Machine Learning Engineer

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-For