ML Ops Engineer
Rollenübersicht von JobGrid
ML Ops Engineer at Habitat Energy: Austin, Vereinigte Staaten; Hybrid; Vollzeit; Senior; IT. JobGrid adds normalized role facts, source context, and a path to the employer application page so candidates can compare the listing before applying.
- Location and workplace: Austin, Vereinigte Staaten, Hybrid
- Role classification: IT, Data Science & ML, Vollzeit, Senior
- Source freshness: checked by JobGrid on 2026-05-31.
- Application path: candidates continue to the employer application page with non-personal referral tags.
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.