A/AI Machine Learning Engineering Mgr - L5

Standard Job Description

The Lockheed Martin Chief Digital and AI Office (CDAO) is seeking a leader for the Applied AI Mission Integration team, leading STAR.Data. STAR.Data is a solution to DoW’s data distribution and management challenges, especially when operating in contested and bandwidth challenged environmentsand; it is also part of the STAR.OS ecosystem of capabilities. The STAR.Data team works in agile and faced paced environment, across all LM Business Areas and major corporate initiatives. The selected candidate will oversee deploying STAR.Data as a point solution that address specific, high impact use cases, as well as a fully integrated software stack that enables seamless data and function sharing across domains.

Key responsibilities include:
• Leadership of the STAR.Data product and team
• Development and Maturation of STAR.Data product lines for deployment into mission applications
• Close coordination with LM Business Areas and external customers to integrate trusted AI solutions to meet mission requirements, with a focus on proliferation of STAR.Data
• Cultivating a talent pipeline in coordination with the LM Business Areas to accelerate technology transition

Basic Qualifications

• Bachelor’s Degree in Engineering, Computer Science, or other related discipline
• Experience building and deploying software for defense mission systems; experience with database solutions
• Demonstrated functional and technical leadership experience in directing high-performing projects
• Ability to effectively communicate technical concepts to both technical and non-technical stakeholders, with a strong focus on collaboration and teamwork.
• Experience with one of the following: C++, Java, GO, Rust and other compiled languages
• Experience with DevOps tools: Docker, Git [GitLab, GitHub], Continuous Integration [CI], Continuous Deployment [CD]

Desired Skills

• Master’s Degree in Engineering, Computer Science, or other related discipline
• Experience with software orchestration (e.g. Kubernetes, Helm, Istio/Linkerd) and modern GitOps workflows
• Knowledge of machine learning frameworks (Pytorch, Tensorflow, MxNet, Caffe, etc.)
• Experience developing microservices and microservice architectures
• Experience with real-time embedded software engineering
• Experience with hardware-accelerated computing for both CPUs and GPUs (e.g. CUDA, Intel oneAPI, SYCL)
• Experience with model deployment software libraries and stacks (e.g. NVIDIA TensorRT, Triton Inference Server, OnnxRuntime)
• Experience deploying AI-enabled systems in multiple environments (e.g. cloud, edge devices)