Senior Machine Learning Engineer, Powertrain Design and Optimal Selection

Senior Machine Learning Engineer, Powertrain Design and Optimal Selection

Tesla

Athens, Greece

What You’ll Do

• Lead Machine Learning for Design Optimization: Develop and deploy advanced ML models using high-fidelity physics-based data to minimize reliance on time-intensive simulations and explore vast design spaces efficiently.
• Drive Data-Driven Decision Making: Conduct large-scale data analysis to inform and accelerate human-driven selection of optimal powertrain designs, fostering faster, more precise engineering outcomes.
• Manage Data Lifecycle: Collect, clean, process, and analyze complex datasets to derive actionable insights for ML applications, ensuring data integrity and scalability.
• Innovate and Validate Models: Design, implement, evaluate, and iterate on sophisticated ML models to enhance design strategies and system performance, incorporating techniques like deep learning and reinforcement learning.
• Integrate ML into Core Workflows: Spearhead the embedding of ML-driven insights into existing design processes, promoting efficiency, innovation, and cross-functional collaboration.
• Mentor and Collaborate: Guide junior team members, contribute to strategic planning, and stay abreast of emerging ML trends to continually elevate our capabilities.

What You’ll Bring

• PhD in Computer Science, Data Science, Electrical Engineering, or a related field (or Master's with equivalent professional experience); strong preference for candidates with research focused on ML applications in engineering or physics-based systems.
• 2+ years of hands-on experience in machine learning and data science, with a demonstrated ability to work independently on complex, real-world projects.
• Advanced proficiency in Python and expertise in ML frameworks such as TensorFlow, PyTorch, or Scikit-learn.
• Proven expertise in data processing and analysis, including handling large datasets with tools like Pandas, NumPy, SQL, and big data technologies (e.g., Spark or Dask).
• Strong background in software engineering practices, including Agile methodologies, version control (e.g., Git), and CI/CD pipelines.
• Excellent analytical and problem-solving skills, with a structured approach to tackling ambiguous technical challenges.
• Fluency in English (written and spoken); additional languages are a plus.