues and contribute to open-source training libraries
Requirements
- 01Hands-on experience training LLMs with reinforcement learning
- 02Understanding of GRPO or PPO, including group advantage computation, clipped objectives, and KL penalty design
- 03Experience with reward engineering
- 04Experience building multi-turn agent environments with tool use
- 05Comfort across dataset construction, training, evaluation, and deployment
- 06Experience with production ML systems
- 07Preferred: experience with RL training frameworks
- 08Preferred: publications at NeurIPS, ICML, or ICLR focused on RL for LLMs, reward modeling, or alignment
Perks
Skills
Full description
ABOUT BASETEN
Baseten powers mission-critical inference for the world's most dynamic AI companies, like Cursor, Notion, OpenEvidence, Abridge, Clay, Gamma, and Writer. By uniting applied AI research, flexible infrastructure, and seamless developer tooling, we enable companies operating at the frontier of AI to bring cutting-edge models into production. We're growing quickly and recently raised our $1.5B Series F, led by Altimeter Capital, Conviction Partners, and Spark Capital. Join us and help build the platform engineers turn to ship AI products.
THE ROLE
This role sits at the applied end of our post-training research efforts. You will work directly with stakeholders from the world’s fastest-growing AI companies to post-train open-source models that outperform frontier closed models on their specialised tasks. Your day-to-day is finding creative ways to extract signal from complex, domain-specific datasets and building the reward functions, environments, eval harnesses, and training pipelines that turn that signal into better models. The models you train ship to production and reach millions of users.
We are looking for people with hands-on LLM fine-tuning and RL experience. Researchers who are excited by the prospect of shipping models into production, who can translate a customer's domain-specific requirements into an effective training curriculum, and who know when to be rigorous and when to iterate fast.
RECENT RESEARCH
RESPONSIBILITIES
Design and run post-training pipelines: SFT, GRPO, DPO, RLVR, reward function engineering, and synthetic data generation.
Build task-specific training environments and evals tailored to customer domains like healthcare, code generation, and legal, spanning multi-turn tool use, sandboxed execution, and agentic workflows.
Work directly with customers to translate production data into training signal, designing reward loops from real usage patterns and handling distribution shift.
Run and analyze training experiments end-to-end: diagnose reward hacking, importance sampling drift, and advantage estimation instabilities.
Publish findings at top venues and contribute to Baseten's open-source training libraries.
QUALIFICATIONS
Hands-on experience training LLMs with reinforcement learning — demonstrated understanding of GRPO or PPO beyond recipe-level reproduction, including group advantage computation, clipped objectives, and KL penalty design
Strong intuition for reward engineering: the ability to distinguish between a reward that trains effectively and one that will exploit at scale
Experience building multi-turn agent environments with tool use, not limited to single-turn question-answering setups
Comfort working across the full pipeline from dataset construction through training, evaluation, and deployment
Experience with production ML systems. Preference for candidates who have closed a training–inference loop where production data feeds back into model improvement
PREFERRED QUALIFICATIONS
Experience with RL training frameworks
Publications at NeurIPS, ICML, ICLR, focused on RL for LLMs, reward modeling, or alignment
BENEFITS
Competitive compensation, including meaningful equity
(U.S. only) 100% coverage of medical, dental, and vision insurance for employee and dependents
Flexible PTO policy including company wide Winter Break (our offices are closed from Christmas Eve to New Year's Day!)
Paid parental leave
Fertility and family-building stipend through Carrot
(U.S. only) Company-facilitated 401(k)
Exposure to a variety of ML startups, offering unparalleled learning and networking opportunities.
Apply now to embark on a rewarding journey in shaping the future of AI! If you are a motivated individual with a passion for machine learning and a desire to be part of a collaborative and forward-thinking team, we would love to hear from you.
At Baseten, we are committed to fostering a diverse and inclusive workplace. We provide equal employment opportunities to all employees and applicants without regard to race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, or veteran status.
We are an Equal Opportunity Employer and will consider qualified applicants with criminal histories in a manner consistent with applicable law (by example, the requirements of the San Francisco Fair Chance Ordinance, where applicable).
Compensation: $200K - $275K