🚀 Machine Learning Engineer
📍 Austin, TX (Hybrid/Remote Considered)
💰 $140,000 - $180,000 Base
We're partnering with a fast-growing energy firm looking to hire a Machine Learning Engineer to join a highly technical platform engineering team supporting traders, analysts, and quantitative researchers.
This is not a pure data science role. We're looking for an engineer who enjoys building robust production systems, scaling data and ML infrastructure, and working closely with front-office stakeholders to deliver real business impact.
What you'll be doing:
- Building and maintaining ML and data platforms used for forecasting, optimization, and trading workflows
- Designing scalable cloud-native infrastructure and deployment pipelines
- Productionizing quantitative models and analytics tools
- Developing distributed data and compute systems
- Working directly with traders and business users to deliver reliable solutions
- Driving engineering best practices across CI/CD, observability, testing, and automation
Tech stack includes:
Python | AWS | Kubernetes | Docker | Terraform | Airflow | Spark | MLflow | Databricks | Kafka | CI/CD
We're interested in people from backgrounds such as:
✔ Machine Learning Engineering
✔ MLOps Engineering
✔ Platform Engineering
✔ Software Engineering (with ML/Data exposure)
✔ Quant Development
✔ Infrastructure Engineering
Ideal candidates will have strong Python skills, cloud and DevOps experience, and a track record of building production systems. Experience within energy, trading, forecasting, or quantitative environments is beneficial but not essential.
If you'd like to learn more, please send me a message or apply directly.
They prefer the role to be worked on a hybrid model of 1-2 days a week. Salary offered is $140,000-$180,000.