Senior Machine Learning Engineer

This is a fully remote position, open to applicants in United States.

📋 Description

• Facilitate the implementation of machine learning models by constructing and sustaining robust, scalable pipelines for training, evaluation, deployment, and lifecycle management in cloud, on-premise, and edge computing environments.

• Collaborate closely with autonomy researchers, software engineers, systems teams, and field operators to convert mission requirements into actionable ML capabilities.

• Establish automated CI/CD workflows specifically designed for ML systems, ensuring reproducible experiments, dependable packaging, and the continuous delivery of updated models alongside relevant data pipelines.

• Oversee ML runtime infrastructure utilizing containerization and orchestration frameworks (such as Docker and Kubernetes) while integrating model serving platforms (e.g., Seldon, KServe, BentoML).

• Create monitoring systems to assess model health, performance, data drift, system utilization, and mission relevance through tools like Prometheus, Grafana, and ELK/EFK stacks.

• Ensure that ML deployments comply with defense, customer, and platform security standards, focusing on data integrity, traceability, and operational reliability.

• Assess and incorporate emerging MLOps, distributed training, and edge inference technologies to improve the reproducibility, extensibility, scalability, and deployment speed of ML systems.

⛳️ Requirements

• Bachelor’s degree in Computer Science, Electrical Engineering, Data Science, or a related technical discipline (Master’s degree preferred).

• Over 5 years of professional experience in software engineering, machine learning engineering, MLOps, or similar roles.

• Proficient in operationalizing ML systems at a production scale, encompassing model training, versioning, packaging, deployment, and monitoring.

• Strong expertise in Python and familiarity with at least one deep learning framework (e.g., PyTorch, TensorFlow).

• Practical experience with MLOps frameworks and workflow tools (e.g., MLflow, Kubeflow, Airflow, DVC, BentoML).

• Experience in deploying containerized ML services using Docker and orchestrating workloads with Kubernetes (including air-gapped or constrained environments).

• Understanding of CI/CD workflows and DevOps methodologies as they apply to ML systems (e.g., Git, Code Review, Metrics Evaluation).

• Familiarity with monitoring, observability, and logging tools (e.g., Prometheus, Grafana, ELK/EFK).

• Capability to obtain and maintain U.S. Government security clearance (U.S. Citizenship required).

• Willingness to travel up to 20%.

🏝️ Benefits

• Competitive salary.

• Equity opportunities.

• Comprehensive benefits package.

• 401k plan with a 5% company match.

• Paid holidays and a generous paid time off policy.

• Paid leave programs.

• Patent bonus program.

• Employee referral bonus program.

• Learning and development initiatives.

• Opportunity to collaborate with a team of highly skilled, creative, and motivated individuals.