Machine Learning Engineer

Harnham; Austin (TX); À distance; USD 180 000 - 200 000; Plein temps

Avantages offerts par ce poste

Bonus & equity

Fully remote (USA)

4-day workweek

Flexible PTO

Parental leave

Learning & development

Résumé du poste

Harnham seeks a high-impact Machine Learning Engineer to build and scale production systems for real-time decisioning. You will join an AI Lab of 15 people and own end-to-end ML pipelines, delivering models that drive commercial outcomes at scale.

Collaborate with Product, Engineering, and Risk teams, deploy scalable pipelines in distributed environments (Spark), and translate business problems into robust ML solutions.

Qualifications

• Proven ability to build and deploy ML models from first principles.
• Experience with distributed computing environments (e.g., Spark).
• Strong Python and SQL for data processing and model development.
• Experience designing and running experiments rather than relying on pre-built models.
• Ability to translate business problems into scalable ML solutions.

Responsabilités

• Design and implement production-grade ML models for real-time decisioning.
• Build and optimise scalable ML pipelines to enable rapid experimentation.
• Explore and integrate new ML techniques to improve model accuracy and scalability.
• Collaborate with Product, Engineering, and Risk teams to deliver end-to-end solutions.
• Own projects end-to-end with accountability for production outcomes.

Connaissances

ML model deployment

Distributed computing (Spark)

Python

SQL

Experimentation design

Outils

Spark

Description du poste

$180,000 to $200,000 base + bonus + equity

A high-impact Machine Learning Engineering role focused on building and scaling production systems for real-time decisioning. You will join an AI Lab of 15 people and own end-to-end ML pipelines, working on models that directly drive commercial outcomes at scale, as well as contribute to ML research within the AI Lab.

The Company

This organisation operates at the intersection of data, commerce, and risk, building advanced machine learning systems to solve complex transactional challenges to combat fraud. Their platform processes high volumes of real-time transactions, using AI to optimise customer experiences while managing risk. Machine learning is central to their product and growth, not a supporting function, giving teams strong visibility across the business.

They foster a culture that values scientific thinking, creativity, and engineering rigour, with teams structured to own their services end to end.

The Role

You will contribute to the development of production-grade machine learning systems, working across the full lifecycle from experimentation through to deployment.

• Designing and implementing machine learning models that drive real-time decisioning


• Building and optimising scalable ML pipelines to enable rapid experimentation


• Exploring and integrating new ML techniques to improve model accuracy and scalability


• Collaborating closely with Product, Engineering, and Risk teams


• Owning projects end to end, with full accountability for outcomes in production

Your Skills and Experience

• Strong commercial experience building and deploying machine learning models from first principles


• Hands-on experience working in distributed computing environments such as Spark


• Proficiency in Python and SQL for data processing and model development


• Experience designing and running experiments rather than relying on pre-built models or APIs


• Ability to translate business problems into scalable machine learning solutions

What They Offer

• Competitive base salary up to $200,000 depending on experience


• Annual bonus / equity package


• Fully remote working within the USA


• 4 day working week, flexible working and unlimited paid time off


• Generous parental leave and learning and development support


• The opportunity to work on high-scale systems with meaningful business impact