Founding Reinforcement Learning Engineer
At a glance
Mid-level Machine Learning Engineering role at Clera. San Francisco · full-time · $125,000–$200,000 base.
aimlroles summary, based on the employer's posting.
What you'll do
- Build RL environments, reward functions and training workflows, then train and tune models with the listed methods
- Create ways to assess model and agent performance, run experiments and choose what to investigate next
- Scale training across GPU clusters and keep the pipelines dependable
- Move research concepts into production, while shaping engineering practices and hiring future teammates
What you bring
- At least two years working hands-on in reinforcement learning or machine learning engineering
- Strong Python skills and substantial experience with PyTorch or JAX
- Practical model-training work with reinforcement learning, such as policy gradients, reward modeling or RLHF
- A degree in computer science, mathematics, physics or a related area, plus experience taking research into production
Who this fits
This suits an engineer who wants to build reinforcement learning systems end to end and help set technical direction. The role is on-site in San Francisco; you must already be based there or be willing to relocate. Visa sponsorship is available.
From the employer
About the Role
Join an early-stage AI team building reinforcement learning systems from the ground up. You will own the end-to-end process, from environment design and model training to evaluation, helping shape the technical direction and engineering culture.
What You'll Do
Design and build reinforcement learning environments, reward functions, and training pipelines.
Train and fine-tune models using methods such as PPO, GRPO, DPO, RLHF, and RLAIF.
Develop evaluation frameworks to measure model and agent performance.
Run experiments, interpret results, and decide what to explore next.
Scale training on GPU clusters and keep training pipelines reliable.
Turn research ideas into production systems.
Help establish engineering practices and hire future team members.
What We're Looking For
At least 2 years of hands-on experience in reinforcement learning or machine learning engineering.
Strong Python skills and deep experience with PyTorch or JAX.
Practical experience training models with reinforcement learning methods, including policy gradients, reward modeling, or RLHF.
Experience with distributed training, GPU infrastructure, RL environments, reward functions, training pipelines, and model or agent evaluation.
A track record of taking research ideas into production systems.
A degree in computer science, mathematics, physics, or a related field; a master's or PhD is a plus.
LLM post-training or agent training experience is beneficial, as are RL publications or open-source work and experience with simulation frameworks or training libraries.
Compensation & Benefits
Base salary: $125,000 to $200,000 USD annually. Visa sponsorship is available.
Location
On-site in San Francisco, California, United States. Candidates must be based in or willing to relocate to San Francisco.
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