Machine Learning Engineer - Digital Services (Remote)

Responsibilities include, but are not limited to: -Operationalize ML models by building robust pipelines for training, evaluation, deployment, and monitoring across diverse compute environments (cloud, on-prem, and edge). -Collaborate with development teams and mission stakeholders to translate requirements into ML systems that can be deployed and sustained in operational settings. -Implement CI/CD practices for ML, enabling automated testing, packaging, and deployment of models and data pipelines. -Manage ML infrastructure and tooling, including containerization (Docker), orchestration (Kubernetes), and model serving platforms (e.g., Seldon, KServe, BentoML). -Develop monitoring and observability systems to track model performance, data drift, and resource utilization, ensuring reliability in mission environments. -Contribute to security and compliance in ML pipelines, ensuring model deployments meet defense and customer requirements. -Explore and integrate modern MLOps technologies to improve reproducibility, scalability, and maintainability of ML capabilities. Necessary Skills and Experience: -Bachelor’s degree in Computer Science, Electrical Engineering, Data Science, or a related technical discipline. Master’s degree preferred. -5+ years of professional experience in software engineering, machine learning, or related fields. -Experience with MLOps tools and frameworks (MLflow, Kubeflow, Airflow, DVC, etc.). -Proficiency in building and deploying containerized ML services (Docker, Kubernetes). -Strong understanding of CI/CD pipelines and DevOps practices applied to ML. -Familiarity with deep learning frameworks (PyTorch, TensorFlow) and their deployment requirements. -Knowledge of monitoring and logging systems (Prometheus, Grafana, ELK/EFK stacks). -Strong software engineering background (Python required; C, Rust, or MATLAB a plus). -Active U.S. Government Secret clearance with SCI eligibility (TS/SCI). Beneficial Skills and Experience: -Experience in DoD programs and drone (UAS) development. -Experience working with diverse data types (RF signals, imagery, video, sensor feeds) is a plus. -Experience deploying ML models to edge or constrained environments is highly desirable. -Familiarity with secure software deployment in defense environments. -Experience with air-gapped registries, offline updates, reproducible builds, and SBOM attestation in CI. -Experience with Explainable AI/ML.