Senior Manager, Machine Learning Operations (Perception)

Exol•Wilmington, MA

4d•Onsite

About The Position

Symbotic is seeking an experienced leader to join their team as the Senior Manager, Machine Learning Operations (ML Ops) within the Perception team. This role is responsible for the development, scaling, and reliability of the ML lifecycle that supports robotic perception systems. The position involves overseeing data pipelines, deployment infrastructure, and monitoring systems. The Senior Manager will collaborate closely with perception algorithm and controls teams to ensure the robust and scalable performance of models across a large fleet of robots and various warehouse environments.

Requirements

Bachelor’s Degree in Computer Science, Computer Engineering, Robotics, or a related field; Master’s Degree preferred or equivalent work experience.
Minimum of 10+ years of experience in software engineering, machine learning systems, or infrastructure development.
Minimum of 3+ years of experience managing or leading engineering teams in ML, data, or infrastructure domains.
Proficiency in Python and/or C++.
Experience with distributed systems and cloud platforms (e.g., GCP).
Experience with containerization (Kubernetes).
Experience with data/streaming technologies (Kafka, Snowflake).
Strong cross-functional leadership, execution focus, communication skills, and the ability to drive process maturity in complex, fast-paced engineering environments.

Nice To Haves

Experience building and scaling ML Ops platforms, including data pipelines, model deployment systems, and monitoring and observability tools.
Experience with analytics and visualization tools (e.g., Tableau).

Responsibilities

Drives the development and scaling of ML Ops infrastructure, including data pipelines, model training and validation workflows, deployment systems, and monitoring frameworks.
Coordinates cross-functional efforts with perception, controls, and platform teams to ensure seamless integration of ML models into production robotic systems.
Leads end-to-end ML lifecycle processes, including dataset management, model versioning, release processes, and production validation across distributed fleets.
Manages and mentors a team of ML and data engineers, fostering a high-performing, collaborative, and execution-focused team environment.
Ensures high-quality, reliable delivery of ML-enabled functionality by establishing best practices in reproducibility, observability, and operational excellence.
Drives continuous improvement of monitoring systems, including software and hardware telemetry, to support system performance, diagnostics, and maintenance operations.