Join Baseten as a Post-Training Applied Researcher to enhance AI models for leading companies in a collaborative environment.
Posted by employer 3 days ago
First seen on Joblaze 1 day ago
Last verified on the company career page 1 day ago
Skills & Technologies
What you'll build
- Design and run post-training pipelines
- Build task-specific training environments
- Translate production data into training signal
- Run and analyze training experiments
- Publish findings at top venues
Must have
- Hands-on experience training LLMs with reinforcement learning
- Strong intuition for reward engineering
- Experience building multi-turn agent environments
- Comfort working across the full pipeline from dataset construction through training
- Experience with production ML systems
Nice to have
- Experience with RL training frameworks
- Publications at NeurIPS, ICML, ICLR
AI in the day-to-day
We enable companies to bring cutting-edge models into production.
Not disclosed in this posting: compensation, years of experience, work arrangement, visa sponsorship.
Benefits
Joblaze summary
In the role of Post-Training Applied Researcher at Baseten, the individual will focus on enhancing open-source models by collaborating with AI companies to develop effective training pipelines and reward functions tailored to specific domains. Key skills include hands-on experience with LLM fine-tuning and reinforcement learning, as well as a strong understanding of reward engineering. This position is ideal for researchers with a background in machine learning who are eager to translate complex datasets into actionable insights and production-ready models.
Joblaze insights
- Listed yesterday — first seen on Joblaze October 9, 2026. Last confirmed on baseten's careers page October 9, 2026.
- LLM appears in 7.1% of 465 comparable mid ai/ml roles in United States; synthetic data generation appears in 0.2% of 465 comparable mid ai/ml roles in United States.
Quick facts
- What's the tech stack?
- Joblaze extracted these technologies from the posting: LLM, multi-turn agent environments, reinforcement learning, reward engineering, synthetic data generation.
- What seniority level is this role?
- baseten targets mid-level candidates for this position.
- Is this full-time or contract?
- Full-time for this Post-Training Applied Researcher role at baseten.
From the original posting
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
Paid parental leave
Fertility and family-building stipend through Carrot
(U.S. only) Company-facilitated 401(k)
Standard company text repeated across baseten's postings is omitted here.