Senior RL Environment Architect - Hybrid (Menlo Park)

Simulation 95; Menlo Park (CA); Hybrid; USD 360,000 - 440,000; Full time

Job summary

Simulation95 in Menlo Park, CA is seeking a Senior Reinforcement Learning Environment Engineer to build scalable training environments and tools for AI agents. You will design observations, actions, rewards, and evaluation metrics, collaborating with ML engineers to ensure reproducibility and efficiency.

The role blends software engineering with RL research, offering meaningful responsibility in a hybrid, full-time setting with a highly competitive compensation package.

Qualifications

• Strong Python skills and experience building reliable software systems.
• Experience with reinforcement learning environments, simulation, agent infrastructure, or automated evaluation.
• Practical understanding of reward design, partial observability, and long-horizon tasks.
• Experience with APIs, containers, testing, and concurrent or distributed execution.
• Ability to define an approach to an ambiguous problem and carry it through implementation and delivery.

Responsibilities

• Build interactive environments with clearly defined observations, actions, state transitions, and success criteria.
• Develop task generators, reward functions, and reliable verification systems.
• Create sandboxed tools and interfaces for agent workflows involving multiple steps.
• Make environments easy to reset, reproduce, inspect, and run at scale.
• Identify reward exploitation, information leakage, and environment failures that could distort training or evaluation results.
• Connect environments to training and evaluation pipelines in collaboration with machine learning engineers.

Skills

Python

RL environments

Software engineering

APIs

Containers

Testing

Distributed systems

Problem solving

Tools

Git

CI/CD

Job description

Simulation95 in Menlo Park, CA is seeking a Senior Reinforcement Learning Environment Engineer to build scalable training environments and tools for AI agents. You will design observations, actions, rewards, and evaluation metrics, collaborating with ML engineers to ensure reproducibility and efficiency.

The role blends software engineering with RL research, offering meaningful responsibility in a hybrid, full-time setting with a highly competitive compensation package.