Learning-Based Controls Engineer for Energy Systems

EQORE Inc.; Somerville, Northern (MA, KY); Hybrid; USD 120,000 - 180,000; Full time

Job summary

EQORE Inc. seeks a hands-on ML engineer to own QoreAI's learning-based control and optimization stack, designing controllers for energy systems and evaluating performance under real-world constraints.

You will build reinforcement learning pipelines in Python, integrate forecasts and market signals, and ensure safe, scalable deployment as the fleet grows across sites and tariffs. You’ll work in person with a small team and contribute to reliable, measurable improvements that customers see on

Qualifications

• Hands-on experience building reinforcement learning systems for continuous control or sequential decision-making.
• Strong Python and experience owning complete reinforcement learning training and evaluation pipelines.
• Strong command of deep RL fundamentals, including policy optimization, exploration, credit assignment, stability, and sample efficiency.
• Experience designing environments and rewards around noisy real-world data, partial observability, and operational constraints.
• Understanding of safe deployment, failure modes, and rigorous evaluation in closed-loop systems.

Responsibilities

• Own QoreAI's learning-based control and optimization architecture.
• Design and evaluate control approaches for battery operation under uncertainty and real-world constraints.
• Develop, train, tune, and evaluate learning-based control policies against strong heuristic and optimization baselines.
• Build simulation, offline evaluation, backtesting, shadow-mode, and production-validation systems.
• Integrate forecasts and market signals produced with the Energy Markets engineer into control inputs and evaluation environments.
• Support ConnectedSolutions, Clean Peak Standard, ICAP, and other programs.
• Generalize controllers across sites, tariffs, grid programs, operating regimes, and hardware.
• Monitor live performance, diagnose regressions and distribution shift, and build safe fallbacks.

Skills

Reinforcement learning

Python programming

RL training pipelines

Closed-loop control

Policy optimization

Tools

PyTorch

JAX

Job description

EQORE Inc. seeks a hands-on ML engineer to own QoreAI's learning-based control and optimization stack, designing controllers for energy systems and evaluating performance under real-world constraints.

You will build reinforcement learning pipelines in Python, integrate forecasts and market signals, and ensure safe, scalable deployment as the fleet grows across sites and tariffs. You’ll work in person with a small team and contribute to reliable, measurable improvements that customers see on