Senior AI/ML Scientist

The Role:  

As an Artificial Intelligence and Machine Learning Scientist, you’ll be part of a team that is pioneering the integration of simulation, automation, AI agents, large language models (LLMs), and machine learning into critical systems for vehicle design, calibration, and performance.  You will work cross-functionally with engineers, data scientists, simulation specialists, domain experts and platform teams to define and execute high-impact AI/ML initiatives. Your role will blend hands-on development, technical direction-setting, and mentorship, helping GM scale next-generation capabilities. 

What You’ll Do:  

• Lead and/or support the integration of AI/ML into core engineering tools and simulation frameworks, ensuring robustness, interpretability, and physical relevance of outputs. 

• Translate complex engineering needs into actionable AI/ML solutions, balancing innovation with stability and traceability. 

• Use data analytics and signal processing to analyze simulation output data 

• Develop custom feature extraction methods for predictive modeling - used in optimizations. 

• Apply statistical methods, ML, Big data analytics, anomaly detection methods, and clustering to uncover patterns 

• Work with large scale data sets and collaborate with subject matter experts to incorporate physical interpretations of insights 

• Work collaboratively with a team of specialists ranging from data scientists, simulation experts and calibration technical specialists to cohesively build new capabilities into our existing Co-Simulation (Digital Twin) framework. 

• Lead and/or support the development and maintenance of cloud and/or on-prem databases. 

• Define strategies for large-scale data ingestion, embedding generation, retrieval tuning, and prompt optimization in production environments. 

• Establish and champion engineering best practices, coding standards, and documentation norms for AI/ML systems across teams. 

Your Skills and Abilities (Required Qualifications):  

• Bachelor’s degree in Computer Science, Engineering, or Mathematics 

• Proficiency in modern programming languages such as Python and C/C++ (JavaScript optional depending on your stack), with strong foundations in object‑oriented design and software architecture. 

• 5+ years of experience developing and deploying machine learning or deep learning systems in production environments  or  5+ years working in LLM development, NLP, or AI‑driven automation. 

• Solid understanding of data science, big‑data workflows, and applied statistics; familiarity with signal‑processing techniques is a plus. 

• Strong experience in major ML frameworks and toolchains (e.g., PyTorch, TensorFlow, HuggingFace Transformers, Scikit-learn, XGBoost) 

• Demonstrated experience with transformer architectures, LLMs, AI agents, or models integrated with simulation workflows. 

• Experience with retrieval-augmented generation (RAG), prompt engineering, and embedding optimization. 

• Excellent problem-solving skills with the ability to thrive in a demanding, fast-paced work environment. 

• Strong interpersonal and communication skills and a willingness to collaborate cross-functionally with different teams. 

What Can Give You a Competitive Edge (Preferred Qualifications):  

• Master’s or PhD in Computer Science, Engineering, Mathematics 

• Experience in automotive or physical system simulation domains. 

• Familiarity with co-simulation frameworks, physical modeling tools (e.g., Simulink, AMESIM), or automotive calibration workflows. 

• Knowledge of optimization techniques (e.g., PSO, GD) applied to AI-simulation or engineering workflows. 

• Experience with MLOps practices, including containerized deployment (Docker, Kubernetes), CI/CD pipelines, and cloud‑native model serving. 

• Experience building scalable ML systems or full‑stack AI pipelines using modern frameworks (e.g., FastAPI, Ray, cloud services). 

• Willingness to learn and continue developing knowledge in an up-and-coming field. 

• Visionary thinking: You identify and pursue novel AI/ML applications in engineering workflows. 

• Strategic ownership: You drive initiatives from concept to integration, influencing cross-org direction. 

• Cross-domain fluency: You connect simulation, embedded systems, and data science to deliver tangible value. 

• Commitment to mentorship: You uplift others and scale your expertise across the team.