Research Engineer, Production Model Post Training
Research Engineer implements and scales post-training techniques like Constitutional AI and RLHF for production AI models, optimizing capabilities, alignment, and safety. Requires strong Python skills, ML systems experience, and ability to handle complex distributed training pipelines.
About the job
Responsibilities:
- Implement and optimize post-training techniques at scale on frontier models
- Conduct research to develop and optimize post-training recipes that directly improve production model quality
- Design, build, and run robust, efficient pipelines for model fine-tuning and evaluation
- Develop tools to measure and improve model performance across various dimensions
- Collaborate with research teams to translate emerging techniques into production-ready implementations
- Debug complex issues in training pipelines and model behavior
- Help establish best practices for reliable, reproducible model post-training
You may be a good fit if you:
- Thrive in controlled chaos and are energised, rather than overwhelmed, when juggling multiple urgent priorities
- Adapt quickly to changing priorities
- Maintain clarity when debugging complex, time-sensitive issues
- Have strong software engineering skills with experience building complex ML systems
- Are comfortable working with large-scale distributed systems and high-performance computing
- Have experience with training, fine-tuning, or evaluating large language models
- Can balance research exploration with engineering rigor and operational reliability
- Are adept at analyzing and debugging model training processes
- Enjoy collaborating across research and engineering disciplines
- Can navigate ambiguity and make progress in fast-moving research environments
Strong candidates may also:
- Have experience with LLMs
- Have a keen interest in AI safety and responsible deployment
We welcome candidates at various experience levels, with a preference for senior engineers who have hands-on experience with frontier AI systems. However, proficiency in Python, deep learning frameworks, and distributed computing is required for this role.
Logistics
Education requirements: We require at least a Bachelor's degree in a related field or equivalent experience.
Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.
Skills
Python, PyTorch, Constitutional Ai, RLHF, LLMs, Distributed Computing, Model Fine-Tuning, Ml Pipelines, High-Performance Computing, Deep Learning
Similar jobs
AI Research jobs
Anthropic
San Francisco, CA
Research Engineer building large-scale AI capability evaluations, telemetry, data pipelines, and analysis tools for Anthropic’s Takeoff Intel team. The role requires hands-on large language model experimentation, rapid prototyping, data expertise, and strong research collaboration.
Thinking Machines Lab
San Francisco, CA
Research role focused on improving agentic coding capabilities through reinforcement-learning training, synthetic data, coding environments, reward design, and evaluations. Requires strong Python engineering, scalable distributed-training experience, and a bachelor’s degree or equivalent; research experience and a PhD are preferred.
Thinking Machines Lab
San Francisco, CA
Conduct AI safety research across data curation, post-training, evaluations, synthetic data, and red-teaming to improve model reliability on harmful and dual-use requests. The role requires AI safety experience, Python, deep learning frameworks, and scalable technical research skills.
OpenAI
San Francisco, CA
Researcher or engineer focused on designing, evaluating, and productionizing oversight systems and safety mitigations for autonomous AI agents. The role requires strong systems or security reasoning, threat-modeling ability, and experience building practical evaluations and controls.
OpenAI
San Francisco, CA
Researcher focused on training and evaluating frontier AI agents, mining incidents, and building scalable safety measurement systems. The role requires strong research or ML engineering execution, quantitative judgment, and the ability to own ambiguous projects end to end.