Data Engineer - Axion
Builds scalable data pipelines and data engine architecture for machine learning, integrating foundation models to automate labeling and discovery. The role requires 5+ years of experience, modern ML infrastructure expertise, and U.S. citizenship with security-clearance eligibility.
About the job
Responsibilities
- Construct optimized data pipelines to run machine learning models.
- Evolve data engine architecture to scale high-fidelity labels, reduce annotation costs, and accelerate machine learning iteration cycles.
- Integrate foundation models, including LLMs, VLMs, and multimodal models, to automate and enhance labeling, quality assurance, and data discovery.
- Leverage software-in-the-loop and hardware-in-the-loop testing.
- Interact with DoD customers to understand use cases, requirements, and triage needs during field events.
Requirements
- 5+ years of relevant work experience.
- Familiarity with modern machine learning infrastructure, data-centric AI approaches, and running large-scale jobs on GPUs.
- Experience creating or working on microservices and/or databases for data-oriented software.
- Willingness to learn and grow into a position of ownership and impact on a new product team.
- U.S. citizenship, legally required, and eligibility to obtain a security clearance.
Nice to Have
- Full-stack experience with React and TypeScript.
- Experience with Python or Golang.
- Experience with Docker, Kubernetes, OpenSearch, and PostgreSQL.
- Direct experience with foundation models, including LLMs and VLMs, for data automation tasks.
- Background in autonomous driving or robotics perception.
- Experience with active learning, auto-labeling, or human-in-the-loop machine learning systems.
Compensation and Benefits
- Base salary, equity, and benefits.
- Comprehensive health, dental, vision, life, and disability insurance coverage.
- 401(k) retirement benefits with employer match.
- Learning and wellness stipends.
- Paid time off.
Skills
Python, Go, React, TypeScript, Docker, Kubernetes, Opensearch, Postgres, Gpu Computing, Microservices, Machine Learning Infrastructure, Foundation Models, LLMs, Vlms, Active Learning
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