Senior Software Engineer, Reinforcement Learning
New
H
HUDAI data infrastructure
Secondary Locations: Singapore, Remote (Asia), Remote (North America); open to remote candidates, 70–80% overlap with either San Francisco or Singapore time zonesFull-TimeSenior
Salary100,000 - 170,000 USD per year
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Job Details
- Experience
- We prioritize technical aptitude and learning potential over years of experience.
- Required Skills
- AWSDockerPythonKubernetesTypeScriptNext.jsReactCI/CD
Requirements
- Bring strong software engineering fundamentals and full-stack range.
- Be proficient in Python and a modern web stack such as React, TypeScript, Next.js, or similar.
- Have experience owning user-facing or internal products end-to-end.
- Be comfortable with cloud infrastructure, Docker, CI/CD, observability, and production debugging.
- Build intuitive tools for technical and non-technical users.
- Communicate effectively across research, engineering, operations, vendors, and founders.
- Experience with data collection, labeling, annotation, evaluation, or research tooling platforms is a plus.
- Experience with dashboards, review workflows, observability tools, or debugging interfaces for complex systems is a plus.
- Experience with developer tools, infrastructure products, internal platforms, or workflow products is a plus.
- Experience with AWS, Kubernetes, Terraform, Docker, Grafana, or similar infrastructure tools is a plus.
Responsibilities
- Develop tools for browsing environments, inspecting trajectories, reviewing task quality, debugging failures, and understanding model behavior.
- Build vendor workflows for creating, submitting, testing, and iterating on RL environments and training data.
- Create dashboards and observability tools for environment quality, evaluation results, data collection progress, grader issues, reward signal problems, and pipeline health.
- Design backend services and APIs connecting task authoring, data collection, evaluation, QA/QC, and RL training infrastructure.
- Partner with research, operations, and GTM teams to turn ambiguous requests into well-designed systems.
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