Research Manager

HUD; Singapore; Hybrid; SGD 120,000 - 180,000; Full time

Benefits offered by this job

Medical/dental/vision

Office meals

Holiday break

Equinox membership

401k

Commuter benefits

Unlimited tokens access

Job summary

Qualifications

• Experience leading technical research projects from problem to working results.
• Strong ability to mentor researchers and research engineers while staying hands-on.
• Deep understanding of ML, RL, and how data/feedback shapes model behavior.

Responsibilities

• Set research direction for data quality and evaluation for training agents.
• Lead researchers from problem definition through experiments and conclusions.
• Design and review experiments linking model behavior to data, env, and rewards.
• Develop scalable data validation methods for training data and evals.
• Collaborate with engineers, domain experts, and data vendors to improve workflows.
• Communicate tradeoffs clearly to prioritize work across research areas.

Skills

Research leadership

Machine learning

Reinforcement learning

Experiment design

Communication

Mentoring engineers

Education

PhD in ML

Job description

About HUD

HUD is building infrastructure to create RL training data and evals for frontier AI agents, as well as a marketplace to sell these to frontier labs through the HUD marketplace. Our platform is used by frontier labs, Fortune 500 companies, and startups. We’ve raised $16M from top VCs and were YC W25.

About HUD

HUD is building infrastructure to create RL training data and evals for frontier AI agents, as well as a marketplace to sell these to frontier labs through the HUD marketplace. Our platform is used by frontier labs, Fortune 500 companies, and startups. We’ve raised $16M from top VCs and were YC W25.

About The Role

We’re looking for a Research Manager to lead research that makes HUD’s agent training data and evals more useful for improving frontier models. You’ll lead research engineers through ambiguous projects, and turn findings into methods that can be used at scale.

You’ll stay close to the technical work while helping the team choose the right questions, run rigorous experiments, and deliver results. This role calls for an understanding of how and why models train: which signals teach useful behavior, where apparent progress is misleading, and how data and eval design can change outcomes.

Responsibilities

• Set the research direction for data quality, including how HUD measures whether tasks, trajectories, rewards, and evals are reliable and useful for training agents
• Lead research engineers from problem definition through experiments, implementation, and clear conclusions; coach them to strengthen technical judgment and execution
• Design and review experiments that connect model behavior and failure modes to data, environment, and reward design
• Develop methods for validating and improving training data at scale, including trajectory audits, grader checks, and feedback loops
• Partner with research engineers, domain experts, and data vendors to turn research insights into better workflows, tools, and quality standards
• Communicate findings and tradeoffs clearly so the team can prioritize work and apply what it learns across research areas

You may be a good fit if you have:

• Experience leading technical research projects to completion, from an open question to evidence, a decision, and a working result
• Experience directly managing and mentoring researchers or research engineers while remaining engaged in technical work
• A strong understanding of machine learning and reinforcement learning, including how training objectives, data, and feedback shape model behavior
• Experience with agent training data, evals, benchmarks, synthetic data, or model evaluation infrastructure
• Sound experimental judgment: you can distinguish a useful training signal from a task or metric that only looks convincing
• Strong written communication and the ability to explain methods and findings to researchers, engineers, and external partners

Strong Candidates May Also Have

• Built scalable data quality systems or validation pipelines for model training
• Experience diagnosing reward hacking, grader errors, or other subtle agent failure modes
• Experience translating research findings into tools and processes used by others
• Early-stage startup experience and strong communication skills for collaboration across teams and time zones

We prioritize technical aptitude and learning potential over years of experience. Motivated candidates are encouraged to apply even if they don't meet all criteria.

Team & Company Details

• Team Size : ~25 people currently, mostly full‑time in‑person, but some remote.
• Our team: Our team includes 4 International Olympiad medalists (IOI, ILO, IPhO), serial AI startup founders, and researchers with publications at ICLR, NeurIPS, etc.
• Company stage: We have 8 figures in funding and are scaling profitably and quickly to meet very strong demand.

Logistics

• Employment : Full‑time.
• Location : We have offices in San Francisco or Singapore but are open to remote candidates who can work hours that 70-80% overlap with either San Francisco or Singapore time zones.
• Visa Sponsorship : We provide support for relocation and visas for strong full‑time candidates to the US or Singapore.
• Timeline : Applications are rolling. The process is 2 technical interviews and a 2‑3 day work trial.

What we offer

• Competitive compensation
• 100% covered top‑of‑the‑line medical, dental, and vision from Blue Shield of CA (US employees)
• Lunch and dinner when you’re in the office (in‑office employees)
• Company‑wide holiday break (Christmas Eve to New Year’s Day) on top of PTO and paid holidays
• Other perks including an Equinox membership, 401k, and commuter benefits (US employees)
• Unlimited* access to tokens for ChatGPT, Claude Code, Cursor, etc. *By unlimited, we mean no one on our token usage leaderboard has ever hit a limit. So we have no idea what the limit is.

Due to high volume, we may not actively respond to every application, but feel free to contact us at [[email removed]] or elsewhere if we missed your application!