Software Engineer, Backend, Vehicle Engineering

Tesla’s Vehicle Engineering department is composed of thousands of the world’s best vehicle, battery, and manufacturing engineers. These engineers are responsible for the mechanical and industrial design of all major programs, for example, batteries, Optimus, Robotaxi, and all new factories. Engineering Automation Software, which is part of Vehicle Engineering, is responsible for making the tools these engineers use every bit as intelligent and dynamic as the products themselves. 

As a software engineer on the Engineering Automation Software team, you will develop software applications that automate or streamline parts of the design process.  

• Build data pipelines that handle a diverse set of engineering related data such as operational meta data, drawings, telemetry, and 3D geometries 
• Develop and optimize robust applications through proficient use of Go and Python 
• Manage our deployment infrastructure using custom GitHub Actions, Docker, ArgoCD, with images deployed to on-prem Kubernetes clusters 
• Influence architectural decisions with focus on security, scalability, reliability and high-performance using tools like Prometheus, Grafana, Splunk and OpsGenie 
• Design and implement tools, tests, metrics, and dashboards to accelerate the development cycle of our simulations 
• Work closely with frontend and machine learning engineers to seamlessly integrate with backend systems 
• Work closely with the other Vehicle Engineering teams to design and implement backend components required for future Vehicle Engineering features and processes 

• Strong knowledge of at least one programming language related to data engineering, such as Python or Golang 
• Proven experience in database management with specific proficiency in PostgreSQL, including the ability to write complex SQL statements. Familiarity with database concepts such as views and SQL functions
• Experience with data processing frameworks such as Spark, Dask, or Ray is preferred 
• Experience with GitHub Actions, Docker, ArgoCD, and Kubernetes is preferred 
• Experience with scientific computing libraries such as numpy, pandas, or scikit-learn is preferred
• Experience with machine learning libraries such as PyTorch or JAX is preferred 
• Experience with ElasticSearch or other scalable search systems is preferred 
• Knowledge of machine learning, computer vision, or neural networks is preferred