Jobs / United States / Latitude AI LLC
Senior Engineering Manager - Scalable Machine Learning
Latitude AI LLC · 🇺🇸 Dearborn, Michigan, United States, Palo Alto, California, United States, Pittsburgh, PA, United States, Remote · Remote
Sponsorship verdict
Sponsorship possible
One solid signal, not two — worth applying, and worth asking about sponsorship early.
- Employer is on a government sponsor recordThe US Department of Labor certified 38 H-1B/E-3 labor condition applications for this employer between Oct 2025 and Jun 2026 (latest Jun 2026) — the step every H-1B hire needs first. USCIS also records 1 H-1B approvals in FY2023. Source: LCA disclosure data (US Department of Labor (OFLC)).
- The posting doesn’t mention sponsorshipSilence isn’t a refusal — ask the recruiter before investing much time.
- No salary bar for this routeH-1B has no fixed salary bar: the employer must pay at least the prevailing wage for the role and area. Cap-subject employers enter a lottery weighted by wage level. Source: https://www.federalregister.gov/documents/2025/12/29/2025-23853/weighted-selection-process-for-registrants-and-petitioners-seeking-to-file-cap-subject-h-1b, rules effective 2026-02-27.
- What Latitude AI LLC paid sponsored hires in similar roles1 certified filing for “Software Engineering Manager II” (Computer and Information Systems Managers) in PA: $203k–$203k, median $203k. Most were filed at wage level IV (100%) — 4 lottery entries, ≈61% projected selection odds for cap-subject employers. Source: US Department of Labor LCA disclosure data (Oct 2025 – Jun 2026).
- Confirmed live todayWhen a source last listed this job as open.
US H-1B: cap-subject employers enter a lottery weighted by wage level — Level I gets 1 entry, Level IV gets 4 (DHS projected selection odds ≈15% at Level I to ≈61% at Level IV). Universities and non-profit research employers are cap-exempt. The $100,000 fee for new petitions from abroad is currently blocked by a court order (appeal pending).
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Sponsor Radar — Latitude AI LLC
The US Department of Labor certified 38 H-1B/E-3 labor condition applications for this employer between Oct 2025 and Jun 2026 (latest Jun 2026) — the step every H-1B hire needs first. USCIS also records 1 H-1B approvals in FY2023. Source: LCA disclosure data (US Department of Labor (OFLC)).
Past sponsorship or register membership never guarantees sponsorship for this vacancy or for you. Full Sponsor Radar for Latitude AI LLC →
About the role
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. • 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. What you'll need to succeed: • 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,