AI Research Engineer (hybrid, junior career)

Lockheed Martin•Fort Worth, CA

1d•Hybrid

About The Position

The Lockheed Martin Artificial Intelligence Center (LAIC) team is seeking a highly skilled and motivated individual to support the Applied AI team and specifically the Cognitive Modules portfolio. As a key member of our team, you'll have the opportunity to work on a wide range of projects, from prototypes to deployment, and contribute to the development of innovative solutions that provide discriminating capability to our customers.

Requirements

Bachelor’s Degree in Engineering, Computer Science, or other related discipline.
Experience with machine learning algorithms, in particular computer vision
Experience with a deep learning library, such as PyTorch, Torch, Keras, Tensorflow, Scikit-learn (sklearn), or opencv
Experience with python
Must be a US Citizen; this position will require a government security clearance.
This position is located at a facility that requires special access.

Nice To Haves

Experience designing and working with tactical weapon, vehicle, and/or mission systems software
Experience writing custom and novel machine learning architectures
Work with real-world, dynamic datasets
Experience building and deploying software for defense mission systems
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)
Experience with high-performance computing

Responsibilities

Developing software products that implement Artificial Intelligence to provide cutting-edge solutions to our customers
Engaging in every phase of the system development lifecycle, including requirements generation, system and software design, implementation, integration, and flight test
Participating in activities such as proof of concepts, development of prototypes, building demonstrators, and transition to production
Leveraging current computer science paradigms, including object-oriented programming, data structures and algorithms, concurrency, and I/O (block storage network database)
Developing code to solve complex problems and field innovative solutions