Senior Software Engineer - ML Infrastructure
$179k - $269k • Pittsburgh, PA, Palo Alto, CA, Detroit, MI • Full-time
Posted 10d ago
Job Location
Pittsburgh, PA, Palo Alto, CA, Detroit, MI
Tech Stack
Remote Work Policy
On-site
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 operate with the agility of a startup while building a hands-free ADAS platform for millions of customers. The Performance Prediction team plays a crucial role by building data pipelines, machine learning infrastructure, and evaluation systems. Our work transforms large-scale driving data into actionable insights, enabling teams across Latitude to confidently advance autonomy development. We manage the full ML lifecycle, from curating high-quality datasets and discovering representative scenarios to training and deploying various learned models, including classical and deep learning approaches. We also develop essential metrics, experiment tracking tools, and internal dashboards that empower engineers throughout the autonomy division.
Responsibilities
- Curate datasets by building pipelines to discover, label, and organize driving scenarios from real-world and simulated data into balanced, high-quality training and evaluation sets.
- Build and maintain scalable, reproducible data pipelines with versioning, provenance, well-designed splits, and clear data contracts.
- Analyze dataset composition, coverage, and label quality to characterize behavior evolution across releases and scenario types, sharing findings to shape priorities.
- Scale ML infrastructure by building robust training, inference, and evaluation pipelines with efficient data loading, checkpointing, and experiment tracking.
- Develop, tune, and productionize learned models, from classical approaches to deep learning, integrating them into automated analytics pipelines.
- Define, implement, and validate model and release metrics with strong statistical rigor.
- Set up and run experiments, including baselines, ablations, cross-validation, and holdout comparisons, quantifying uncertainty and communicating results.
- Develop dashboards and services to facilitate software version comparison, scenario-level exploration, model output visualization, and feedback gathering.
- Improve engineering practices by designing clean interfaces, championing testing, type checking, code review, and fast CI.
- Collaborate with autonomy, simulation, data, and infrastructure teams to move ideas from prototype to production.
Requirements
- Bachelor's degree in Computer Science, Computer Engineering, Electrical Engineering, Robotics, or a related field, plus 4+ years of relevant industry experience (or Master's degree and 2+ years of relevant experience).
- Strong Python software engineering skills, including designing modular, maintainable, well-tested systems.
- Experience building end-to-end ML systems, covering data pipelines, dataset curation, training, evaluation, and production, using tools like SQL, pandas, PyTorch, or scikit-learn.
- Experience designing scalable ML infrastructure, including training/evaluation pipelines, experiment tracking, model serving, or distributed jobs.
- Solid understanding of statistics and experimental design, including selecting appropriate evaluation strategies, metrics, and thresholds for real-world decisions.
- Clear communication skills and ability to work effectively across software, ML, and autonomy teams.
Benefits
- Competitive compensation packages
- High-quality individual and family medical, dental, and vision insurance
- Health savings account with available employer match
- Employer-matched 401(k) retirement plan with immediate vesting