Senior Software Engineer - ML Infrastructure

Latitude AI•Palo Alto, CA

1d•$179,200 - $268,800

About The Position

Latitude AI is building Ford’s autonomy roadmap and developing an in-house hands-free advanced driver assistance platform. The Performance Prediction team builds data pipelines, machine learning infrastructure, and evaluation systems that help assess how autonomy behavior changes between software releases. The team works across the ML lifecycle, including curating real-world and simulated driving data, training and shipping models, and building metrics, experiment-tracking systems, and tools used across autonomy teams. This role suits a software engineer who builds reliable, well-tested Python systems and values trustworthy ML results. Work combines data and ML infrastructure, model development and evaluation, and internal tools for partner teams. The position collaborates with autonomy, simulation, data, and infrastructure teams to bring ideas from prototype to production. The expected base salary range for this full-time position in California is $179,200–$268,800 USD. Actual starting pay depends on job-related factors, including work location, experience, training, education, and skill level. Employees may also be eligible for annual bonus programs, equity compensation, and company benefits, subject to eligibility requirements. Candidates must be legally authorized to work permanently in the United States; employment eligibility will be verified at hire. Visa sponsorship is available.

Requirements

Bachelor’s degree in Computer Science, Computer Engineering, Electrical Engineering, Robotics, or a related field and 4+ years of relevant industry experience; alternatively, a master’s degree and 2+ years of relevant experience.
Strong Python software engineering skills, including designing modular, maintainable, well-tested systems in a shared codebase.
Experience building end-to-end ML systems, from data pipelines and dataset curation through training, evaluation, and production, using tools such as SQL, pandas, PyTorch, or scikit-learn.
Experience designing scalable ML infrastructure, such as training and evaluation pipelines, experiment tracking, model serving, or distributed jobs.
Solid grounding in statistics and experimental design, including selecting evaluation strategies, metrics, and thresholds for real-world decisions.
Clear communication skills and ability to work effectively across software, ML, and autonomy teams.

Nice To Haves

Experience with probabilistic forecasting, sequential or graph-based models, or classical methods such as gradient boosting.
Experience with calibration, uncertainty estimation, ablation studies, error analysis, or release-over-release comparison.
Experience with data-centric ML, such as active learning, auto-labeling, embedding-based search, or dataset versioning.
Experience with orchestration, experimentation, and internal tooling such as Dagster, Airflow, Ray, Weights & Biases, Dash, or Streamlit.
Experience with Bazel or other large-scale build systems.
Autonomous driving or robotics experience; helpful but not required.

Responsibilities

Build pipelines to discover, label, and organize driving scenarios from real-world and simulated data into balanced training and evaluation datasets.
Design and maintain scalable, reproducible data pipelines with versioning, provenance, appropriate dataset splits, and clear data contracts.
Analyze dataset composition, coverage, and label quality; characterize behavior across releases and scenario types; and share findings that inform model, dataset, and autonomy priorities.
Build scalable training, inference, and evaluation pipelines with robust data loading, checkpointing, and experiment tracking.
Train, tune, and productionize learned models ranging from classical methods to deep learning, and integrate them into automated analytics pipelines.
Define, implement, and validate model and release metrics, including precision, recall, F1, ROC AUC, calibration quality, and task-specific metrics.
Set up baselines, ablations, cross-validation, and holdout comparisons; quantify uncertainty; and communicate results to support decisions.
Develop dashboards and services for comparing software versions, exploring scenario-level results, visualizing model outputs, and gathering reviewer feedback.
Design clean interfaces between data, modeling, and product layers; promote testing, type checking, code review, and dependable continuous integration.
Collaborate with autonomy, simulation, data, and infrastructure teams to take ideas from prototype to production.

Benefits

Competitive compensation packages.
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 option to elect 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 at office locations.
Pre-tax healthcare and dependent-care spending accounts.
Pre-tax commuter benefits.
Monthly wellness stipend.
Backup child and elder care program.
Professional development reimbursement.
Employee assistance program.
Discounted programs including legal services, identity theft protection, and pet insurance.
Employee resource groups, quarterly team activity stipend, and wellness initiatives.
Annual bonus programs and equity compensation, subject to eligibility requirements.