ML Ops Engineer

Habitat Energy•Austin, TX

1d•Hybrid

About The Position

Habitat Energy is a fast-growing technology company focused on the physical and financial optimization of energy storage and renewable generation assets globally through complex models and trading. By maximizing 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 a Machine Learning 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.

Requirements

3+ years in MLOps, ML Engineering, Data Engineering, or closely related roles building and running ML/data pipelines.
Strong Python data and ML stack experience, including tools such as Polars/Pandas, PyArrow, PySpark, NumPy/SciPy.
Experience integrating models built with frameworks such as PyTorch, TensorFlow, or Keras into scalable pipelines.
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 Haves

Exposure to US Power or financial markets, particularly automated trading or forecasting.
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.

Responsibilities

Operationalize trading algorithms into reliable, distributed workflows covering feature extraction, training, evaluation, inference, and model lifecycle management.
Bring structure, repeatability, and engineering best practices to an evolving applied research environment.
Build the tooling and platforms that enable the data science team to scale model development and deployment.
Optimize automated trading systems across power, forecasting, and portfolio management stacks.
Define architectural standards and select scalable, cloud-native toolchains aligned with long-term technology strategy.
Engineer solutions for distributed training and large-scale data processing.