Role Summary
This role is an opportunity to work with a high-agency startup team operating in the hard-tech and energy systems space. You will play a critical role in building and scaling predictive analytics and AI systems on top of Amperesand’s IoT platform. This is a hands-on role. You will design, build, and productionize machine-learning systems that ingest high-volume telemetry data and generate actionable insights such as failure prediction, anomaly detection, forecasting, and optimization signals. The role requires close collaboration with backend, edge, firmware, manufacturing, and product teams.
Responsibilities
- Own the design and implementation of predictive analytics systems for Amperesand’s IoT and energy platforms.
- Build end-to-end ML pipelines including data ingestion, feature engineering, model training, inference, deployment, and monitoring.
- Develop and deploy models for predictive maintenance, anomaly detection, and performance forecasting.
- Collaborate with edge and cloud teams to define where intelligence runs.
- Productionize models with strong focus on reliability, observability, retraining, and explainability.
- Design and integrate AI agentic frameworks enabling systems that reason, plan, and act.
- Partner with product and domain experts to translate operational problems into ML objectives.
- Continuously improve models using real-world feedback loops.
- Document system designs and tradeoffs for cross-functional stakeholders.
Qualifications
- 5+ years of experience in machine learning or applied AI, including production deployments.
- Strong experience with time-series data and predictive modeling.
- Strong programming skills in Python; Go, Java, or Rust is a plus.
- Experience with ML frameworks such as PyTorch, TensorFlow, or scikit-learn.
- Experience building data pipelines using streaming or event-driven systems.
- Cloud and containerization experience (AWS/GCP/Azure, Docker).
- Strong understanding of monitoring, drift detection, and retraining.
- Work experience into defining hardware specifications for training environment.
- Hands-on experience with AI agentic frameworks.
- Proficiency in English and ability to communicate with wider teams.
- Ability to travel up to 20% including internationally.
Bonus Qualifications
- Background in energy engineering systems (power systems, energy storage, power electronics, or controls).
- Experience with IoT, manufacturing, or industrial platforms.
- Exposure to edge AI deployments.
- Knowledge of signal processing or physics-informed ML.
- Experience with MES, digital twins, or asset health monitoring.
- Open source AI agents project contribution.
- Familiarity with MLOps and CI/CD workflows.
- Experience working with hardware, firmware, or operations teams.