Habitat Energy is hiring: ML Ops Engineer in Austin
Habitat Energy, Austin, TX, United States
Machine 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 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.
You will be responsible for:
Software Development Lifecycle (SDLC) MLOps Ownership: Operationalize trading algorithms into reliable, distributed workflows covering feature extraction, training, evaluation, inference, and model lifecycle management.
Applied Research Integration: Bring structure, repeatability, and engineering best practices to an evolving applied research environment.
Forecasting & Optimization Capability Development ML Infrastructure: Build the tooling and platforms that enable the data science team to scale model development and deployment.
Execution Systems: Optimize automated trading systems across power, forecasting, and portfolio management stacks.
Tool Selection & Architectural Standards Architecture & Toolchain: Define architectural standards and select scalable, cloud-native toolchains aligned with long-term technology strategy.
Distributed ML Systems: Engineer solutions for distributed training and large-scale data processing.
Preferred Skills & Experience 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 Have 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.
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.
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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.
You will be responsible for:
Software Development Lifecycle (SDLC) MLOps Ownership: Operationalize trading algorithms into reliable, distributed workflows covering feature extraction, training, evaluation, inference, and model lifecycle management.
Applied Research Integration: Bring structure, repeatability, and engineering best practices to an evolving applied research environment.
Forecasting & Optimization Capability Development ML Infrastructure: Build the tooling and platforms that enable the data science team to scale model development and deployment.
Execution Systems: Optimize automated trading systems across power, forecasting, and portfolio management stacks.
Tool Selection & Architectural Standards Architecture & Toolchain: Define architectural standards and select scalable, cloud-native toolchains aligned with long-term technology strategy.
Distributed ML Systems: Engineer solutions for distributed training and large-scale data processing.
Preferred Skills & Experience 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 Have 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.
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.
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