Senior Machine Learning Engineer

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

📋 Description

• Design, develop, test, document, deploy, and maintain production-level machine learning and statistical modeling software.

• Automate processes and enhance customers’ mission operations.

• Construct and uphold robust, scalable pipelines for training, evaluation, deployment, and lifecycle management across cloud, on-premises, and edge computing environments.

• Collaborate with autonomy researchers, software engineers, systems teams, and field operators to convert mission requirements into deployable machine learning capabilities.

• Implement automated CI/CD workflows for machine learning systems, encompassing repeatable experiments, reliable packaging, and continuous delivery of models and data pipelines.

• Manage machine learning runtime infrastructure utilizing Docker, Kubernetes, and model-serving platforms such as Seldon, KServe, and BentoML.

• Create monitoring systems for model health, performance, data drift, system utilization, and mission relevance using Prometheus, Grafana, and the ELK/EFK stacks.

• Ensure that machine learning deployments adhere to defense, customer, and platform security requirements, with a focus on data integrity, traceability, and operational reliability.

• Assess and integrate emerging MLOps, distributed training, and edge inference technologies.

⛳️ Requirements

• 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 engineering, MLOps, or similar roles.

• Proven experience in operationalizing machine learning systems at production scale, including model training, versioning, packaging, deployment, and monitoring.

• Strong command of 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 machine learning services using Docker and orchestrating workloads with Kubernetes, including in air-gapped or constrained environments.

• Understanding of CI/CD workflows and DevOps practices as applied to machine learning systems, including Git, code reviews, and metrics evaluation.

• Familiarity with monitoring, observability, and logging platforms such as Prometheus, Grafana, and ELK/EFK.

• Capability to obtain and maintain U.S. Government security clearance; U.S. citizenship is required.

• Willingness to travel up to 20%.

• Preferred: experience in deploying models and runtimes to edge devices; optimizing models for memory- and CPU-constrained systems; supporting U.S. Department of War programs, cUAS systems, or mission-critical autonomous platforms; working with diverse data sources such as audio/acoustics, RF signals, and EO/IR imagery; deploying and optimizing ML inference on edge or resource-limited systems; knowledge of Explainable/Auditable AI/ML tools and interpretable model design; familiarity with AI software development tools like GitHub Copilot and Claude.

🏝️ Benefits

• Equity.

• Comprehensive benefits package.

• 401k with a 5% company match.

• Paid holidays and a generous paid time off offering.

• Paid leave programs.

• Patent bonus program.

• Employee referral bonus program.

• Learning and development program.

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