Research Engineer, RL Environments and Infrastructure

Hyphen Hyphen LLC

San Francisco, CA, United States

24 days ago

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Role details

Contract type

Permanent contract

Employment type

Full-time (> 32 hours)

Working hours

Regular working hours

Job source

www.indeed.com

Tech stack

Training Data Application Programming Interfaces (APIs) Artificial Intelligence C++ (Programming Language) Nvidia CUDA Databases Python (Programming Language) TypeScript Reinforcement Learning Rust (Programming Language) Graphics Processing Unit (GPU) Golang

Job description

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.

Requirements

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.