Machine Learning Engineering Manager

Comity; Chicago (IL); On-site; USD 130,000 - 160,000; Full time

Job summary

Comity is seeking a Machine Learning Engineering Manager to lead a team developing our energy pricing models. This role involves close collaboration with Quantitative Researchers to design and deploy models for power markets, ensuring scalability and impact.

You will have ownership over platform design and research agenda while mentoring ML engineers. A strong foundation in machine learning and experience in financial settings are crucial.

The ideal candidate has 8-12 years of relevant experience and is passionate about team development.

Qualifications

• 8-12 years of experience developing machine learning models in an industry and/or academic setting.
• Experience leading and mentoring ML engineers, software engineers, and/or data scientists.
• Solid grasp of machine learning fundamentals.

Responsibilities

• Lead a team developing energy pricing models and their deployment.
• Work with Quantitative Researchers on models and optimization algorithms.
• Develop data infrastructure and research platforms.

Skills

Machine learning models development

Leading ML engineers

Probabilistic forecasting

Data mining algorithms

Communication skills

Tools

Python

SQL

PyTorch

CatBoost

Job description

Machine Learning Engineering Manager

Comity is looking for a Machine Learning Engineering Manager to lead a team developing our energy pricing models and working on their deployment in market‑leading power contract origination strategies.

In this role, you'll be a hands‑on leader, working alongside your team and closely with our Quantitative Researchers to design and deploy models and optimization algorithms for participating in wholesale power markets, and ensure that they can scale to new markets and greater volume. You will help develop our data infrastructure and research platform, which give our researchers streamlined access to relevant data sources and powerful computing resources, as well as the confidence that their research will translate into real‑world results. As an early hire in this role, you will have broad impact and ownership over our platform and framework design, research agenda, technology choices, and team culture.

Qualifications

• 8-12 years of experience developing machine learning models (or other predictive models) in an industry and/or academic setting.
• Aligned with an onboarding plan that includes some time as an individual contributor to learn our domain and context.
• Experience leading and mentoring ML engineers/researchers, software engineers, and/or data scientists.
• Solid grasp of machine learning fundamentals and ability to read research results to understand operational requirements and assumptions.
• Experience with probabilistic forecasting models for multivariate time series and other structured data. Experience applying ML in financial settings or with graph and/or spatial data is a plus.
• Comfortable with the workflow to research and develop machine learning models and data mining algorithms, and working with distributed computing resources.
• Lifelong learner and empathetic teacher. Passion for growth and development of teammates and strong communication.
• Strong foundations are more important than specific technical knowledge, but experience with any part of our data stack (Python, SQL, PyTorch, CatBoost) is a plus.