Senior Software Engineer - ML Infrastructure
Latitude AI4 hours ago
Base Salary
$179k - $269k/yr
Responsibilities
- Build pipelines to discover, label, organize, version, and curate training and evaluation datasets from real-world and simulated driving data.
- Design scalable and reproducible data pipelines with provenance, data contracts, and train/validation/holdout splits.
- Build training, inference, evaluation, checkpointing, and experiment-tracking infrastructure at scale.
- Train, tune, productionize, and integrate classical and deep learning models into automated analytics pipelines.
- Define and validate model and release metrics, including precision, recall, F1, ROC AUC, calibration quality, and task-specific measures.
- Run baselines, ablations, cross-validation, holdout comparisons, uncertainty analysis, and error analysis.
- Develop dashboards and services for comparing software versions, exploring scenario-level results, visualizing model outputs, and collecting reviewer feedback.
- Improve software quality through clean interfaces, testing, type checking, code review, and dependable CI.
- Collaborate with autonomy, simulation, data, and infrastructure teams to take 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 a master’s degree plus 2+ years of relevant experience.
- Strong Python software engineering skills and experience designing modular, maintainable, and well-tested systems in a shared codebase.
- Experience building end-to-end ML systems covering data pipelines, dataset curation, training, evaluation, and production using tools such as SQL, pandas, PyTorch, or scikit-learn.
- Experience designing scalable ML infrastructure, including training and evaluation pipelines, experiment tracking, model serving, or distributed jobs.
- Solid grounding in statistics and experimental design, including evaluation strategies, metrics, and decision thresholds.
- Clear communication and ability to work across software, ML, and autonomy teams.
- Preferred experience with probabilistic forecasting, sequential or graph-based models, gradient boosting, calibration, uncertainty estimation, ablation studies, error analysis, release comparisons, active learning, auto-labeling, embedding-based search, or dataset versioning.
- Preferred experience with Dagster, Airflow, Ray, Weights & Biases, Dash, Streamlit, Bazel, or other large-scale build systems.
- Autonomous-driving or robotics experience is helpful but not required.
Benefits
- Competitive compensation packages, annual bonus eligibility, and equity compensation.
- Medical, dental, and vision insurance, including family coverage, plus a health savings account with available employer match.
- Employer-matched 401(k) with immediate vesting and employer-paid group term life insurance.
- Paid parental leave, adoption/surrogacy support, paid medical leave, unlimited vacation, and 15 paid holidays.
- Daily lunches, snacks, and beverages at office locations, plus pre-tax healthcare, dependent-care, and commuter benefits.
- Monthly wellness stipend, backup child and elder care, professional development reimbursement, and an employee assistance program.
- Discounted legal services, identity theft protection, pet insurance, employee resource groups, quarterly team activity stipends, and wellness initiatives.
- Full-time position based in California with a stated base salary range of $179,200–$268,800; employment requires permanent US work authorization, and visa sponsorship is available.
Categories
Data EngineeringML Engineering