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# AI Infrastructure Engineer, Model Optimization & Deployment, Optimus

- **Company:** Tesla Motors
- **Location:** Palo Alto, CA, United States
- **Salary:** $176,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Artificial Neural Networks, Automation of Tests, Microsoft Azure, Cloud Computing, Program Optimization, Serialization, Memory Management, Protocol Buffers, Python (Programming Language), Machine Learning, Prometheus, Azure Machine Learning, Smart Devices, Software Engineering, AI Infrastructure, Google Cloud, Pytorch, Flask (Web Framework), Grafana, Fastapi, Containerization, Kubernetes, ONNX (Open Neural Network Exchange) Format, Avro, Machine Learning Operations, TensorRT, Restful APIs, Serverless Computing, Docker
- **Published:** June 15, 2026
- **Apply:** https://diversityjobs.com/career/13557285/Machine-Learning-Engineer-Model-Optimization-Deployment-Optimus-California-Palo-Alto

## About the Role

* Strong proficiency in Python and PyTorch

 * Experience with model optimization tools (e.g., ONNX, TensorRT, TFLite, TVM)

 * Experience with model inference optimization and quantization

 * Solid understanding of containerization and orchestration (Docker, Kubernetes)

 * Familiarity with cloud platforms (AWS, GCP, Azure) and serverless deployments

 * Strong grasp of software engineering principles and CI/CD pipelines

 * Experience deploying models to edge devices or mobile platforms

 * Knowledge of data serialization formats (e.g., protobuf, Avro)

 * Exposure to observability tools (e.g., Prometheus, Grafana) for ML monitoring

## Description

Tesla AIissolvingrobust, real-world AI through humanoid robots.As a Software Engineer for the Optimus team, you will build the tools and infrastructure to make and measure improvements to neural network architecture, visualize data,assistwith exporting and deploying neural networks toTesla'sneural network chip with real-time latency constraints on Optimus, and evaluate experimental results. You will help us automate the entire workflows of training, validation, and production ofOptimus. Most importantly, you will see your work repeatedly shipped to andutilizedby thousands of Humanoid Robots in real world applications.
What You'll Do

 * Optimize ML models for latency, memory usage, and inference speed

 * Quantize, prune, and convert models (e.g., to ONNX, TensorRT, TFLite) for deployment on various platforms (cloud, edge, mobile)

 * Benchmark and profile model performance across different environments

 * Package and deploy models as REST APIs, batch jobs, or streaming services using tools like FastAPI, Flask, or gRPC

 * Implement CI/CD pipelines for automated testing and deployment of ML models

 * Ensure scalability and reliability of ML services in production environments


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