Lead AI/ML Engineer

. Build and ship AI/ML features across data preparation, training or fine-tuning, evaluation, deployment, and monitoring .

Posted 9/25/2026full-timeRemote • United StatesSenior💰 $156,560 - $260,933 per yearWebsite

Tools & technologies

AWSAzureGoogle Cloud PlatformPandasPythonPyTorchScikit-Learn

Key responsibilities & impact

Build and ship AI/ML features across data preparation, training or fine-tuning, evaluation, deployment, and monitoring
Own features end-to-end and iterate based on production feedback
Build RAG pipelines and work with LLM APIs and open-source models
Design prompts for reliability and contribute to agentic workflows
Build data pipelines, labeling workflows, and evaluation frameworks
Apply AI to manufacturing, supply chain, and physical operations problems
Work on computer vision for quality inspection, predictive maintenance, sensor data, demand forecasting, inventory planning, supplier risk, and logistics
Collaborate with Vehicle Engineering, Manufacturing, and Operations to translate requirements into measurable AI systems
Report to the Distinguished Engineer of Generative AI

What you’ll need

PhD in a relevant field for early-career candidates, or 5+ years of professional or research experience working directly on ML systems for candidates without a PhD
Demonstrated ability to build an end-to-end project, thesis, or production system
Foundational understanding of model training, loss functions, evaluation metrics, overfitting, and regularization
Practical experience with supervised learning, NLP, computer vision, and time-series modeling
Familiarity with LLM APIs such as OpenAI, Anthropic, Gemini, or similar
Basic exposure to RAG, embeddings, or retrieval systems
Ability to evaluate model quality rigorously
Python proficiency with PyTorch or JAX, Hugging Face, pandas, and scikit-learn
Ability to write production-quality code
Familiarity with AWS, GCP, or Azure at a working level
Version control, experiment tracking, and basic MLOps practices
BS required
Ability to explain technical decisions to non-technical stakeholders
MS or PhD in a relevant field preferred
Background or genuine interest in Mechanical Engineering, Electrical Engineering, Robotics, Industrial Engineering, or a related physical discipline preferred
Exposure to computer vision, sensor data, or real-time systems preferred
Familiarity with supply chain, logistics, or operations research problems preferred
Experience with simulation environments or physical hardware preferred

Comp & perks