Senior Engineering Manager - Scalable Machine Learning
$253k - $380k • Remote • Dearborn, Michigan, United States, Palo Alto, California, United States, Pittsburgh, PA, United States, Remote • Full-time
Posted 25d ago
Job Location
Dearborn, Michigan, United States, Palo Alto, California, United States, Pittsburgh, PA, United States, Remote
Tech Stack
Remote Work Policy
Fully remote
Employment Type
Full-time
Categories
Machine Learning Engineer
About the job
Latitude AI, a subsidiary of Ford Motor Company, is developing the future of autonomous driving technology. We are building an in-house developed, hands-free ADAS platform that will debut in 2027. Our team comprises experts in machine learning, robotics, cloud platforms, and more, all dedicated to making transportation safer and more enjoyable. We operate with the agility of a tech startup while leveraging the resources of Ford. The Intelligent Systems team focuses on converting sensor data into understanding through computer vision and machine learning, managing both onboard sensor suites and offboard cloud infrastructure. Scaling our machine learning models with robust data and fast infrastructure is crucial for achieving optimal performance and enabling a safer future of transportation. The Scalable ML team is responsible for the data generation, orchestration, and distributed training infrastructure that empowers our ML developers to work efficiently with large datasets, bridging the gap between ML Platform, Jobs Platform, Cloud Platform, and High Performance Computing.
Responsibilities
- Lead and mentor a team of software engineers, ensuring high technical standards and continuous skill development in scalable machine learning infrastructure.
- Define and own the multi-quarter technical strategy for data generation, orchestration, and distributed training, influencing roadmaps of adjacent teams.
- Represent the Scalable ML team in org-level planning and architecture decisions, resolving cross-team tradeoffs on shared infrastructure like GPU scheduling, job orchestration, storage, and compute cost.
- Drive the implementation of advanced MLOps practices, including model artifact management, experiment tracking, and automated deployment and testing.
- Build and maintain durable technical partnerships with peer managers and architects on ML Platform, Jobs Platform, Cloud Platform, and HPC teams.
- Ensure high reliability for the team and its systems, including root-causing, fixing, and preventing incidents that cross team boundaries.
Requirements
- Bachelor's degree and 10 years of experience, or Master's degree and 8 years of 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.
- Proven track record of setting technical strategy and direction across multiple teams or organizations.
- Extensive experience designing, building, and operating large-scale distributed systems and cloud infrastructure for machine learning, including managing cross-team dependencies on shared compute.
- 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.
- Track record of successfully delivering complex, multi-org software initiatives from conception to production.
Benefits
- Competitive compensation packages
- High-quality individual and family medical, dental, and vision insurance
- Health savings account with employer match
- Employer-matched 401(k) retirement plan with immediate vesting
- Employer-paid group term life insurance and option for voluntary life insurance
- Paid parental leave and Adoption/Surrogacy support program
- Paid medical leave
- Unlimited vacation and 15 paid holidays
- Daily lunches, snacks, and beverages in office locations
- Pre-tax spending accounts for healthcare and dependent care expenses
- Pre-tax commuter benefits
- Monthly wellness stipend
- Backup child and elder care program
- Professional development reimbursement
- Employee assistance program
- Discounted programs (legal services, identity theft protection, pet insurance, etc.)