Website:
rastefleet.com
Job details:
Senior Mechanical Engineer, Vehicle Diagnostics & AI
About the role
Raste is building a software platform that combines vehicle engineering, machine learning, and AI to predict failures and help users understand vehicle health.
We are looking for a mechanical engineer with strong automotive knowledge, practical experience working on cars, and demonstrated ability to build software and machine learning solutions using modern AI tools.
You will work directly with the CTO, progressively taking ownership of vehicle failure prediction models and their implementation in the product. This role offers substantial responsibility for turning engineering knowledge into working software.
Requirements:
- Bachelor’s degree in Mechanical, Automotive or Aerospace Engineering.
- 2–5 years of professional experience in the automotive or aerospace industry, with demonstrated responsibility for solving engineering problems and delivering results.
- Strong mechanical engineering fundamentals and a good understanding of how vehicle systems operate, degrade, and fail.
- Practical experience working on cars, including troubleshooting, inspecting components, interpreting diagnostic information, and connecting observed symptoms to potential causes.
- Applied experience in statistics, data analysis, and machine learning, including preparing datasets, developing features, training models, and evaluating performance. Experience with sensor or time-series data is particularly relevant.
- Working proficiency in Python and the ability to read, modify, debug, and test code. Familiarity with Git, APIs, and integrating models into software applications.
- Demonstrated ability to use AI coding tools such as Claude Code, Codex, or equivalent tools to build functioning software, while independently verifying its correctness and reliability.
- Practical experience running and integrating local large language models (LLMs), using model APIs, and configuring AI workflows. Working knowledge of agent harnesses, tool calling, and Model Context Protocol (MCP) integrations.
- Valid driver’s license.
Roles and responsibilities
- Analyze real-world vehicle operating data, diagnostic information, and maintenance records to identify failure patterns and improve Raste’s predictive models.
- Apply mechanical and automotive engineering knowledge to identify failure mechanisms, select relevant signals, and develop physically meaningful model features.
- Prepare and validate training datasets, including cleaning data, investigating missing or inconsistent measurements, and verifying failure labels against available evidence.
- Develop, train, compare, and improve machine learning models for vehicle fault detection and failure prediction. Evaluate false alarms, missed failures, and prediction lead time using appropriate validation methods.
- Conduct practical vehicle investigations and data collection to test engineering hypotheses and validate model findings against observed vehicle behavior.
- Build and integrate analysis tools, model pipelines, and product functionality using AI-assisted development. Take responsibility for reviewing, debugging, testing, and documenting the resulting software.
- Develop and improve Raste’s AI chatbot features so they explain vehicle data and model outputs in simple terms, communicate uncertainty (if present), and ground responses in available evidence.
- Integrate LLMs, engineering tools, and data sources through APIs, MCP, and agent workflows where they improve product functionality or engineering productivity.
- Progressively assume technical ownership of 2–3 vehicle system failure models within the first 6–8 months.Ownership includes understanding failure mechanisms, maintaining data and code, validating performance, supporting deployment, and driving subsequent improvements.
- Work with the CTO to translate product priorities into engineering tasks, communicate technical tradeoffs, and independently execute agreed work.
Preferred qualifications
- Master’s degree or PhD in Mechanical Engineering or a closely related field.
- Experience with vehicle diagnostic systems and data, including OBD-II, CAN bus, diagnostic trouble codes, or vehicle telemetry.
- Experience in predictive maintenance, reliability engineering, failure analysis, or deploying machine learning models in production.
- Experience evaluating LLM applications, connecting responses to verified technical information, and measuring response accuracy and reliability.
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