Senior Engineering Manager - Scalable Machine Learning

In the Intelligent Systems team, we convert photons into understanding, primarily via computer vision and machine learning. Our organization is responsible for all downstream tasks of the vehicle sensor suite onboard, as well as the full offboard/cloud infrastructure for machine learning at Latitude.

Scaling our machine learning models with data and fast infrastructure is vital to achieve optimal performance and to enable a safer and more sustainable future of transportation. The Scalable ML team builds the data generation, orchestration, and distributed training infrastructure that lets our ML developers work efficiently with large volumes of data, and it sits at the intersection of several infrastructure teams: ML Platform, Jobs Platform, Cloud Platform, and High Performance Computing.

In this role, you will manage the Scalable ML team while also setting the technical strategy for how our data and training infrastructure interoperates with those adjacent platform teams. This is a job for someone who can both run a high-performing engineering team and act as the technical bridge across organizations that do not report to them.

• →Lead and mentor a team of software engineers, driving high technical standards and continuous skill development for scalable machine learning infrastructure.
• →Define and own the multi-quarter technical strategy for data generation, orchestration, and distributed training, including where that strategy depends on or shapes the roadmaps of other teams.
• →Represent Scalable ML team in org-level planning and architecture decisions, resolving cross-team tradeoffs on shared infrastructure such as GPU scheduling and quota, job orchestration, storage, and compute cost.
• →Drive the implementation of advanced MLOps practices, including model artifact management, experiment tracking, and automated model deployment and testing strategies, at a standard that other teams adopt.
• →Build and maintain durable technical partnerships with peer managers and architects on ML Platform, Jobs Platform, Cloud Platform, and HPC teams to design and review shared-infrastructure changes jointly.
• →Hold the team and its systems to a high reliability bar, including how incidents that cross team boundaries get root-caused, fixed, and prevented.

• A Bachelor's and 10 years experience or Master's Degree and 8 years experience in Computer Engineering, Computer Science, Electrical Engineering, Robotics, or a related field.
• Minimum of 10 years of experience in software development, with at least 5 years in a Staff/Principal engineering or senior technical leadership role.
• A track record of setting technical strategy and direction across multiple teams or organizations, not just within a single team.
• Extensive experience designing, building, and operating large-scale distributed systems and cloud infrastructure for machine learning, including direct experience with the kind of cross-team dependencies that come from shared compute (GPU scheduling/quota, job orchestration, multi-tenant clusters).
• Proven expertise in developing and optimizing machine learning data pipelines, including data generation, management, and distributed training.
• Strong proficiency in Python for developing high-performance, production-quality software.
• Demonstrated ability to influence and align peer teams and leaders without direct authority over them.
• Track record of successfully delivering complex, multi-org software initiatives from conception to production, emphasizing scalability, reliability, and organizational alignment.

• Master's or PhD in Computer Science, Electrical Engineering, Robotics, or a closely related field.
• Hands-on experience with distributed machine learning frameworks and tools such as Ray, PyTorch, Dagster, LakeFS, or PyArrow.
• Experience with multi-sensor data fusion and processing techniques relevant to autonomous driving applications.
• Familiarity with safety-critical software development principles in automotive or robotics domains.
• Deep understanding of machine learning concepts, computer vision use cases, and the challenges of L2/L3 autonomy.