Machine Learning Engineer

Gatik is seeking a technically deep Machine Learning Engineer to develop, optimize, and deploy production machine learning models across its autonomous vehicle stack. The role owns the end-to-end ML lifecycle, develops models for perception, prediction, planning, and scene understanding, and integrates and optimizes them for real-time vehicle and cloud workflows.

What you'll do:

• Own the full ML lifecycle, including data strategy, preprocessing, training, evaluation, optimization, deployment, and monitoring
• Develop and improve models supporting perception, prediction, planning, and scene understanding
• Optimize models using techniques such as quantization, pruning, sparsification, compression, and efficient architecture design to meet strict latency, compute, memory, and power constraints
• Integrate trained models into C++-based autonomy systems and optimize inference for production vehicle hardware
• Profile and optimize neural networks using CUDA, TensorRT, and related technologies
• Analyze model performance using simulation and real-world driving data, identify failure modes, and drive improvements
• Build high-throughput pipelines for training, evaluation, data processing, and large-scale offline inference
• Develop reliable pipelines for dataset curation, annotation, preprocessing, visualization, diagnostics, benchmarking, and continuous feedback from field data
• Partner with autonomy, systems, hardware, and infrastructure teams to ensure ML components integrate reliably into the broader vehicle platform

What they're looking for:

• MS or PhD in Computer Science, Machine Learning, Robotics, Electrical Engineering, Statistics, Optimization, or a related field
• 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 cloud-based ML training and evaluation pipelines, preferably Azure
• Experience with transformers, multimodal models, diffusion models, world models, or end-to-end driving models is a plus
• 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