AI Engineer

As an AI Engineer, here’s what you’ll be doing

Core AI Development

• Design, implement, and deploy machine learning models for battery state estimation, degradation prediction, and performance optimization
• Develop advanced time-series forecasting models for energy demand, supply, and pricing across diverse market conditions
• Design and validate ML models for anomaly detection and failure prediction
• Create reinforcement learning algorithms for optimal charging strategies, grid balancing, and energy arbitrage
• Build robust data pipelines processing terabytes of sensor data from batteries, inverters, and grid connections
• Develop monitoring and observability systems to ensure AI models maintain performance in production

Research and Innovation

• Stay at the forefront of AI research, particularly in domains relevant to energy systems, physics-informed neural networks, and optimization
• Collaborate with battery scientists and power electronics engineers to translate domain expertise into AI solutions
• Publish research and represent Coulomb AI at top-tier conferences (NeurIPS, ICML, ICLR, IEEE Power & Energy)
• Build novel architectures that incorporate physical constraints and energy system dynamics into neural networks

Cross-functional Collaboration

• Work closely with hardware engineers to optimize AI algorithms for edge deployment
• Partner with product teams to define AI-powered features that deliver exceptional user experiences
• Collaborate with safety and regulatory teams to ensure AI systems meet automotive and grid-scale safety standards
• Support business development by demonstrating AI capabilities to potential customers and partners

We are excited about you because you have:

• MS/PhD in Computer Science, Electrical Engineering, Physics, or related field, or equivalent practical experience
• 3+ years of experience developing and deploying machine learning systems in production environments
• Expert-level proficiency in Python, PyTorch/TensorFlow, and modern MLOps tools (MLflow, Kubeflow, etc.)
• Strong background in time-series analysis, optimization algorithms, or computer vision
• Experience with distributed computing frameworks (Ray, Dask, Spark) and cloud platforms (AWS, GCP, Azure)
• Proficiency with transformer architectures and modern NLP libraries (Hugging Face Transformers, LangChain, etc.)
• Experience building and deploying Retrieval-Augmented Generation (RAG) systems for technical documentation and knowledge management
• Hands-on experience fine-tuning and deploying open-source foundation models (DeepSeek, Llama, Mistral, etc.)
• Familiarity with vector databases (Pinecone, Weaviate, Chroma) and embedding models for semantic search applications

You’ll be a great fit if:

Domain Knowledge (Preferred)

• Understanding of battery technologies, electrochemistry, or power systems engineering
• Experience with physics-informed machine learning or scientific computing
• Background in control systems, signal processing, or embedded systems development
• Knowledge of energy markets, grid operations, or renewable energy systems
• Experience with automotive or aerospace safety-critical system development
• Familiarity with open-source AI ecosystems and model fine-tuning techniques (LoRA, QLoRA, PEFT)
• Experience with prompt engineering, few-shot learning, and in-context learning for foundation models

Engineering Mindset

• Track record of shipping ML products that operate reliably at scale
• Strong software engineering fundamentals with experience in system design and architecture
• Comfort working in fast-paced environments with rapidly evolving requirements
• Experience with A/B testing, model evaluation, and performance monitoring in production
• Ability to work effectively across multiple codebases and collaborate with diverse engineering teams
• Hands-on experience with model serving infrastructure (vLLM, TGI, Ollama) and API development
• Proficiency with modern AI development workflows including model versioning, experiment tracking, and automated evaluation pipelines

Problem-Solving Approach

• First principles thinking and ability to break down complex problems into manageable components
• Data-driven decision making with strong intuition for when and how to apply different ML techniques
• Comfortable with ambiguity and able to drive projects forward with minimal supervision
• Strong communication skills and ability to explain complex technical concepts to diverse audiences

A meeting lands on your calendar

If they’re a yes, I book the chat. You show up — that’s the whole job-hunt.