Staff AI Engineer - SimAI Team
General MotorsAbout the role
Job Description
As an AI Engineer on the team, you will build and deploy applied AI/ML solutions that directly support simulation workflows, internal tools, and mission-critical engineering use cases. You will operate at the full spectrum of innovation—from early exploration and rapid prototyping to shipping robust, maintainable production systems.
You will collaborate closely with simulation, scenario-generation, data, and engineering teams to uncover opportunities where AI can create value —then deliver solutions that meet clear customer and business success criteria.
We are looking for a Staff AI Engineer with a strong applied research background. Ideal candidates have hands-on experience with state-of-the-art multimodal GenAI and/or 3D reconstruction models, and excel at building robust, high-performance inference pipelines. This role is not focused on training foundation models, but rather on fine‑tuning, adapting, optimizing, evaluating, and shipping them as production systems.
Minimum Qualifications
Master’s degree required, PhD preferred, in CS, Robotics, EE, or related fields.
8+ years of applied ML/AI engineering experience, with at least 3+ years delivering production AI systems at scale.
Deep knowledge of at least one modern multimodal or 3D computer vision pipeline (e.g., NeRF, Gaussian Splatting, world models, diffusion-based generation).
Hands-on experience building AI agents or tools and deploying machine learning models in production.
Strong programming skills in Python, along with experience using a modern ML/AI library such as PyTorch, TensorFlow, OpenAI APIs, HuggingFace, gsplat, etc.
Solid understanding of ML fundamentals including data preprocessing, training pipelines, evaluation metrics, and model performance optimization.
Ability to move fluidly between research exploration and practical engineering.
Demonstrated ability to lead projects end-to-end with minimal guidance.
Preferred Qualifications
Proven experience fine-tuning and optimizing foundation models (LLMs, VLMs, diffusion, 3D neural models).
Experience integrating models into simulation pipelines, robotics systems, mapping workflows, or AV perception stacks.
Experience with scene understanding, or computational geometry
Background in 3D geometry, graphics, or sensor modeling.
Proficiency with vector search/RAG systems and embedding pipelines.
Familiarity with GCP or other cloud
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