3 hours ago
Base Salary
$105k - $260k/yr
Responsibilities
- Build infrastructure for reliable environment execution, trajectory collection, and evaluation of agent behavior at scale.
- Develop reusable tools, APIs, and pipelines for launching experiments, managing datasets and configurations, and inspecting trajectories and evaluation results.
- Turn research prototypes and experimental workflows into tested, documented software that researchers can reuse and extend.
- Design experiments to understand model behavior, agent failure modes, and data quality issues.
- Improve orchestration, observability, failure recovery, reproducibility, experimentation speed, and cost efficiency.
- Partner with research and engineering teams to translate evolving needs into infrastructure priorities and architecture decisions, owning systems through operation.
Requirements
- Strong software engineering fundamentals and proficiency in Python, with experience building maintainable systems used by others.
- Experience owning backend services, data pipelines, developer tools, or infrastructure from design through deployment and operation.
- Sound judgment regarding APIs, data models, asynchronous workloads, and reliable-system tradeoffs.
- Comfort with Docker, Linux, cloud infrastructure, and debugging failures across application code and its underlying systems.
- Ability to work with researchers and translate open-ended experimental needs into useful software.
- Independent technical judgment for prioritization, pragmatic architecture decisions, and scaling systems with their users and workloads.
- Preferred: working knowledge of reinforcement learning, including environments, rollouts, reward signals, and evaluation workflows.
- Preferred: experience with research infrastructure, ML pipelines, distributed job execution, containerized environments, high-concurrency workloads, experiment reproducibility, data lineage, or complex pipeline failure visibility.
- Technical aptitude and learning potential are prioritized over a specific number of years of experience.
Benefits
- Competitive compensation with base salary bands of $175,000-$260,000 in San Francisco and other major US cities, $130,000-$195,000 in Singapore, and $105,000-$195,000 elsewhere.
- 100% covered medical, dental, and vision insurance for US and Singapore employees.
- Lunch and dinner for in-office employees.
- Company-wide holiday break from Christmas Eve through New Year's Day, in addition to PTO and paid holidays.
- 401(k) with a 4% match.
- Equinox membership and commuter benefits for US employees, plus a health and wellness stipend for Singapore employees.
- Access to tokens for ChatGPT, Claude Code, Cursor, and similar tools.
- Full-time work in San Francisco or Singapore, or remote work with 70-80% overlap with either time zone.
- Relocation and visa support for strong full-time candidates to the US or Singapore.
- Annual travel budget to visit either office; applications are rolling and the process includes two technical interviews and a 2-3 day work trial.
About HUD
HUD builds infrastructure for generating reinforcement-learning training data and evaluations for frontier AI agents, plus a marketplace where labs can buy and sell these assets. Its platform is used by frontier labs, Fortune 500 companies, and startups to create RL environments and post‑training datasets for RFT. Founded in 2025 and based in San Francisco, the company is privately held and a Y Combinator W25 alum.