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Salary: USD 170,000 - 240,000 / annual
Gatik AI is seeking a Machine Learning Engineer to develop, optimize, and deploy production ML models across their autonomous vehicle (AV) stack. This is a full-time, onsite role (5 days/week) based in Santa Clara, CA.
You will own the complete ML lifecycle—from data strategy and preprocessing through training, evaluation, optimization, deployment, and monitoring. Your work will span perception, prediction, planning, and scene understanding models for autonomous driving systems.
Key responsibilities include:
- Developing end-to-end ML models with ownership across the full pipeline
- Building and improving autonomous driving models for perception, prediction, planning, and scene understanding
- Optimizing neural networks using quantization, pruning, sparsification, compression, and efficient architecture design to meet strict latency, compute, memory, and power constraints
- Integrating trained models into C++-based autonomy systems and optimizing inference for production vehicle hardware
- Profiling and optimizing neural networks using CUDA, TensorRT, and related technologies
- Analyzing model performance using simulation and real-world driving data to identify failure modes and drive improvements
- Building high-throughput pipelines for training, evaluation, data processing, and large-scale offline inference
- Developing reliable pipelines for dataset curation, annotation, preprocessing, visualization, diagnostics, benchmarking, and continuous feedback from field data
- Partnering with autonomy, systems, hardware, and infrastructure teams to ensure ML components integrate reliably into the broader vehicle platform
Requirements:
- MS or PhD in Computer Science, Machine Learning, Robotics, Electrical Engineering, Statistics, Optimization, or related field
- Strong Python skills and experience with 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
Bonus qualifications:
- Experience with transformers, multimodal models, diffusion models, world models, or end-to-end driving models
- Experience in autonomous driving, robotics, or other safety-critical real-time ML systems
- Publications or demonstrated technical contributions in efficient ML, autonomous driving, robotics, or related areas
- Prior contributions to large-scale ML systems deployed in production
