Senior Engineering Manager - Scalable Machine Learning
Required skills
- Scalable Machine Learning
- MLOps
- distributed training
- data generation
- orchestration
- Python
- large volumes of data
- large-scale distributed systems
- cloud infrastructure
- machine learning
Nice to have
- Ray
- PyTorch
- Dagster
- LakeFS
- PyArrow
- multi-sensor data fusion
Role summary
Latitude AI is seeking a Senior Engineering Manager for their Scalable Machine Learning team. This role involves managing a team of software engineers focused on building and scaling machine learning infrastructure, including data generation, orchestration, and distributed training. The manager will set technical strategy, bridge gaps between infrastructure teams (ML Platform, Jobs Platform, Cloud Platform, HPC), and drive MLOps practices. The ideal candidate has extensive experience in distributed systems, ML infrastructure, and people management, with a strong track record of delivering complex software initiatives. Experience with computer vision and distributed ML frameworks is a plus.
Latitude AI (lat.ai) is building the future of Ford’s autonomy roadmap to make travel safer, less stressful, and more enjoyable for everyone. Bringing this vision to scale, our fully in-house developed hands-free ADAS platform will debut on the all-new Ford Fathom in 2027.
When you join the Latitude team, you’ll work alongside leading experts across machine learning and robotics, cloud platforms, mapping, sensors and compute systems, test operations, systems and safety engineering – all dedicated to redefining the relationship between people and their vehicles for millions of customers.
As a Ford Motor Company subsidiary, we operate independently to develop automated driving technology at the speed of a technology startup. Latitude is headquartered in Pittsburgh with engineering centers in Dearborn, Mich., and Palo Alto, Calif.
Meet the team:
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.
What you’ll do:
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.
What you'll need to succeed:
Nice to have (optional):
What we offer you:
Learn more about Latitude’s team, mission and career opportunities at lat.ai!
The expected base salary range for this full-time position in California is $253,120 - $379,680 USD. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. Latitude employees are also eligible to participate in Latitude’s annual bonus programs, equity compensation, and generous Company benefits program, subject to eligibility requirements.
Candidates for positions with Latitude AI must be legally authorized to work in the United States on a permanent basis. Verification of employment eligibility will be required at the time of hire. Visa sponsorship is available for this position.
We are an Equal Opportunity Employer committed to a culturally diverse workforce. All qualified applicants will receive consideration for employment without regard to race, religion, color, age, sex, national origin, sexual orientation, gender identity, disability status or protected veteran status.
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