DescriptionMachine Learning Operations 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 130+ 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 MLOps Software Engineer to join our US team based in Austin, Texas. This role will take ownership of the Analytical foundation that powers our trading and analytics operations. Your primary focus will be the integrity, reliability, and long-term institutionalization of our most critical models with a particular emphasis on forecasting, optimization, financial engineering, and analytical workflows. You will also play a key supporting role in cross-functional work with our Quantitative and Applied Analytics teams to enhance modeling capabilities for front office objectives.
You will be responsible for:
- Trading Model Deployment: Take ownership of productionising complex convex optimization models and fundamental market forecasts. You will partner closely with researchers and traders to translate market hypotheses into robust, live systems.
- Forward-Deployed Engineering: Act as the technical bridge between research and core software engineering. You will rapidly prototype solutions on the desk while simultaneously advocating for and implementing scalable engineering practices (version control, testing, performance profiling) within the trading and research teams.
- Research & Data Infrastructure: Build and continuously improve our data engineering tools, backtesting frameworks, and research environments. Champion data quality by ensuring high-fidelity ingress for critical market and fundamental datasets, creating a reliable and shared understanding of data across the trading and technical teams.
- Cross-Functional Execution: Collaborate tightly across Trading, Data Science, and Core Tech to build consensus and ensure our core architecture supports advanced quantitative strategies and rapid iteration.
- Live Desk Support: Provide rapid-response troubleshooting, tooling creation, and escalation support for live trading applications and models. Please note this role includes an out-of-hours escalation component.
- Mentorship & Leadership: Mentor more junior team members and provide regular guidance on technical skills, working practices, and career development.
- Security & Architecture: Think holistically about security, efficiency, scalability, and operational impact when designing solutions, while maintaining proactive defense against external threats.
RequirementsPreferred Skills & Experience
- 3+ years in MLOps, ML Engineering, Data Engineering, or closely related roles building and running ML/data pipelines.
- Strong Python data and scientific stack experience, including tools such as Polars/Pandas, PyArrow, PySpark, NumPy/SciPy.
- Experience integrating quantitative or ML models into scalable, production grade code.
- Hands-on experience with MLOps and orchestration tooling such as MLFlow, Ray, Prefect, or Airflow.
- Practical CI/CD experience for ML/data services using Git-based workflows.
- Experience working in AWS or similar cloud environments, including running containerized ML or data workloads in Kubernetes.
Nice to Have
- Exposure to US Power or financial markets, particularly automated trading or forecasting.
- Experience developing production trading systems or high-availability software
- Demonstrated experience working with timeseries data, ideally including financial market-derived signals.
- Experience building batch and streaming pipelines (Kafka, Debezium, Spark, Flink) for CDC and real-time ingestion.
- Familiarity with modern data stack tooling: open table formats (Iceberg), compute engines (Spark, Trino, Snowflake), and advanced SQL.
- Experience managing distributed data systems or Kubernetes clusters in production.
- Optimization experience, especially linear programming and mixed-integer programming.
- Understanding of time-series forecasting and integration of GenAI/LLMs into quantitative workflows.
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, we’ll 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 on our company website: https://habitat.energy/privacy-policy/.
