Senior Software Engineer - ML Infrastructure

Latitude AI•Detroit, MI

12h

About The Position

Latitude AI is hiring a Senior Software Engineer for its Performance Prediction team, which builds data pipelines, machine learning infrastructure, and evaluation systems to track 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 learned models, and creating metrics, experiment tracking, and tools used by autonomy engineers. The role combines data and ML infrastructure, model development and evaluation, and internal tools for partner teams. The engineer will build reliable, well-tested Python systems that produce trustworthy ML results. The work includes organizing driving scenarios into high-quality datasets; developing scalable, reproducible pipelines with versioning and provenance; analyzing dataset coverage and label quality; scaling model training, inference, and evaluation; defining and validating metrics; conducting rigorous experiments; and building dashboards and services for reviewing results. The role also involves improving engineering practices and collaborating with autonomy, simulation, data, and infrastructure teams to bring ideas from prototype to production.

Requirements

Bachelor's degree in Computer Science, Computer Engineering, Electrical Engineering, Robotics, or a related field and 4+ years of relevant industry experience, or 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.

Responsibilities

Curate balanced, high-quality training and evaluation datasets from real-world and simulated driving scenarios.
Design and maintain scalable, reproducible data pipelines with versioning, provenance, train/validation/holdout splits, and clear data contracts.
Explore dataset composition, coverage, and label quality, and characterize behavior across software releases and scenario types.
Build scalable training, inference, and evaluation pipelines, including 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.
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, and champion testing, type checking, code review, and dependable CI.
Collaborate with autonomy, simulation, data, and infrastructure teams to take ideas from prototype to production.

Benefits

Competitive compensation packages.
Medical, dental, and vision insurance for individuals and families.
Health savings account with available employer match.
Employer-matched 401(k) retirement plan with immediate vesting.
Employer-paid group term life insurance and optional 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.