Controls & QoreAI Engineer

EQORE Inc.; Somerville (MA); Presencial; USD 140.000 - 200.000; Jornada completa

Descripción de la vacante

EQORE Inc. in Somerville, MA is seeking a hands-on engineer to improve QoreAI dispatch across demand charges, tariffs, and grid programs.

You’ll own major parts of the stack—from forecasts to dispatch to the tooling that proves an improvement is real—while keeping the fleet safe as it scales. You’ll build load and event forecasts, refine optimization baselines, and implement simulation and shadow-mode tooling to catch regressions before customers notice.

Formación

• Hands-on experience building control, optimization, or forecasting systems that ran on real data.
• Strong Python and the ability to own work from problem formulation through deployment.
• Solid grounding in optimization (LP/MILP, MPC) or reinforcement learning, ideally both.
• Experience with safe deployment and rigorous evaluation in closed-loop systems.
• An ownership mindset: you own outcomes, not tasks.

Responsabilidades

• Improve QoreAI dispatch for peak shaving, ConnectedSolutions, Clean Peak, ICAP, and other programs.
• Build load and event forecasts and use them in control decisions.
• Maintain strong optimization baselines and safe fallbacks, and measure every new approach against them.
• Build simulation, backtesting, shadow-mode, and release tooling that catches regressions before they reach customers.
• Generalize controllers across sites, tariffs, operating patterns, and hardware.
• Monitor live performance, diagnose problems, and fix them.

Conocimientos

Python

Optimization

Forecasting

Reinforcement learning

Herramientas

JAX

PyTorch

Descripción del empleo

Improve how QoreAI dispatches batteries across demand charges, tariffs, and grid programs, and turn better control into measurable savings across a growing fleet.

About the role

QoreAI decides when every EQORE battery charges and discharges, juggling demand charges, tariffs, and grid-program events. Each decision is money on a customer’s bill. As the fleet grows across more sites, utilities, and programs, the control problem gets harder: more uncertainty, more constraints, more at stake. This role exists to make QoreAI better and keep it safe while the fleet multiplies.

You’ll work alongside the team that built QoreAI and take ownership of major parts of the stack, from forecasts to dispatch to the tools that prove a change is an improvement.

What you’ll do

• Improve QoreAI dispatch for peak shaving, ConnectedSolutions, Clean Peak, ICAP, and other programs.
• Build load and event forecasts and use them in control decisions.
• Maintain strong optimization baselines and safe fallbacks, and measure every new approach against them.
• Build simulation, backtesting, shadow-mode, and release tooling that catches regressions before they reach customers.
• Generalize controllers across sites, tariffs, operating patterns, and hardware.
• Monitor live performance, diagnose problems, and fix them.

What you’ll bring

• Hands-on experience building control, optimization, or forecasting systems that ran on real data.
• Strong Python and the ability to own work from problem formulation through deployment.
• Solid grounding in optimization (LP/MILP, MPC) or reinforcement learning, ideally both.
• Experience with safe deployment and rigorous evaluation in closed-loop systems.
• An ownership mindset: you own outcomes, not tasks.

Nice to have

Reinforcement or imitation learning, JAX or PyTorch, energy or battery systems, robotics, industrial control, probabilistic forecasting, or MLOps.

Why EQORE

The future of energy is distributed. Commercial and industrial customers consume more than 65% of America’s electricity, yet less than 1% of recent battery deployments have gone to C&I sites. We are building the engine that closes that gap: not just the software that manages batteries, but the machine that sells, finances, and deploys them across the United States. Our goal is to become the largest deployer and manager of C&I energy resources in the world.

Your controller runs every battery we deploy, and its results show up on customer bills. Each improvement compounds across the whole fleet.