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About
My client is a highly specialized engineering firm supporting critical federal and defense initiatives with multi-year, mission-critical funding. Their core mission focuses on building next-generation sensor fusion platforms to process complex, real-time physical telemetry and anomaly detection. They operate with a low-ceremony, high-autonomy engineering culture that prioritizes technical problem-solving over corporate bureaucracy. Why Join?
Transition your data engineering career into a specialized machine learning infrastructure role supporting low-TRL temporal data models. Work intimately with data scientists to bridge the gap between prototype algorithms and production-ready distributed systems. Solve highly complex scaling challenges involving massive streams of numerical, scientific, and spatiotemporal sensor data. Operate with high autonomy in a remote-first or hybrid environment, contributing to a modern open-source ecosystem without the pressure of designing enterprise architectures from absolute scratch. The Role
A technical, hands-on role where you will: Build and optimize data engineering pipelines to support the full machine learning lifecycle, including building training datasets and managing model versioning. Deploy and maintain containerized data workloads and Python-based microservices within an existing Kubernetes infrastructure. Manage the ingestion and processing of continuous, structured time-series and spatiotemporal data from remote sensor platforms. Collaborate directly with the applied research team to translate data science requirements into scalable, reliable platform engineering solutions. Implement orchestration and deployment workflows using open-source tools to ensure reproducible data delivery. Troubleshoot and scale distributed processing environments to handle high-throughput physical telemetry, explicitly
avoiding
LLM or Generative AI ecosystems. The Essential Requirements
Eligible to obtain a U.S. Security Clearance (U.S. Citizenship required). 3+ years of hands-on experience in data engineering, platform engineering, or machine learning infrastructure, with a strong foundation in Python. Practical experience deploying and managing containerized workloads within Kubernetes. Proven understanding of machine learning lifecycles, including model deployment, versioning, and training dataset construction. Direct experience processing structured
temporal, time-series, spatiotemporal, or physical sensor data
(e.g., IoT, telemetry). What Will Make You Stand Out
A background transitioning from Data Science or Machine Learning Engineering into Data/Platform Engineering. Previous experience operating within the defense, telecommunications, space, or industrial IoT sectors. Familiarity with event-driven architectures or specific open-source data orchestration tools. Disclaimer
No terminology in this advert is intended to discriminate on the grounds of age, sex, race, religion or belief, disability, pregnancy and maternity, marriage and civil partnership, sexual orientation, gender, and/or gender reassignment, and we confirm that we are happy to accept applications from anyone for this role. Attis Global Ltd operates as an employment agency and employment business. More information can be found at attisglobal.com. Keywords for Search (SEO) #J-18808-Ljbffr
Languages
- English
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