> Markdown version of [/jobs/ext/3447432-machine-learning-engineer](https://www.wearedevelopers.com/jobs/ext/3447432-machine-learning-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Machine Learning Engineer - **Company:** Gatik AI, Inc. - **Location:** Santa Clara, CA, United States - **Salary:** $170,000.0 - $240,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Neural Networks, Microsoft Azure, C++ (Programming Language), Cloud Computing, Software Code Optimization, Profiling, Nvidia CUDA, Computer Programming, Data Transformation, Software Debugging, Python (Programming Language), Machine Learning, Software Architecture, Tensorflow, Software Systems, Data Streaming, Systems Integration, Data Processing, Pytorch, Data Strategy, Information Technology, Low Latency, Machine Learning Operations, TensorRT - **Published:** September 9, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=7ad706dadadab692 ## About the Role * 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., * 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. ## Description 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. * Cross-Functional Integration: Partner with autonomy, systems, hardware, and infrastructure teams to ensure ML components integrate reliably into the broader vehicle platform. ## Related Videos - [Tour de Force: Open-Source LLM Inference Optimization from Simple to Sophisticated](https://www.wearedevelopers.com/videos/100099-tour-de-force-open-source-llm-inference-optimization-from-simple-to-sophisticated) - [Introduction to Azure Machine Learning](https://www.wearedevelopers.com/videos/368-introduction-to-azure-machine-learning) - [Profiling Symfony & PHP apps with Blackfire](https://www.wearedevelopers.com/videos/265-profiling-symfony-php-apps-with-blackfire) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. 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