Machine Learning Ops Engineer, Habitat Energy

Salary not provided

SQL

Docker

Kubernetes

Python

Postgres

Airflow

Tensorflow

Terraform

Spark

NumPy

Pandas

Scikit-Learn

Prometheus

CUDA

Snowflake

PyTorch

Git

Mid and Senior level

Austin

2 days a week in office

Battery storage and renewable energy optimisers

Be an early applicant

Battery storage and renewable energy optimisers

101-200 employees

FintechB2BMarketplaceEnergyeCommerce

Be an early applicant

Salary not provided

SQL

Docker

Kubernetes

Python

Postgres

Airflow

Tensorflow

Terraform

Spark

NumPy

Pandas

Scikit-Learn

Prometheus

CUDA

Snowflake

PyTorch

Git

Mid and Senior level

Austin

2 days a week in office

101-200 employees

FintechB2BMarketplaceEnergyeCommerce

Company mission

To deliver outstanding returns to our clients to increase the attractiveness of renewable energy globally and support the transition to a clean energy future.

Job

Company

Role

Who you are

The ideal candidate will have excellent Python skills, write clean and robust code, and possess strong communication abilities to foster close collaboration between the Data Science and Software Engineering teams
A natural inclination toward documentation and quality assurance is essential
3+ years of Python experience
3+ years of working with data scientist/ML researchers to develop tooling, collaborate on backtesting frameworks, build data pipelines, build/maintain orchestration workflows or productionise code
BA/BSc degree in Computer Science, Machine Learning, Electrical Engineering, or related technical field
Proficiency with Orchestration and IaC (Airflow, Prefect, Ray, ECS, Kubernetes, Terraform, CloudFormation), Modern data strategies (Iceberg, Trino), Git, Containerization (docker), SQL (Postgres, Snowflake)
You are fluent in Python and its wider numerical ecosystem (Pandas, NumPy, Scikit-learn, Polars, etc.)

Desirable

Data engineering experience collecting, curating, managing and monitoring large timeseries data
Experience building and maintaining distributed systems for data and ML applications using Dask, Trino, Spark, Ray, or similar frameworks
Experience tuning GPU-based applications for performance and cost efficiency using frameworks like CUDA, PyTorch, TensorFlow, or TensorRT, with a deep understanding of their tradeoffs across training, inference, and data processing workload
Experience with monitoring frameworks (Prometheus)
Machine learning experience especially time-series forecasting & generative ML problems
Optimization experience especially linear programming / mixed-integer programming

What the job involves

We have a vacancy for a Machine Learning Operations Engineer to join our US team based in Austin
This role is for a Software Developer embedded within the Data Science team
Implement robust, scalable, and efficient Python code that transforms machine learning and optimization prototypes into production-grade solutions, in line with our SDLC process
Assist in developing and maintaining frameworks for model backtesting and optimization to ensure models perform well in live environments
Provision and development of tooling and infrastructure to enable the running of ML models produced by the data science team, including workflows, platforms and tooling
Contribute to the development and continuous improvement of data engineering tools, workflows, and platforms to ensure smooth deployment and operation of ML models
Enhance systems that manage the flow of forecast and optimization data to ensure seamless integration with operational models and analytics
Transform forecasting and optimization models into production-ready solutions, ensuring they are optimized for live operations, and contribute to continuous optimization and backtesting of models
Contribute to selecting tools and defining architectural standards that align with the company’s goals and technology strategy
Participate in the selection of tools that support scalable data infrastructure for ML workflows and optimization models
Support the integration of ML models into a CI/CD pipeline, automating deployment and ensuring stable operations of models in production

Our take

With the world moving onto more sustainable energy, countries across the globe are aiming to harness the power of green energy alternatives. While sustainable energy production is on the rise, technology and finance need to work in tandem so that solutions are profitable and scalable.

Habitat Energy is combining the latest techniques in machine learning and artificial intelligence to provide customers with battery storage and renewable energy assets that deliver the best possible returns.

Following funding and partnerships, the company has invested capital into increasing its overseas portfolio and in the US market, expanding its product offerings into renewable and hybrid facilities. Already a market leader in the UK, the global expansion is already showing promise with a number of successful collaborations and projects already in the works. This is an exciting time for the company and will sure be one to watch in the coming years.

Kirsty

Company Specialist at Welcome to the Jungle

Company

Funding (2 rounds)

Jun 2020

$0.3m

GRANT

Apr 2019

$1.8m

GRANT

Total funding: $2.1m

Company HQ

City Centre, Oxford, UK

Leadership

Andrew Luers

(Co-Founder & CEO)

Graduated from the University of Oxford with a Doctor of Philosophy in Physics. Also the Co-Founder of ProviderTrust where they serve as a Managing Partner.

Ben Irons

(Co-Founder & CSO)

Previously an Executive Director and Board Member at Aurora Energy Research, Consulting Manager at McKinsey and Company and Strategy Director at Centrica Energy.

Salary benchmarks

We don't have enough data yet to provide salary benchmarks for this role.

Submit your salary to help other candidates with crowdsourced salary estimates.

Share this job

View 3 more jobs at Habitat Energy