Founding Reinforcement Learning Engineer - On-site SF

Emploive; San Francisco (CA); On-site; USD 125,000 - 200,000; Full time

Job summary

Emploive is building core reinforcement learning systems and seeks a founding engineer to own the end-to-end development from environments to production. You will work directly with the founders, shape technical direction, and move quickly in a small, early-stage team.

The role emphasizes hands-on building, distributed training, and collaboration with a tight-knit team to evolve the platform and hire future engineers.

Qualifications

• 2+ years hands-on experience in reinforcement learning or ML engineering.
• Experience with policy gradient methods, reward modeling, or RLHF preferred.
• Strong Python skills and experience with PyTorch or JAX required.

Responsibilities

• Design and build reinforcement learning environments, reward functions, and training pipelines.
• Train and fine-tune models using PPO, GRPO, DPO, RLHF, and RLAIF.
• Develop evaluation frameworks to measure model and agent performance.
• Run experiments, interpret results, and decide which approaches to explore next.
• Scale training on GPU clusters and maintain reliable pipelines.
• Turn research ideas into production systems.
• Help establish engineering culture and hire future engineers.

Skills

Python

PyTorch

JAX

Reinforcement learning

Distributed training

Ray

CUDA

Kubernetes

Education

Bachelor's degree in CS/Math/Physics

Master's or PhD is a plus

Tools

Gymnasium

Ray RLlib

MuJoCo

OpenRLHF

TRL

Job description

Emploive is building core reinforcement learning systems and seeks a founding engineer to own the end-to-end development from environments to production. You will work directly with the founders, shape technical direction, and move quickly in a small, early-stage team.

The role emphasizes hands-on building, distributed training, and collaboration with a tight-knit team to evolve the platform and hire future engineers.