Senior Software Engineer - Performance Prediction

Latitude AI•Palo Alto, CA

11h

About The Position

Latitude AI (lat.ai) develops automated driving technologies, including L3, for Ford vehicles at scale. We’re driven by the opportunity to reimagine what it’s like to drive and make travel safer, less stressful, and more enjoyable for everyone. 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 making a real, positive impact on the driving experience for millions of people. 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: The Performance Prediction team builds the Machine Learning models, evaluation pipelines, and internal tools that help us understand how autonomy behavior changes across software releases. We work on problems that span behavior classification, ride quality detection, probabilistic trajectory prediction, and release regression analysis. Our systems support both classical and modern ML approaches. That includes compact learned classifiers such as tree-based models for behavior and ride quality detection, as well as deep learning-based probabilistic prediction models for more complex autonomy tasks. We also build the software around those models: dataset definition, feature generation, training and tuning workflows, offline metrics, experiment tracking, and tools that help engineers inspect regressions at the slice and scenario level. This role is a strong fit for someone who enjoys building reliable Python systems and applying rigorous ML evaluation methods in a safety-critical domain. In practice, the work is a mix of ML systems development, model and evaluation work, and internal tooling used by partner teams across autonomy.

Requirements

Bachelor's degree in Computer Engineering, Computer Science, Electrical Engineering, Robotics or a related field and 4+ years of relevant experience (or Master's degree and 2+ years of relevant experience, or PhD)
Strong software engineering skills in Python, including experience building modular, maintainable, well-tested systems in a shared codebase
Experience developing, training, tuning, or productionizing supervised ML models
Strong grounding in statistics and experimental design, including experience designing model training and evaluation tests
Experience selecting and interpreting model metrics, thresholds, and tradeoffs for real-world decision-making
Experience with ML tooling such as PyTorch, scikit-learn, or similar frameworks
Experience working with large datasets using SQL, pandas, and NumPy
Strong communication skills and the ability to work effectively across software, ML, and autonomy teams

Nice To Haves

PhD in Computer Science, Machine Learning, Statistics, Robotics, or a closely related field is preferred
Equivalent research-heavy industry experience is highly valued
Experience with probabilistic forecasting, trajectory prediction, sequential modeling, or graph-based models
Experience with classical ML methods such as random forests, gradient boosting, or calibrated linear models
Experience with calibration, uncertainty estimation, ablation studies, error analysis, or release regression methodology
Experience building internal analytics or ML tools with Dash, Plotly, Streamlit, or similar frameworks
Experience with workflow orchestration or experimentation tools such as Dagster, Airflow, or Weights and Biases
Experience with Bazel or other large-scale build systems
Prior autonomous driving experience is helpful but not required. Strong experience with production ML systems and rigorous model evaluation is sufficient

Responsibilities

Build production software for model training, offline evaluation, and release-comparison workflows
Develop and improve learned models for performance prediction, including behavior classifiers and probabilistic prediction models
Design training, validation, and holdout strategies that produce trustworthy results
Define and track model and release metrics such as precision, recall, F1, ROC AUC, calibration quality, and task-specific forecasting metrics
Run experiments, tune models, and analyze results with strong statistical rigor
Build internal tools that help engineers compare software versions, inspect model outputs, and investigate regressions
Partner with autonomy, simulation, and infrastructure teams to move ideas from prototype to production
Raise engineering quality through testing, code review, CI, and maintainable interfaces across data, modeling, and product layers

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
Employer-paid group term life insurance and the option to elect voluntary life insurance
Paid parental leave
Paid medical leave
Unlimited vacation
15 paid holidays
Daily lunches, snacks, and beverages available in all office locations
Pre-tax spending accounts for healthcare and dependent care expenses
Pre-tax commuter benefits
Monthly wellness stipend
Adoption/Surrogacy support program
Backup child and elder care program
Professional development reimbursement
Employee assistance program
Discounted programs that include legal services, identity theft protection, pet insurance, and more
Company and team bonding outlets: employee resource groups, quarterly team activity stipend, and wellness initiatives