Data Engineer (Machine Learning / Infrastructure)

The Company

• A highly specialized R&D firm backed by multi-year, mission-critical federal funding.
• They build next-generation sensor fusion platforms to process complex, real-time physical telemetry.
• They operate a high-autonomy, low-bureaucracy engineering culture focused entirely on technical problem-solving.

Why Join?

• Pure building: Contribute to modern open-source ecosystems without the pressure of designing enterprise architectures from absolute scratch.
• Bridge the gap: Partner directly with data scientists to turn prototype algorithms into production-ready distributed systems.
• Complex data: Tackle massive scaling challenges using continuous spatiotemporal, numerical, and sensor data.
• Pivot your career: Transition from standard data engineering into highly specialized Machine Learning infrastructure.

The Role

A technical, hands-on role where you will:

• Build and optimize automated pipelines for the ML lifecycle (compiling training datasets, managing model versioning).
• Deploy and scale containerized Python microservices within an existing Kubernetes cluster.
• Ingest and process massive streams of time-series and spatiotemporal telemetry from remote sensors.
• Translate applied research requirements into scalable, reliable platform engineering solutions.
• Troubleshoot distributed environments for high-throughput physical data (strictly avoiding LLM or GenAI ecosystems).

The Essential Requirements

• Eligible to obtain a U.S. Security Clearance (U.S. Citizenship required).
• 3+ years in Data/Platform Engineering or ML Infrastructure with a strong foundation in Python.
• Hands-on experience deploying and managing workloads in Kubernetes.
• ML Lifecycles: Proven understanding of model deployment, versioning, and training dataset construction.
• Domain Data: Direct experience processing physical sensor, time-series, or spatiotemporal data (e.g., IoT, telemetry).

What Will Make You Stand Out

• A career path transitioning from Data Science into Platform Engineering.
• Professional background in the Defense, Telecom, Space, or Industrial IoT sectors.
• Familiarity with event-driven architectures and open-source data orchestration tools.