Overview
About the Role This is a hands-on, end-to-end ownership role on a small, high-caliber engineering team building infrastructure for reinforcement learning environments and AI evaluation. You will be the person who unblocks critical deployments for frontier AI labs and data vendors, turning urgent, ambiguous technical challenges into clean solutions and reusable tooling. What You'll Do Take the lead on diagnosing and resolving ambiguous technical problems for customers and partners. Own technical deployment requests from triage through to completion, coordinating across research and go-to-market teams. Ask the right questions to clarify under-specified asks and identify what actually needs to be done. Build tools and one-off pipelines to solve urgent customer or partner problems quickly and reliably. Balance speed and quality in fast-turnaround situations where the path is not fully define
Full job description
Full Job Description
About the Role
This is a hands-on, end-to-end ownership role on a small, high-caliber engineering team building infrastructure for reinforcement learning environments and AI evaluation. You will be the person who unblocks critical deployments for frontier AI labs and data vendors, turning urgent, ambiguous technical challenges into clean solutions and reusable tooling.
What You'll Do
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Take the lead on diagnosing and resolving ambiguous technical problems for customers and partners.
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Own technical deployment requests from triage through to completion, coordinating across research and go-to-market teams.
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Ask the right questions to clarify under-specified asks and identify what actually needs to be done.
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Build tools and one-off pipelines to solve urgent customer or partner problems quickly and reliably.
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Balance speed and quality in fast-turnaround situations where the path is not fully defined.
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Document recurring issues and convert repeated manual work into reusable tools and processes.
What We're Looking For
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2 to 4 years of experience in applied research engineering, forward-deployed engineering, or a comparable hands-on technical role.
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Strong generalist AI engineering skills with the ability to move quickly and independently.
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Proficiency in Python, Docker, and Linux environments.
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Experience working on benchmarks and evals, with sound judgment about what makes a task realistic, a rubric reliable, and a trajectory useful for RL training.
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Strong debugging instincts across code, data, and environments.
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Demonstrated ability to operate independently in ambiguous situations without a fully prescribed roadmap.
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Experience handling urgent production, customer, or deployment issues.
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Comfort working directly with technical customers, vendors, or cross-functional internal teams.
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Early-stage startup experience and an ability to thrive in fast-paced environments.
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Strong written and verbal communication skills for collaboration across time zones.
Compensation & Benefits
Salary range: USD 150,000 to 250,000 annually. Visa sponsorship is available.
Location
On-site in Singapore.
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