Staff Machine Learning Engineer

Motional is a driverless technology company focused on making autonomous vehicles a safe, reliable, and accessible reality. As a Staff Machine Learning Engineer, you will be a technical leader responsible for defining the roadmap and architecture for machine learning systems that enhance data discovery and model improvement lifecycles.

Responsibilities:

• Define Technical Strategy & Roadmaps: Develop and execute multi-quarter, high-impact technical roadmaps for core ML systems. Proactively inform leadership to guide reprioritization, ensuring initiatives consistently drive team-wide and department-level OKRs and KPIs
• Architect System-Level Solutions: Own the system-level architecture for complex ML products. Design scalable frameworks for massive data mining and highly optimized, real-time inference across GPU/CPU clusters
• Drive Cross-Functional Execution: Lead multi-person projects to completion across teams. Influence partner teams' technical roadmaps (such as Autonomy) to solve shared problems, break down silos, and build alignment
• Elevate Engineering Excellence: Establish department-wide standards for ML system design, code quality, testing, and deployment. Deliver processes to proactively address issues and participate in org-wide incident response planning
• Operate as a Generalist Expert: Apply a broad toolkit of ML techniques (deep learning, representation learning, active learning, generative AI) to solve complex, ambiguous problems. Unblock yourself and your team when facing unprecedented technical challenges
• Mentor and Lead: Act as a role model and technical go-to person. Coach Senior and junior engineers, lead architectural reviews, and elevate Motional’s engineering culture through internal documentation, tech talks, and collaborative design

Requirements:

• BS in Computer Science, Machine Learning, or a related field (or equivalent practical experience)
• 8+ years of hands-on ML engineering experience, with a proven track record of owning architecture, deployment, and optimization of large-scale ML systems
• Demonstrated experience working with multimodal foundation models in ML production systems, including integration, scaling, fine-tuning, or deployment of models that process multiple data modalities (e.g., camera, LiDAR, radar, text)
• Demonstrated technical leadership: defining multi-quarter roadmaps, leading multi-person initiatives, and driving department-level technical strategy
• Expert-level proficiency in Python and ML frameworks (PyTorch, TensorFlow, or JAX), backed by strong software engineering fundamentals (system design, CI/CD, containerization)
• Broad ML generalist knowledge, with practical experience spanning model training, deep learning architectures, evaluation methodologies, and production deployment at scale
• Experience deploying ML models in cloud environments (AWS, GCP, or Azure) and optimizing for latency, throughput, and hardware efficiency
• Proven ability to mentor peers, explain complex trade-offs to leadership, and drive consensus across disparate teams
• MS/PhD in Computer Science, Machine Learning, or a related field
• Background in autonomous driving, robotics, or complex real-time decision-making systems
• Experience with massive-scale ML data mining, active learning loops, and contrastive/representation learning
• Familiarity with multimodal learning, sensor fusion, or large foundation models
• Deep knowledge of model serving tools (TF Serving, Triton, TorchServe) and enterprise MLOps platforms
• Demonstrated experience leading org-wide severity reviews or establishing incident response planning for mission-critical ML platforms