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# Research Engineer, RL Environments and Infrastructure

- **Company:** Hyphen Hyphen LLC
- **Location:** San Francisco, CA, United States
- **Contract:** Permanent contract
- **Skills:** Training Data, Application Programming Interfaces (APIs), Artificial Intelligence, Systems Engineering, C++ (Programming Language), Nvidia CUDA, Databases, Python (Programming Language), TypeScript, Reinforcement Learning, Rust (Programming Language), Graphics Processing Unit (GPU), Data Management, Golang
- **Published:** October 26, 2026
- **Apply:** https://www.careerbuilder.com/job-details/research-engineer-rl-environments-and-infrastructure-san-francisco-ca--7b6f64e4-db4c-4ebf-8c53-2d90483b8fee

## About the Role

Reinforcement Learning, Data Management, Artificial Intelligence (AI), Systems Engineering, High Throughput, Research Skills, Application Programming Interface (API), Operating Systems, Metrics, Benchmarking, * Production AI Experience: Track record of deploying evaluations, RL loops, or sandboxed agent environments into production.

 * Systems Polyglot: Deep systems background (Python, Rust, C++, Go, TypeScript, CUDA)-you pick up new tools and frameworks within days.
 * San Francisco On-Site: In-person collaboration at our SF office to iterate quickly with founders and domain experts.
 * Pragmatic Problem Solver: Comfortable navigating raw paper implementations, undocumented SDKs, and custom distributed training setups.

## Description

We are looking for a hybrid Systems Engineer and AI Researcher to lead the development of our agent evaluation framework and post-training data pipelines. You will design sandboxed execution environments, high-throughput reinforcement learning feedback loops, and robust evaluation setups that handle non-deterministic agent outputs., * Build Stateful Agent Environments: Design deterministically verifiable, stateful sandboxes (web, OS, API, database) where agents can execute 50+ step action trajectories safely.

 * Scale Post-Training & RL Pipelines: Implement high-throughput post-training infrastructure (RLHF, Direct Preference Optimization, Process-Supervised Reward Models) for dynamic policy optimization.
 * Design Enterprise Benchmarks: Formulate evaluation metrics and automated grading harnesses that catch agent drift, hallucination, and loops in realistic enterprise environments.
 * Systems Optimization: Keep latency low and compute efficiency high across distributed GPUs and sandboxed runtime environments.