Machine Learning Engineer - Reinforcement Learning & SimulationLocation: San Francisco, CA
Working Arrangement: Full-time, office-based
Compensation: $120,000-$220,000 base salary + equity
The Role We're looking for a strong Machine Learning Engineer with excellent technical fundamentals and an interest in reinforcement learning, simulation, and AI systems.
Your primary focus will be scaling the production of reinforcement learning environments, turning research requirements into robust, efficient, and maintainable software systems. You'll work closely with researchers to build the infrastructure needed to run simulations at scale and support the training and evaluation of advanced AI models.
This is a highly engineering-focused role. You won't necessarily need to develop novel reinforcement learning algorithms or have an extensive robotics background. Instead, we're looking for someone who can build excellent software, solve difficult technical problems, and learn new concepts quickly.
For more experienced candidates, there is also scope to take on greater technical ownership, mentor other engineers, and help establish the engineering practices that will support the team's next phase of growth.
What You'll Be Doing - Build, scale, and productionise reinforcement learning environments used to train and evaluate advanced AI models.
- Translate research ideas and experimental requirements into reliable, high-performance engineering systems.
- Develop infrastructure to run simulation environments efficiently and at scale.
- Improve software quality, maintainability, testing, and engineering standards across the team.
- Collaborate with research colleagues to identify which environments and approaches deliver measurable improvements in model performance.
- Support model post-training, evaluation, and benchmarking where appropriate.
- Help validate simulation results through real-world model evaluation and testing on robotic systems.
- Identify opportunities to improve performance, scalability, and the efficiency of development workflows.
- Take ownership of technical challenges in a fast-moving environment where priorities can evolve quickly.
What We're Looking For The team places significant emphasis on technical ability, problem-solving, learning velocity, and ownership rather than a rigid checklist of previous experience.
You'll ideally bring:
- Strong software engineering fundamentals and the ability to build clean, reliable, production-quality systems.
- A strong general technical background, with an interest in machine learning, AI, or related disciplines.
- Excellent analytical and problem-solving skills.
- The ability to learn unfamiliar technologies and technical concepts quickly.
- A practical, hands-on approach to engineering, with the initiative to take projects from concept through implementation.
- High levels of ownership, adaptability, and intellectual curiosity.
- The motivation to work closely with a small, ambitious team tackling technically challenging problems.
Experience in one or more of the following would be advantageous:
- Machine learning engineering or research engineering.
- Reinforcement learning and simulation environments.
- Production ML systems or large-scale software infrastructure.
- GPU-accelerated computing and simulation.
- Model training, post-training, or evaluation.
- Robotics and embodied AI.
A background in robotics is not essential. The priority is strong software and AI capability rather than a purely mechanical, electrical, or controls-focused background.
Candidates with a bachelor's or master's degree, a PhD, or equivalent practical experience are all welcome to apply. Exceptional early-career engineers will be considered alongside more experienced candidates.
The Technical Environment The team is working with modern AI training and simulation technologies, including GPU-accelerated simulation and model post-training frameworks.
Technologies discussed include:
- Python and modern software engineering tools.
- Reinforcement learning and simulation workflows.
- GPU-accelerated simulation, including Newton and MuJoCo Warp.
- Model post-training tools, including Tinker.
- Model evaluation, benchmarking, and performance optimisation.
The precise stack is flexible. Strong underlying engineering ability and the capacity to pick up new tools quickly are more important than prior experience with every technology listed.
Working Environment This is a full-time, office-based role in San Francisco. The team values close collaboration, fast iteration, high ownership, and a strong commitment to delivering results.
The environment is intense and best suited to people who enjoy working hard, tackling difficult problems, and learning rapidly alongside ambitious engineers and researchers. Candidates should be comfortable with a demanding startup environment and a high level of commitment to the work.
Compensation The salary range for this position is
$120,000-$220,000 base salary, plus equity, depending on technical ability, experience, and the scope of the role.
Interested? If you're a strong ML engineer who wants to build the infrastructure behind next-generation AI systems, we'd love to hear from you.
Whether you come from a traditional software engineering background, ML engineering, research engineering, or a related technical discipline, we're interested in people who combine excellent fundamentals with the ability to learn quickly and deliver high-quality work.
