About the Team
Scaling 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 Role
As 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
More about Gatik
Founded in 2017, Gatik has grown into a commercial force in autonomous freight, operating trucks daily in live customer networks across the United States and Canada. We're headquartered in Santa Clara, California, but our impact is felt on the roads and in the supply chains that keep essential goods moving for some of the biggest companies in North America.
Our trucks do some of the most demanding work in freight, running high-frequency, time-sensitive routes between distribution centers and stores, where reliability matters every single day. For our customers, that means more capacity, better service and products arriving where they need to be, when they need to be there.
Behind it all is AI-first technology built from the ground up for autonomous trucking, capable of navigating highways and surface streets and tackling the complexity of real-world logistics at scale.
Visit Gatik for more company information and Careers at Gatik for more open roles.
Notable News
For the latest company news, visit the Gatik newsroom.
Recent media coverage
TechCrunch: Self-driving truck startup Gatik raises $200M following PepsiCo deal
Reuters: Gatik secures $200 million in funding as autonomous freight market accelerates
Bloomberg: Driverless Trucks Can Save Lives on America's Roads
PepsiCo website: PepsiCo and Gatik announce multi-year agreement to deploy autonomous freight in North America
Wall Street Journal: Driverless Trucks Are Here - and They’re Delivering Bags of Doritos
CBS News: Pepsi deploys self-driving delivery trucks
Bloomberg: Autonomous Trucking Firm Gatik Inks Contracts Worth $600 Million
Forbes: Hundreds’ Of Gatik Robot Delivery Trucks Headed For U.S. Roads
CNBC-TV18: No more test runs: Gatik says driverless trucks are now a real, money-making business
Forbes: Gatik And Loblaw Announce Largest Commercial Deployment Of AV Trucks
Taking Care of Our Team
At Gatik, we connect people of extraordinary talent and experience to an opportunity to create a more resilient supply chain and contribute to our environment’s sustainability. We are diverse in our backgrounds and perspectives yet united by a bold vision and shared commitment to our values. Our culture emphasizes the importance of collaboration, respect and agility.
We at Gatik strive to create a diverse and inclusive environment where everyone feels they have opportunities to succeed and grow because we know that together we can do great things. We are committed to an inclusive and diverse team. We do not discriminate based on race, color, ethnicity, ancestry, national origin, religion, sex, gender, gender identity, gender expression, sexual orientation, age, disability, veteran status, genetic information, marital status or any legally protected status.
E-Verify Notice: By submitting your application, you acknowledge that Gatik participates in the U.S. Department of Homeland Security E-Verify program.
