The Aerospace Corporation is the trusted partner to the nation’s space programs, solving the hardest problems and providing unmatched technical expertise. As the operator of a federally funded research and development center (FFRDC), we are broadly engaged across all aspects of space— delivering innovative solutions that span satellite, launch, ground, and cyber systems for defense, civil and commercial customers. When you join our team, you’ll be part of a special collection of problem solvers, thought leaders, and innovators. Join us and take your place in space.
At Aerospace, we are reimagining how we operate and deliver values to our customers using AI. It is an exciting time to join and help us shape our future. We are looking for a Senior MLOps Engineer to help us build and maintain the infrastructure, pipelines, and tooling to automate end-to-end ML lifecycle — from experimentation, training to deployment to monitoring. You will work with a small team of bright and experienced DevOps engineers to push the boundaries of ML platform. The selected candidate will have a broad knowledge of MLOps lifecycle and will work effectively with data scientist, ML engineers, DevOps engineers, to ensure ML systems secured, reliable, scalable, and reproducible in production.
The selected candidate will be required to work full-time, on-site at our facility in El Segundo, CA.
What You’ll Be Doing
Defining, designing, implementing, and operating a MLOps platform/framework
Design, build, and maintain end-to-end CI/CD/CT (Continuous Training) pipelines for model training, validation, deployment, and retraining.
Deploy ML models to production using containerized services (Docker, Kubernetes) and model-serving frameworks (TensorFlow Serving, TorchServe, Triton Inference Server, Seldon, or KServe).
Implement and maintain tools for experiment tracking, model versioning, and reproducibility (MLflow, Weights & Biases, DVC).
Build and maintain feature stores (Feast, Tecton, or custom solutions) to ensure consistent feature computation across training and inference.
Automate provisioning of ML infrastructure (compute, storage, GPU clusters) using Terraform, Pulumi, or CloudFormation across cloud environments.
Implement monitoring for model performance, data drift, concept drift, and prediction quality in production — triggering alerts and automated retraining when thresholds are breached.
Manage GPU/TPU resources for distributed training and high-throughput inference, optimizing for cost and performance.
Work closely with Data Engineering to ensure clean, reliable, and well-governed data pipelines feed into training and inference systems.
Establish model registries, lineage tracking, and audit trails to support compliance, reproducibility, and rollback capability.
Collaborate closely with Data Scientists, ML Engineers, and Platform/DevOps teams to translate experimental models into robust, production-grade systems.
Diagnose and resolve production issues related to model latency, throughput, resource contention, or pipeline failures
What You Need to be Successful
Minimum Requirements for the Technical and Cloud Computing Engineer Staff III:
Bachelor's degree in Computer Science, Data Science, Engineering, or a related field
3+ years of experience in MLOps, ML Engineering, DevOps, or a closely related role, with direct experience deploying ML models to production
Proficiency in Python, with strong software engineering fundamentals, including testing, version control, and code review practices
Hands-on experience with containerization (Docker) and orchestration (Kubernetes) for ML workloads
Experience with at least one major cloud platform (AWS, Azure, or GCP) and its ML/compute services
Familiarity with CI/CD tools (e.g., Jenkins, ArgoCD, GitHub Actions, GitLab CI) applied to ML pipelines
Experience with experiment tracking and model versioning tools (e.g., MLflow, DVC, or similar)
Solid understanding of ML fundamentals — including the…