Software Engineering Careers

Location: Remote / Seattle, WA Type: Full-time

Energsoft is redefining how the world understands and manages batteries. We’re building the next generation of AI-driven battery intelligence software, integrating Microsoft Fabric, Hugging Face Transformers, and LLM-powered analytics pipelines to help enterprises accelerate innovation in clean energy, electrification, and data science.

We’re looking for a hands-on Data Scientist / ML Engineer who thrives at the intersection of data engineering, applied AI, and product innovation — someone who can turn terabytes of time-series data into predictive insights and smarter decisions.

• Build and deploy machine learning and LLM-based models for predictive and prescriptive analytics on large-scale battery datasets.
   
• Design and optimize data pipelines on Microsoft Fabric, Azure ML, and Databricks, integrating distributed compute, vector databases, and model orchestration.
   
• Fine-tune open-source models (Hugging Face, PyTorch, TensorFlow) for anomaly detection, forecasting, and digital twin simulations.
   
• Apply deep learning, reinforcement learning, and graph-based models to battery lifecycle prediction, SoH/SoC estimation, and recommendation systems.
   
• Develop LLM agents that summarize, interpret, and reason over experimental and operational battery data.
   
• Collaborate cross-functionally with engineers, electrochemists, and product managers to deliver customer-facing ML insights.
   
• Contribute to Energsoft’s internal MLops framework with CI/CD for model deployment and automated retraining.
   

• 15+ years of industry experience in data science or machine learning, preferably with energy, hardware, or industrial IoT data.
   
• Advanced proficiency in Python, SQL, and modern ML frameworks (PyTorch, TensorFlow, scikit-learn, Hugging Face, LangChain).
   
• Experience building and scaling systems on Azure, AWS, or GCP using modern tools like Microsoft Fabric, Vertex AI, or SageMaker.
   
• Strong understanding of statistics, deep learning, recommender systems, and graph or time-series modeling.
   
• Proven experience in LLM fine-tuning, embedding models, or multi-modal transformer pipelines.
   
• Familiarity with Databricks, Power BI, or Fabric Lakehouse for data visualization and reporting.
   
• Excellent communication skills — able to explain complex ML insights to both engineers and executives.
   

• Work with a team of PhD battery scientists, AI engineers, and data visionaries building the digital nervous system for the energy transition.
   
• Own projects end-to-end — from data ingestion to model deployment — and see your work directly impact EVs, grid storage, and clean energy systems.
   
• Collaborate in a high-trust, pro-diversity, fast-paced environment that encourages innovation, learning, and autonomy.
   
• Competitive compensation, equity options, and full health coverage.
   

If you’re passionate about LLMs, deep learning, and sustainable tech, and want to help create the AI platform for the battery industry, we’d love to hear from you.