Job description

Who we are Gatik, the leader in autonomous middle-mile logistics, is revolutionizing the B2B supply chain with its autonomous transportation-as-a-service (ATaaS) solution and prioritizing safe, consistent deliveries while streamlining freight movement by reducing congestion. The company focuses on short-haul, B2B logistics for Fortune 500 retailers and in 2021 launched the world’s first fully driverless commercial transportation service with Walmart. Gatik's Class 3-7 autonomous trucks are commercially deployed across major markets, including Texas, Arkansas, and Ontario, Canada, driving innovation in freight transportation. The company's proprietary Level 4 autonomous technology, Gatik Carrier™, is custom-built to transport freight safely and efficiently between pick-up and drop-off locations on the middle mile. With robust capabilities in both highway and urban environments, Gatik Carrier™ serves as an all-encompassing solution that integrates advanced software and hardware powering the fleet, facilitating effortless integration into customers' logistics operations. About the role We are seeking a high impact, technically deep Machine Learning Engineer to develop, optimize, and deploy production ML models across our autonomous vehicle (AV) stack. This role is ideal for engineers who enjoy building models end-to-end - from data and training through optimization and real-time deployment on autonomous vehicles. You will work closely with perception, prediction, planning, infrastructure, systems, and hardware teams to ensure models are efficient, scalable, reliable, and production-ready for both on-vehicle and cloud workflows. This role is onsite 5 days a week at our Santa Clara, CA office! What you'll do End-to-End Model Development: Own the full ML lifecycle, including data strategy, preprocessing, training, evaluation, optimization, deployment, and monitoring. Autonomous Driving Models: Develop and improve models supporting perception, prediction, planning, and scene understanding. Efficient Neural Network Design: Optimize models using techniques such as quantization, pruning, sparsification, compression, and efficient architecture design to meet strict latency, compute, memory, and power constraints. Real-Time Deployment: Integrate trained models into C++-based autonomy systems and optimize inference for production vehicle hardware. Model Optimization: Profile and optimize neural networks using CUDA, TensorRT, and related technologies. Simulation and Evaluation: Analyze model performance using simulation and real-world driving data, identify failure modes, and drive improvements. Scalable ML Infrastructure: Build high-throughput pipelines for training, evaluation, data processing, and large-scale offline inference. Data Workflows and Tooling: Develop reliable pipelines for dataset curation, annotation, preprocessing, visualization, diagnostics, benchmarking, and continuous feedback from field data. CrossFunctionalIntegration: Partner with autonomy, systems, hardware, and infrastructure teams to ensure ML components integrate reliably into the broader vehicle platform. What we're looking for Education: MS or PhD in Computer Science, Machine Learning, Robotics, Electrical Engineering, Statistics, Optimization, or a related field. Experience: Open to all experience levels. Leveling will be determined based on experience and technical depth. Programming & Frameworks: Strong Python skills and experience with frameworks such as PyTorch or TensorFlow. Strong C++ skills and experience integrating ML models into high-performance production systems. Core ML & Systems Expertise: Deep understanding of ML workflows, including data curation, training, evaluation, ablation studies, deployment, and inference optimization. Experience deploying and optimizing neural networks for real-time, embedded, robotics, autonomous driving, or other performance-constrained systems. Experience with model optimization techniques such as quantization, pruning, compression, and efficient architectures. Experience with software architecture, profiling, latency optimization, system-level debugging, and data flow analysis. Infrastructure & Compute Tools: Experience with CUDA and TensorRT is highly desirable. Experience with cloud-based ML training and evaluation pipelines, preferably Azure. Bonus Qualifications Experience with transformers, multimodal models, diffusion models, world models, or end-to-end driving models is a plus. Experience in autonomous driving, robotics, or other safety-critical real-time ML systems is strongly preferred. Publications or demonstrated technical contributions in efficient ML, autonomous driving, robotics, or related areas are a plus. Prior contributions to large-scale ML systems deployed in production. Salary Range $170,000 - $240,000 More about Gatik Founded in 2017 by experts in autonomous vehicle technology, Gatik has rapidly expanded its presence to Mountain View, Dallas-Fort Worth, Arkansas, and Toronto. As the first and only company to achieve fully driverless middle-mile commercial deliveries, Gatik holds a unique and defensible position in the AV industry, with a clear trajectory toward sustainable growth and profitability. We have delivered complete, proprietary AV technology - an integration of software and hardware - to enable earlier successes for our clients in constrained Level 4 autonomy. By choosing the middle mile – with defined point-to-point delivery, we have simplified some of the more complex AV challenges, enabling us to achieve full autonomy ahead of competitors. Given extensive knowledge of Gatik’s well-defined, fixed route ODDs and hybrid architecture, we are able to hyper-optimize our models with exponentially less data, establish gate-keeping mechanisms to maintain explainability, and ensure continued safety of the system for unmanned operations. Visit us at Gatik for more company information and Careers at Gatik for more open roles. Notable News Forbes: Forget robotaxis. Upstart Gatik sees middle-mile deliveries as the path to profitable AVs Tech Brew: Gatik AI exec unpacks the regulations that could shape the AV industry Business Wire: Gatik Paves the Way for Safe Driverless Operations (‘Freight-Only’) at Scale with Industry-First Third-Party Safety Assessment Framework Auto Futures: Autonomous Trucking Group Gatik Secures Investment From NIPPON EXPRESS HOLDINGS Automotive News: Gatik foresees hundreds of self-driving trucks on road soon, and that's just the beginning Forbes: Isuzu And Gatik Go All In To Scale Up Driverless Freight Services Bloomberg: Autonomous Vehicle Startup Takes Off by Picking Off Easier Routes Reuters: Driverless vehicles on limited routes bump along despite US robotaxi scrutiny 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

