Senior Software Engineer, ML Infrastructure Santa Clara, CA

Gatik; Santa Clara (CA); On-site; USD 180,000 - 260,000; Full time

Job summary

Gatik is hiring a Senior/Staff Software Engineer, ML Infrastructure in Santa Clara, CA. You will design and build systems turning fleet data into high-quality training and evaluation sets, and run scalable training and evaluation with robust experiment tracking.

You will lead multi-team collaboration, shape ML platform tooling, and drive metrics and release decisions for safety-critical driverless operations. This role emphasizes mentoring and cross-team technical direction.

Qualifications

• Bachelor’s degree in Computer Science, Computer Engineering, Robotics, or related field; 5+ years of relevant industry experience.
• Staff candidates typically bring 8+ years, including time setting technical direction beyond a single team.
• Strong Python software engineering skills with modular, high-performance systems.

Responsibilities

• Build scalable data pipelines that discover, label, and organize driving scenarios with provenance and clear data contracts.
• Curate balanced training and evaluation datasets across routes, weather, and traffic conditions.
• Scale training and evaluation infrastructure on multi-tenant GPU clusters; robust data loading and checkpointing.

Skills

Python

PyTorch

PyTorch Lightning

Ray

Data pipelines

Distributed training

Experiment tracking

MLOps

Statistical design

Cross-team collaboration

Education

Bachelor’s degree in CS/CE/Robotics

Master’s degree in CS/EE/Robotics

Tools

SQL

Pandas

Scikit-learn

Job description

Senior Software Engineer, ML Infrastructure

Who we are

Gatik is in the business of autonomous freight, and that business is already on the road.

Every day, Gatik’s autonomous trucks move freight for Fortune 50 retailers, grocers and consumer brands across Texas, Arizona, Arkansas and Canada, connecting distribution centers and stores and keeping goods moving. At the heart of it all is the Gatik Driver™, our proprietary Level 4 autonomous driving system, powered by scalable, interpretable AI and engineered to deliver safe, reliable, high-frequency freight operations.

Gatik’s momentum is real. With more than $600 million in contracted revenue and $200 million raised in our August 2026 Series D round, Gatik has taken one of the toughest challenges in autonomous technology and turned it into a proven commercial business.

And the market is paying attention. In 2026, Gatik earned a place on Forbes' America's Best Startup Employers list, and the Inc. 5000 list of America's fastest-growing private companies. The Gatik Driver™ won an Edison Award for Advancements in Autonomous Driving, and Inc. named our Co-founder and Chief Engineer, Apeksha Kumavat, to its Female Founders 500 list.

There's a lot more road ahead, and some of the brightest, most driven minds in autonomous technology are building the next chapter at Gatik. We're taking on hard engineering problems, pushing the limits of what's possible and building on a commercial foundation that's already proven itself in the real world with some of the biggest companies in the world, like PepsiCo and Loblaw. If you're ambitious, love solving problems that matter and want to see your work make a real difference, you'll find plenty to get excited about here.

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.

For the latest company news, visit the Gatik newsroom.

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