Habitat Energy
Job Description
Job Description Salary: Optimization Engineer
Habitat Energy is a fast growing technology company focussed on the physical and financial optimisation of energy storage and renewable generation assets globally through complex models and trading. By maximising the returns from these assets we aim to drive investment in renewable energy and accelerate the transition to a low carbon world. Our rapidly growing team of 110+ people in Austin, TX, Oxford, UK, and Melbourne, Australia brings together exceptionally talented and passionate people in the domains of energy trading, data science, software engineering and renewable energy management.
We have a vacancy for an Optimization Engineer to join our US Data Science team based in Austin, Texas reporting to the Optimization Lead. This role will drive the development and implementation of advanced optimization models, focusing on storage (BESS), trading (DART), portfolio optimization, and congestion management across US power markets.
Your responsibilities will include: Optimization Model Development and Maintenance Collaboratively design and implement optimization models for energy storage as part of the Data Science team, focusing on storage + renewable systems, to maximize portfolio performance and integrate market dynamics across ISOs. Deployment and Integration Partner with Software Engineering and DevOps to deploy scalable, production-ready optimization models. Performance Tracking & Iteration Continuously evaluate model performance, enhance decision tools, and adapt to a rapidly changing market environment.
Must have skills and experience: Proficiency in Python and its wider numerical ecosystem (Pandas, NumPy, Polars, etc.), with expertise in optimization frameworks such as CVXPY, Pyomo, and Gurobipy. Ability to collaborate across trading, product, and project teams to guide product vision, roadmaps, and delivery. BA/BSc degree in Computer Science, Machine Learning, Electrical Engineering, Operations Research, or related technical field. 3+ years of commercial experience building and deploying optimization or ML systems into production. Experience with linear, MILP, and nonlinear optimization techniques in an applied setting. Experience building algorithmic decision tools for real-time model predictive control of physical systems. Experience developing scalable backtesting and simulation frameworks to evaluate models and support rapid iteration. Experience with the full software stack, including AWS, Docker,Terraform, Git/version control, CI/CD pipelines, orchestration (Airflow/Prefect), monitoring & alerting frameworks, and modern data infrastructure.
Nice to have skills and experience: Electricity & energy domain modeling (e.g. power systems modeling, power flow, security constrained unit commitment, etc.) Experience with quantitative analysis and modeling of short-term US power markets (ideally ERCOT) in a trading or market-facing environment. Familiarity with the economic and physical operation of wholesale Battery Energy Storage Systems (BESS). Experience with forecasting & time series problems, and familiarity with ML frameworks in Python (Scikit-learn, PyTorch, XGBoost, etc.) Experience with data visualization and dashboarding technologies (e.g. plot.ly, Dash, Streamlit, Superset) MS or PhD degree in Computer Science, Machine Learning, Operations Research, or related technical field. Ultimately we are looking for someone who is a great fit for our company so we encourage you to apply even if you may not meet every requirement in this posting. We value diversity and our environment is supportive, challenging and focused on the consistent delivery of high quality, meaningful work. In return, well give you a competitive salary, flexible working arrangements and a lot of personal development opportunities. We operate a hybrid working model with at least 2 days in our office in Austin.
When you apply for a job with us, we process some of your personal information. You can find out more about how we process your information in our candidate privacy policy, here.
Job Description Salary: Optimization Engineer
Habitat Energy is a fast growing technology company focussed on the physical and financial optimisation of energy storage and renewable generation assets globally through complex models and trading. By maximising the returns from these assets we aim to drive investment in renewable energy and accelerate the transition to a low carbon world. Our rapidly growing team of 110+ people in Austin, TX, Oxford, UK, and Melbourne, Australia brings together exceptionally talented and passionate people in the domains of energy trading, data science, software engineering and renewable energy management.
We have a vacancy for an Optimization Engineer to join our US Data Science team based in Austin, Texas reporting to the Optimization Lead. This role will drive the development and implementation of advanced optimization models, focusing on storage (BESS), trading (DART), portfolio optimization, and congestion management across US power markets.
Your responsibilities will include: Optimization Model Development and Maintenance Collaboratively design and implement optimization models for energy storage as part of the Data Science team, focusing on storage + renewable systems, to maximize portfolio performance and integrate market dynamics across ISOs. Deployment and Integration Partner with Software Engineering and DevOps to deploy scalable, production-ready optimization models. Performance Tracking & Iteration Continuously evaluate model performance, enhance decision tools, and adapt to a rapidly changing market environment.
Must have skills and experience: Proficiency in Python and its wider numerical ecosystem (Pandas, NumPy, Polars, etc.), with expertise in optimization frameworks such as CVXPY, Pyomo, and Gurobipy. Ability to collaborate across trading, product, and project teams to guide product vision, roadmaps, and delivery. BA/BSc degree in Computer Science, Machine Learning, Electrical Engineering, Operations Research, or related technical field. 3+ years of commercial experience building and deploying optimization or ML systems into production. Experience with linear, MILP, and nonlinear optimization techniques in an applied setting. Experience building algorithmic decision tools for real-time model predictive control of physical systems. Experience developing scalable backtesting and simulation frameworks to evaluate models and support rapid iteration. Experience with the full software stack, including AWS, Docker,Terraform, Git/version control, CI/CD pipelines, orchestration (Airflow/Prefect), monitoring & alerting frameworks, and modern data infrastructure.
Nice to have skills and experience: Electricity & energy domain modeling (e.g. power systems modeling, power flow, security constrained unit commitment, etc.) Experience with quantitative analysis and modeling of short-term US power markets (ideally ERCOT) in a trading or market-facing environment. Familiarity with the economic and physical operation of wholesale Battery Energy Storage Systems (BESS). Experience with forecasting & time series problems, and familiarity with ML frameworks in Python (Scikit-learn, PyTorch, XGBoost, etc.) Experience with data visualization and dashboarding technologies (e.g. plot.ly, Dash, Streamlit, Superset) MS or PhD degree in Computer Science, Machine Learning, Operations Research, or related technical field. Ultimately we are looking for someone who is a great fit for our company so we encourage you to apply even if you may not meet every requirement in this posting. We value diversity and our environment is supportive, challenging and focused on the consistent delivery of high quality, meaningful work. In return, well give you a competitive salary, flexible working arrangements and a lot of personal development opportunities. We operate a hybrid working model with at least 2 days in our office in Austin.
When you apply for a job with us, we process some of your personal information. You can find out more about how we process your information in our candidate privacy policy, here.