About the TeamScaling our machine learning models with data and fast infrastructure is central to how Gatik's autonomy stack keeps improving. The Scalable ML team builds the data generation, curation, orchestration, and distributed training infrastructure that lets our ML developers work efficiently with large volumes of real-world fleet data and simulated driving scenarios. The team sits at the intersection of ML Platform, Jobs Platform, Cloud Platform, and High Performance Computing, and partners closely with Gatik's autonomy, simulation, safety, and data teams.
About the RoleAs a Senior or Staff engineer on Scalable ML, you'll design and build the systems that turn raw driving data from Gatik's fleet into high-quality training and evaluation sets, run training and evaluation reliably at scale, and give engineers the metrics and tools they need to make confident release decisions for driverless operations. At the Staff level, you'll also set multi-quarter technical direction and act as a technical bridge across teams that don't report to you.
What You'll Do- Build scalable data pipelines. Design and maintain reproducible pipelines that discover, label, and organize driving scenarios from Gatik's fleet and simulation, with versioning, provenance, well-designed train/validation/holdout splits, and clear data contracts.
- Curate high-quality datasets. Build balanced training and evaluation sets that cover the scenarios our trucks encounter across routes, weather, and traffic conditions, and analyze coverage, composition, and label quality to shape dataset and model priorities.
- Scale training and evaluation infrastructure. Build distributed training, inference, and evaluation pipelines with robust data loading, checkpointing, and experiment tracking that run smoothly on multi-tenant GPU clusters.
- Drive MLOps best practices. Implement model artifact management, experiment tracking, and automated model testing and deployment at a standard other teams across Gatik adopt.
- Own the metrics. Define, implement, and validate model and release metrics (precision, recall, F1, ROC AUC, calibration, and task-specific metrics) with the statistical rigor that safety-critical release decisions demand, and characterize how behavior evolves across software releases and scenario types.
- Run thoughtful experiments. Set up baselines, ablations, and holdout comparisons; quantify uncertainty; and communicate results that help the autonomy team decide with confidence.
- Build tools engineers love. Develop dashboards and services for comparing software versions, exploring results at the scenario level, visualizing model outputs, and gathering reviewer feedback.
- Shape shared infrastructure. Partner with ML Platform, Jobs Platform, Cloud Platform, and HPC engineers to design and review changes to GPU scheduling and quota, job orchestration, storage, and compute cost.
- Raise the engineering and reliability bar. Design clean interfaces between data, modeling, and product layers; champion testing, type checking, code review, and fast CI; and lead root-causing and prevention of incidents, including those that cross team boundaries.
- Lead technically (Staff). Define and drive multi-quarter technical strategy for data and training infrastructure, represent Scalable ML in architecture discussions, and mentor engineers across the team.
What You'll Need to Succeed- Bachelor's degree in Computer Science, Computer Engineering, Electrical Engineering, Robotics, or a related field and 5+ years of relevant industry experience (or a Master's degree and 3+ years). Staff candidates typically bring 8+ years, including time setting technical direction beyond a single team.
- Strong Python software engineering skills, including designing modular, maintainable, high-performance, 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.
- Hands-on production experience with Ray (Ray Core, Ray Data, Ray Train, and/or Ray Tune), including running and debugging Ray workloads at scale.
- Deep experience with PyTorch and PyTorch Lightning for large-scale training, including distributed strategies such as DDP or FSDP, mixed precision, and checkpointing.
- Experience designing and operating scalable ML infrastructure: distributed training and evaluation pipelines, experiment tracking, model serving, or large-scale distributed jobs.
- Familiarity with shared-compute environments, such as GPU scheduling and quota, job orchestration, and multi-tenant clusters.
- Solid grounding in statistics and experimental design, including choosing evaluation strategies, metrics, and thresholds that support real-world decisions.
- Clear communication and the ability to work effectively across software, ML, and autonomy teams.
Salary Range: $180,000 - $260,000
