Associate or Mid-Level AI/ML Engineer, Onsite in Boulder

We are looking for an entry- or mid-level engineer with hands-on skills in machine learning, cloud infrastructure, and software development to help build AI capabilities for satellite operations.

Our client is a profitable, self-funded, 40-person aerospace engineering firm supporting mission-critical civil, commercial, and government space applications. As the company expands its automated satellite operations capabilities, you’ll help develop the pipelines, infrastructure, and tools that bring machine learning into production.

The Opportunity

This is a hands-on engineering role working closely with software, data engineering, and mission operations teams. You’ll contribute to model development and deployment, build data processing workflows, and help apply AI to real satellite operations challenges. We welcome early-career candidates with substantial project, research, or internship experience, as well as mid-level engineers with experience deploying production systems. The scope of your responsibilities will reflect your experience.

What You’ll Do

• Build and maintain data pipelines supporting machine learning training, validation, and deployment.
• Help develop and deploy models using PyTorch, TensorFlow, or similar frameworks.
• Contribute to AWS infrastructure and containerized environments for training and inference.
• Implement MLOps workflows for model versioning, registration, deployment, and monitoring.
• Develop proofs of concept for predictive telemetry analysis, intelligent automation, and workflow integrations.
• Collaborate with software engineers and mission operations teams to integrate AI capabilities into satellite tracking and operations systems.
• Improve testing, observability, reliability, and performance as models move into production.

What You Bring

• Technical foundation: A degree in Computer Science, Mathematics, Physics, Electrical Engineering, or a closely related technical discipline. A master’s degree or PhD is a plus.

• Programming skills: Hands-on Python experience and sound software engineering fundamentals.

• Machine learning experience: Practical experience with PyTorch, TensorFlow, or similar frameworks through professional work, research, internships, or substantial technical projects.

• Cloud and infrastructure familiarity: Experience with AWS, Linux, and containers, plus an understanding of networking and security fundamentals. Exposure to ECS or EKS is a plus.

• MLOps interest or experience: Familiarity with model training, evaluation, deployment, and monitoring, with an interest in building reliable, repeatable workflows.

• Collaborative approach: Curiosity, clear communication, and a willingness to learn while contributing directly to engineering work.

• Onsite availability: Ability to work in Central Boulder five days a week.

• Clearance eligibility: U.S. citizenship and the ability to obtain and maintain a U.S. Government security clearance.

Experience with CUDA, model optimization, distributed systems, or production ML deployments is helpful