This is a hands-on, end-to-end technical role on a small, high-caliber engineering team building infrastructure for reinforcement learning environments and AI evaluation. You will own urgent and ambiguous technical challenges for frontier AI labs and data vendors, turning repeated firefighting into reusable tooling that drives critical deployments forward.
Diagnose and resolve ambiguous technical problems, owning deployment requests from triage to completion.
Clarify underspecified asks by asking the right questions to identify what actually needs to be done.
Build one-off tools and pipelines to solve urgent customer and partner problems under time pressure.
Coordinate with research and go-to-market teams to unblock deployments.
Balance speed and quality when customers need fast turnaround and the path is not fully specified.
Document recurring issues and convert repeated manual work into reusable tools and processes.
2 to 4 years of experience in applied research engineering, forward-deployed engineering, or a closely related hands-on technical role.
Proficiency in Python, Docker, and Linux environments.
Experience working on benchmarks and evals, with sound judgment about task realism, rubric reliability, and trajectory quality for RL training data and AI agents.
Strong debugging instincts across code, data, and environments.
Proven ability to operate independently in ambiguous situations without a fully defined roadmap.
Strong judgment about when to move fast, when to escalate, and when correctness or security requires extra care.
Comfort working directly with technical customers, vendors, or cross-functional teams.
Experience handling urgent production, customer, or deployment issues.
Early-stage startup experience and the ability to thrive in fast-paced environments.
Strong written and verbal communication skills for remote collaboration across time zones.
Salary range: $150,000 to $250,000 USD annually. Visa sponsorship is available.
On-site in Singapore. Candidates willing to relocate are welcome to apply.
Job details are sourced from the employer's original posting.
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