Senior Machine Learning Data Engineer

Applied Intuition Applied Intuition · Robotics · Sunnyvale, CA · Government

The role focuses on building and evolving the data engine for training perception models, including edge applications and cloud data pipelines. It involves optimizing ML model execution, ingesting video data, and integrating foundation models for labeling and QA. The goal is to enable continual improvement of models deployed to the edge.

What you'd actually do

  1. Construct optimized data pipelines to run ML models
  2. Evolve our data engine architecture to scale high-fidelity labels, reduce annotation costs, and accelerate ML iteration cycles
  3. Integrate foundation models (LLMs, VLMs, and multimodal models) to automate and enhance labeling, quality assurance, and data discovery
  4. Leverage software-in-the-loop and hardware-in-the-loop testing
  5. Interact with the DoD customer to understand their use cases, requirements, and triage needs during field events to deliver a superior customer experience

Skills

Required

  • 5+ years of relevant work experience
  • Familiarity with modern ML infrastructure
  • data-centric AI approaches
  • running large-scale jobs on GPUs
  • Created or worked on microservices and/or databases for data-oriented software
  • U.S. citizenship
  • eligibility to obtain a security clearance

Nice to have

  • Full-stack experience React, TypeScript Python, Golang or similar
  • Experience with Docker, Kubernetes, Opensearch and Postgres
  • 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 ML systems

What the JD emphasized

  • U.S. citizenship (legally required)
  • eligibility to obtain a security clearance

Other signals

  • data engine to train perception models
  • run models at the edge
  • backhaul data to our cloud pipeline
  • continual improvement of these models
  • automating and enhancing labeling, quality assurance, and data discovery