Senior Manager, Machine Learning Operations (Perception)

Symbotic LLC; Wilmington (MA); On-site; USD 180,000 - 247,500; Full time

Benefits offered by this job

Medical insurance

Dental insurance

Vision insurance

401K

PTO

Job summary

Symbotic LLC, located in Wilmington, Massachusetts, seeks a seasoned leader to drive the development and scaling of ML Ops infrastructure. The ideal candidate will oversee teams and ensure the integration of ML models into robotic systems while managing the end-to-end ML lifecycle.

The position necessitates a Bachelor’s degree in a relevant field and over 10 years of experience in software engineering or ML systems. The role offers a competitive compensation package including various benefits.

Qualifications

• Requires a Bachelor’s Degree in a related field.
• Minimum 10+ years of experience in software engineering or machine learning systems.
• Minimum 3+ years managing engineering teams.

Responsibilities

• Drive development and scaling of ML Ops infrastructure.
• Coordinate efforts with perception, controls, and platform teams.
• Lead end-to-end ML lifecycle processes.

Skills

Python

C++

Machine Learning

Data Pipelines

Kubernetes

Kafka

Snowflake

Tableau

Education

Bachelor’s Degree in Computer Science, Computer Engineering, Robotics or related field

Master’s Degree preferred or equivalent work experience

Job description

What you’ll do

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

What you’ll need

• Requires a 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.
• Experience building and scaling ML Ops platforms, including data pipelines, model deployment systems, and monitoring and observability tools.
• Proficiency in Python and/or C++; experience with distributed systems and cloud platforms (e.g., GCP), containerization (Kubernetes), and data/streaming technologies (Kafka, Snowflake).
• Experience with analytics and visualization tools (e.g., Tableau) is preferred.
• Strong cross‑functional leadership, execution focus, communication skills, and the ability to drive process maturity in complex, fast‑paced engineering environments.

Our environment

• Up to 10% travel may be required; employees must have a valid driver’s license and the ability to drive and/or fly to client and other customer locations.
• Employees are responsible for owning a credit card and managing expenses personally to be reimbursed on a bi-weekly basis.

The base range for this position in the posted location is $180,000.00 - $247,500.00; however, base pay offered may vary depending on job‑related knowledge, skills, and experience.

The compensation package includes medical, dental, vision, disability, 401K, PTO and/or other benefits.

We do not discriminate based on race, color, ethnicity, ancestry, religion, sex, national origin, sexual orientation, age, citizenship status, marital status, disability, gender identity, gender expression, veteran status, or genetic information.