Staff Machine Learning Engineer

This is a fully remote position, open to applicants in United States.

📋 Description

• Define Technical Strategy & Roadmaps: Create and implement comprehensive, multi-quarter technical roadmaps for core machine learning systems.

• Architect System-Level Solutions: Take ownership of the system architecture for intricate ML products. Design scalable frameworks to facilitate extensive data mining and optimize real-time inference across GPU/CPU clusters.

• Drive Cross-Functional Execution: Lead collaborative projects to successful completion across various teams. Influence the technical roadmaps of partner teams (such as Autonomy) to address common challenges, eliminate silos, and foster alignment.

• Elevate Engineering Excellence: Set organization-wide standards for ML system architecture, code quality, testing, and deployment.

• Operate as a Generalist Expert: Utilize a diverse set of ML techniques to tackle complex and ambiguous challenges.

• Mentor and Lead: Serve as a role model and go-to technical expert. Guide both Senior and junior engineers, lead architectural assessments, and enhance Motional’s engineering culture.

⛳️ Requirements

• Bachelor's degree in Computer Science, Machine Learning, or a related field (or equivalent practical experience).

• Over 8 years of practical ML engineering experience, demonstrating a successful track record in architecture, deployment, and optimization of large-scale ML systems.

• Proven experience with multimodal foundation models in ML production systems, including the integration, scaling, fine-tuning, or deployment of models that handle multiple data modalities (e.g., camera, LiDAR, radar, text).

• Demonstrated technical leadership in defining multi-quarter roadmaps, leading multi-person projects, and driving technical strategy at the departmental level.

• Expert-level knowledge in Python and ML frameworks (PyTorch, TensorFlow, or JAX), supported by strong software engineering fundamentals (system design, CI/CD, containerization).

• Comprehensive ML generalist knowledge, with hands-on experience in model training, deep learning architectures, evaluation methodologies, and production deployment at scale.

• Experience in deploying ML models in cloud environments (AWS, GCP, or Azure) while optimizing for latency, throughput, and hardware efficiency.

• Proven capability to mentor colleagues, articulate complex trade-offs to leadership, and build consensus among diverse teams.

🏝️ Benefits

• Medical

• Dental

• Vision

• 401k with a company match

• Health saving accounts

• Life insurance

• Pet insurance

• Flexible work arrangements