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Open nowFirst seen 3 days ago

Research Engineer, RL Environments and Infrastructure

Hyphen Connect71 open roles

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Your applicationOpen nowResearch Engineer, RL Environments and InfrastructureHyphen Connect

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The clock on this job

Early applications get read.

12.3% of postings close within 7 days. Measured by our own scanner across the market.

Share of postings closed within
  1. 2.1%1 day
  2. 4.5%3 days
  3. 12.3%7 days
  4. 19.3%14 days
  5. 35.6%30 days
  6. 57.8%60 days
This job: first seen 3 days ago

The posting

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.

Core Responsibilities

  • 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.
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