Staff Machine Learning Engineer

Motional•Pittsburgh, PA

1d•Hybrid

About The Position

At Motional, we're transforming how autonomous vehicles discover critical intelligence hidden within petabytes of multimodal sensor data. Our next-generation autonomous driving stack depends on finding the rare edge cases, long-tail scenarios, and model errors that matter most. Omnitag, our ML-powered multimodal data mining framework, is the engine that powers this discovery. As a Staff Machine Learning Engineer, you will serve as a technical leader defining the roadmap and architecture for the machine learning systems that power our data discovery and model improvement lifecycles. Rather than focusing on a single specialized domain, you will leverage your broad ML expertise to architect massive, scalable systems, from multimodal representation learning and active learning loops to hyper-efficient production inference. You will own system-level architecture, lead multi-quarter, multi-person initiatives, and partner across the engineering organization to unblock teams and influence our department-wide technical strategy. By establishing robust processes and mentoring those around you, you will ensure our ML platforms act as a reliable, mission-critical engine for the entire autonomy stack.

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

Nice To Haves

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.

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.