Requirements

Must-haves

You will work closely with perception, prediction, planning, infrastructure, systems, and hardware teams to ensure models are efficient, scalable, reliable, and production-ready for both on-vehicle and cloud workflows.
This role is onsite 5 days a week at our Santa Clara, CA office!
End-to-End Model Development: Own the full ML lifecycle, including data strategy, preprocessing, training, evaluation, optimization, deployment, and monitoring.
Autonomous Driving Models: Develop and improve models supporting perception, prediction, planning, and scene understanding.
Real-Time Deployment: Integrate trained models into C++-based autonomy systems and optimize inference for production vehicle hardware.
Model Optimization: Profile and optimize neural networks using CUDA, TensorRT, and related technologies.
Simulation and Evaluation: Analyze model performance using simulation and real-world driving data, identify failure modes, and drive improvements.
Scalable ML Infrastructure: Build high-throughput pipelines for training, evaluation, data processing, and large-scale offline inference.
Data Workflows and Tooling: Develop reliable pipelines for dataset curation, annotation, preprocessing, visualization, diagnostics, benchmarking, and continuous feedback from field data.
CrossFunctionalIntegration: Partner with autonomy, systems, hardware, and infrastructure teams to ensure ML components integrate reliably into the broader vehicle platform.
Education: MS or PhD in Computer Science, Machine Learning, Robotics, Electrical Engineering, Statistics, Optimization, or a related field.
Experience: Open to all experience levels. Leveling will be determined based on experience and technical depth.
Strong Python skills and experience with frameworks such as PyTorch or TensorFlow.
Strong C++ skills and experience integrating ML models into high-performance production systems.
Deep understanding of ML workflows, including data curation, training, evaluation, ablation studies, deployment, and inference optimization.
Experience deploying and optimizing neural networks for real-time, embedded, robotics, autonomous driving, or other performance-constrained systems.
Experience with model optimization techniques such as quantization, pruning, compression, and efficient architectures.
Experience with software architecture, profiling, latency optimization, system-level debugging, and data flow analysis.
Experience with CUDA and TensorRT is highly desirable.
Experience with cloud-based ML training and evaluation pipelines, preferably Azure.

Nice-to-haves

Bonus Qualifications
Experience with transformers, multimodal models, diffusion models, world models, or end-to-end driving models is a plus.
Experience in autonomous driving, robotics, or other safety-critical real-time ML systems is strongly preferred.
Publications or demonstrated technical contributions in efficient ML, autonomous driving, robotics, or related areas are a plus.

Application prep

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Keywords to mirror

machine learningmlpythonazureaigo

Platform: greenhouse
Effort: quick

Benefits

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Vision