About Our CompanyEquilibrium Energy is a well-funded, Series A clean energy startup backed by some of the most prominent institutional investors in climate. We are building a digital native power company operating at the intersection of grid variability, market volatility, economic optimization, commercial structuring, and risk management, across the end-to-end power value chain. Our mission is to accelerate our collective path to climate, energy, and societal equilibriums. Our goal is to become one of the next-generation, digital-native, end-to-end global clean power companies that reshapes the energy industry.
New colleagues will share our vision that a next-generation energy company must be built from the ground up on deep industry expertise combined with an unwavering commitment to modern digital approaches. We design our commercial strategies, operational approaches, and product suites so as to best leverage data-driven insights, automated workflows, ML-infused pipelines, and fully automated decision engines. These capabilities are enabled by our progressively modern software stack and engineering best practices, which in turn provide the scalable platform we need to put a serious dent in carbon emissions. We're looking for collaborative, talented, passionate and resourceful folks to join our team and help us lay the foundation for our important mission and ambitious plan.
What We Are Looking ForEquilibrium was founded with a vision for building a company where innovation, collaboration, machine learning, data science, and software engineering drive all aspects of our algorithmic decision-making. We are looking for
ML engineers of all levels who are passionate to stay at the forefront of AI/ML technology and cloud engineering, leveraging cloud services, data science platforms, and automation pipelines for the operationalization of AI/ML-driven solutions at an enterprise scale.
What You Will Do- Design, build, implement, and support the ML Engineering platforms and tools underpinning our massive-scale ML pipelines and models
- Design and implement ML toolchains and data platforms to scale ML solutions in production
- Improve the ML infrastructure, software, and tooling that underpin uses these models
- Build the libraries and frameworks that support our large-scale, complex ML pipelines
- Design, build, implement and support production ML pipelines and models
- Define and operate data handling routines to supply training data to training jobs
- Version the training data and record data, trained models, and quality parameters of these models
- Re-train and re-deploy models based on quality parameters collected in continuous monitoring
- Define and monitor quality parameters for ML models in production
- Shape and operate best practices for managing models in production
- Maintain a high-quality standard for all ML models in production
- Collaboratively innovate across a multi-functional technical team
- Proactively communicating with other team members and project stakeholders
- Work closely with data science teams to take over newly developed models into production, and advise data science teams if model redesign should be considered
- Contribute to and help improve the value addition of our computational science efforts
- Bring your data and ML science knowledge to bear in helping our computational science teams identify new techniques, tools, and approaches to improve the value add of EQ's ML models
- Participate in cutting-edge research in artificial intelligence and machine learning applications
The Minimum Qualifications You'll Need- A commitment to clean energy and combating climate change.
- Proficiency in software development with several years of experience, especially in Python.
- Hands-on experience with AWS cloud technologies (like API Gateway, ECS/EKS/Fargate).
- Familiarity with automated deployment and orchestration tools such as CI/CD, Docker, Metaflow, Feature Store, and Temporal.
- Proven experience in applying machine learning techniques to industrial datasets.
- Comprehensive understanding of data science QA methodologies and tools.
- Prior experience in operationalizing machine learning workflows.
- Agility in working with cross-functional teams and adapting to new work methodologies.
- Familiarity with agile practices, or a willingness to learn.
- Strong communication capabilities.
Nice To Have Additional Skills- PhD in computer science or a Bachelor's/Master's degree in computer science or machine learning
- Familiarity with programming in Golang and Julia.
- Experience in time series forecasting.
- Expertise in probabilistic forecasting.
- Background in the energy and power systems sector.
What We OfferEquilibrium is composed of deeply knowledgeable industry experts across all our functions, with decades of experience in energy-specific commercial structuring, power systems engineering, machine learning, computational research, operations research, distributed and compute-intensive infrastructure, and modern software & ML engineering. Our experience in the space means we've previously built versions of nearly every technical component of our platform. We are now designing them better, and combining them in a holistic and novel way, to achieve global scale and climate impact. We pride ourselves on our deeply empathetic & collaborative culture, honest and direct but respectful communication, and our balanced, flexible, and remote-first work environment.
Employee benefits include:
- Competitive base salary and a comprehensive medical, dental, vision, and 401k package
- Opportunity to own a significant piece of the company via a meaningful equity grant
- Unlimited vacation and flexible work schedule
- Ability to work remotely from anywhere in the United States & Europe, or join one of our regional hubs in Boston, SF Bay Area, or London
- Accelerated professional growth and development opportunities through direct collaboration and mentorship from leading industry expert colleagues across energy and tech
Equilibrium Energy is a diverse and inclusive, equal opportunity employer that does not discriminate on the basis of race, gender, nationality, sexual orientation, veteran status, disability, age, or other legally protected status. 
