MS or PhD in Computer Science, Machine Learning, Robotics, Electrical Engineering, Statistics, Optimization, or related field.
Open to all experience levels; leveling based on experience and technical depth.
Strong Python skills with frameworks like PyTorch or TensorFlow.
Strong C++ skills for integrating ML models into production systems.
Deep understanding of ML workflows including data curation, training, evaluation, and deployment.
Experience with real-time, embedded, or autonomous driving systems.
Familiarity with CUDA and TensorRT is highly desirable.
Responsibilities
Own the full ML lifecycle from data strategy to monitoring.
Develop and enhance models for perception, prediction, planning, and scene understanding.
Optimize models for latency, compute, memory, and power constraints.
Integrate trained models into C++-based autonomy systems for real-time deployment.
Profile and optimize neural networks using CUDA and TensorRT.
Analyze model performance using simulation and real-world data to drive improvements.
Build high-throughput pipelines for training, evaluation, and large-scale inference.
Benefits
Onsite work at Santa Clara, CA office 5 days a week.
Opportunity to work on cutting-edge autonomous vehicle technology.
Collaborative environment with cross-functional teams.
Access to advanced tools and technologies in machine learning and robotics.
Full Job Description
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 $180,000 - $260,000
About Gatik AI
Gatik AI is a technology company that develops autonomous vehicles for business to business short-haul logistics. The company was founded in 2017 by Gautam Narang and Arjun Narang. Gatik AI's vehicles are designed to operate on fixed routes between distribution centers, warehouses, and retail locations. The company's mission is to deliver goods safely, efficiently, and on time, while reducing road congestion and carbon emissions. Gatik AI has partnerships with Walmart and Loblaw Companies Limited, two of the largest retailers in North America. The company is headquartered in Vancouver, Canada, with offices in Palo Alto, California.
