Sr. Controls Engineer

Description

We are seeking an experienced Senior Staff Controls Engineer with deep expertise in HVAC optimization, building thermodynamics, and scalable data‑driven control systems, along with strong capabilities in distributed energy resource (DER) optimization, including solar PV, battery energy storage, and flexible load management.

The ideal candidate is a technical leader who understands the complexity and variability of HVAC systems and building thermal dynamics and can apply that expertise to broader energy optimization challenges. You have a proven track record of developing advanced control algorithms and successfully taking them from concept and modeling through prototyping and commercial deployment, supporting performance across diverse field environments.

In this role, you will help drive the development of next‑generation building and distributed energy optimization solutions that integrate HVAC control with onsite generation and energy storage into cohesive, scalable systems.

Open to candidates based in San Francisco Bay Area or Seattle, WA
On site role with presence aligned to local team cadence and needs

HVAC & Building Optimization

• Architect, design, and implement optimization and control algorithms for HVAC systems incorporating component‑level modeling, psychrometric relationships, and whole‑building thermodynamic modeling.
• Build scalable, adaptive models capable of generalizing across heterogeneous HVAC architectures including VAV, VRF, AHU‑centric systems, heat pumps, chillers, and boilers.
• Develop hybrid physics‑based and machine learning models to predict building loads, thermal storage behavior, and equipment performance under uncertainty.
• Design automated tuning and commissioning frameworks enabling rapid deployment across diverse building environments with minimal manual configuration.

Distributed Energy Resource Optimization (Solar + Storage + Loads)

• Develop control strategies that co‑optimize building HVAC loads with onsite solar PV and battery energy storage systems (BESS).
• Build integrated energy optimization models coordinating HVAC flexibility with battery dispatch, solar self‑consumption, and grid price signals.
• Implement forecasting‑informed predictive control strategies that jointly optimize thermal dynamics, PV generation variability, and storage constraints.
• Enhance building‑level and portfolio‑level optimization engines to maximize economic value through demand charge reduction, TOU arbitrage, and grid service participation.

Productization & Deployment

• Lead the full lifecycle of control products from algorithm design and simulation through pilot deployment and scaled commercial rollout.
• Ensure optimization algorithms are computationally efficient, robust, and fault‑tolerant across thousands of deployed assets.
• Partner with software and cloud engineering teams to integrate control logic into distributed real‑time platforms.

Field Analytics & Continuous Improvement

• Analyze operational data from deployed systems to diagnose performance issues and refine control models.
• Improve optimization outcomes through continuous model iteration and performance monitoring.
• Develop monitoring, diagnostics, and automated issue detection tools for HVAC systems, PV generation, battery systems, and integrated energy operations.

Leadership & Cross‑Functional Collaboration

• Work closely with mechanical engineers, data scientists, cloud architects, product managers, and field operations teams.
• Mentor junior engineers and contribute to the organization’s long‑term strategy for scalable building and energy optimization systems.
• Influence the technical roadmap for advanced building energy control and distributed energy integration.

• Master’s or PhD in Mechanical Engineering, Control Engineering, Electrical Engineering, Computer Science, or related field.
• PhD + 5 years OR Master’s + 7 years of experience in advanced control system design, HVAC optimization, building energy modeling, or distributed energy resource optimization.
• Demonstrated track record of taking control algorithms from research through production deployment.
• Ex…