Research, General Agents
Research-focused engineer advancing agentic model capabilities across synthetic data, task environments, evaluations, training, and usability improvements. Requires strong Python engineering, deep learning framework experience, scalable distributed training skills, and scientific experimentation ability.
About the job
Responsibilities
- Build a synthetic data framework used across the team.
- Create task environments to scale training for agentic capabilities, including tool use, long-horizon tasks, and complex workflows.
- Address model capability gaps through data and recipe development, validating proposed changes with controlled ablation studies.
- Improve agent usability end to end by translating internal and external feedback into model improvements.
Requirements
- Strong software engineering skills, including the ability to contribute code and debug complex codebases.
- Ability to design, run, and interpret experiments with scientific rigor and clarity.
- Proficiency in Python and familiarity with at least one deep learning framework such as PyTorch, TensorFlow, or JAX.
- Experience debugging distributed training and writing scalable code.
- Bachelor’s degree or equivalent experience in computer science, machine learning, physics, mathematics, or a related discipline.
- Clear written communication and the ability to explain complex technical concepts.
Nice-to-haves
- Experience building synthetic data pipelines and systems adopted and maintained by a team.
- Experience identifying model usability gaps and addressing them through custom evaluations and training data.
- Experience making large-scale agentic reinforcement-learning infrastructure reliable.
- Experience improving the agentic capabilities of a frontier model.
- PhD in computer science, machine learning, physics, mathematics, or a related discipline, or equivalent industry research experience.
Compensation and Benefits
- Expected annual salary: $350,000–$475,000 USD.
- Health, dental, and vision benefits.
- Unlimited paid time off.
- Paid parental leave.
- Relocation support.
- Visa sponsorship may be available.
Skills
Python, PyTorch, TensorFlow, JAX, Distributed Training, Synthetic Data Pipelines, Reinforcement Learning, Model Evaluation, Deep Learning, Scientific Experimentation
Similar jobs
ML Engineering jobs
Thinking Machines Lab
San Francisco, CA
Build and operate the engineering systems that support post-training research, including reinforcement learning infrastructure, sandboxed execution, data pipelines, and agent scaffolding. The role requires strong Python and systems engineering skills, project ownership, and a relevant bachelor’s degree or equivalent experience.
Thinking Machines Lab
San Francisco, CA
Operates and improves the infrastructure powering large-scale post-training and reinforcement learning runs, partnering with researchers to debug failures, improve reliability, and automate recovery. Requires 4+ years operating distributed production systems and strong Python, Go, or C++ skills.
Thinking Machines Lab
San Francisco, CA
Researcher focused on scaling reinforcement learning for frontier models, with ownership spanning asynchronous RL algorithms, inference and distributed training systems, and large-scale empirical studies. Requires strong Python and deep learning experience, scalable systems debugging, and rigorous research judgment.
OpenAI
San Francisco, CA
Develop multimodal perception and authentication systems combining visual, audio, and other sensor signals for real-world AI products. The role requires machine learning expertise, practical research-to-system experience, and proficiency in Python and PyTorch with comfort in C++.
OpenAI
San Francisco, CA
Build and optimize OpenAI’s inference stack for AWS Trainium across high-performance kernels, compilers, runtimes, and model execution. The role requires systems programming and accelerator experience, with opportunities to solve end-to-end performance problems for frontier-scale AI models.