About Coulomb

At Coulomb AI, we envision a future powered by the seamless integration of AI and Battery Technologies, creating a powerful synergy to combat climate change. We build category-leading software that can significantly improve battery lifespan and performance by employing advanced predictive analytics, real-time monitoring, and adaptive battery management algorithms.

Coulomb is headquartered in San Francisco with its development and research center in Bangalore. Founders Khushboo Shrivastava and Santanu Mondal are alumni of IIT Bombay and have first-hand experience working on first-generation Electric Vehicles at General Motors. We are backed by some of the best climate tech investors in the world, including Y Combinator, Harvard Management Seed Capital, CSVE Ventures, and prominent Silicon Valley-based Angels.

We already have a global presence with customers in 5 countries across UK, US, APAC and India

Yet, we’re still at the start of our journey, and we want exceptional people to join us!

About the Role

We're seeking exceptional AI Engineers to join our rapidly growing team and tackle some of the most complex challenges at the intersection of artificial intelligence and energy systems. You'll work on mission-critical problems including battery state prediction, energy demand forecasting, thermal management optimization, and autonomous energy trading systems that operate at massive scale.

This role offers the opportunity to work with world-class engineers, deploy AI systems that impact millions of users, and directly contribute to solving climate change through technology. You'll be building production systems that must operate with extreme reliability while pushing the boundaries of what's possible with AI.

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

Interview Process

The number and format of interviews that you'll go through will be personalized to your skillset and the type of role that you're looking for. We want to give everyone the best chance at representing their abilities. Everyone starts with one or two interviews on a video call, and if we think there's a fit, we'll bring you onsite for a round of in-person interviews. Below is the sequence of the process.

First Video Call - You should be prepared to talk about your background, experiences, and interest in Coulomb
Writing Good Code - The ability to write code that is Efficient, Effective, Easy to read, maintain, and upgrade
Analyzing the Efficiency of Code - The ability to analyze and improve the efficiency of code so that it runs with optimal complexity, creating systems that store and transmit data efficiently
Technical Interview - You should be prepared to elaborate on your conceptual and practical experience to our Technical experts
Culture Fit - Make sure we are aligned to work together

Coulomb AI

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Engineering

AI Engineer