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Staff Machine Learning Engineer
remoterocketship
- Remote, Oregon, United States
- Remote, Oregon, United States
Über
Define Technical Strategy & Roadmaps: Develop and execute multi-quarter, high-impact technical roadmaps for core ML systems. 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. Operate as a Generalist Expert: Apply a broad toolkit of ML techniques to solve complex, ambiguous problems. 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. 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 Benefits:
Medical Dental Vision 401k with a company match Health saving accounts Life insurance Pet insurance Flexible work arrangements
Sprachkenntnisse
- English
Hinweis für Nutzer
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