Machine Learning Engineer — United States

Location: United States (fully remote)

Base salary: $180,000–$200,000, plus bonus and equity

About The Company

Harnham is representing an opportunity with an organization operating at the intersection of data, commerce, and risk. The company develops advanced machine learning systems to address complex transactional challenges, including fraud prevention. Its platform processes high volumes of transactions in real time, using artificial intelligence to help optimize customer experiences while managing risk.

Machine learning is central to the organization’s products and growth. The AI Lab is a close-knit team of approximately 15 people, where engineers contribute to both production systems and research. The culture values scientific thinking, creativity, and engineering rigor. Teams are encouraged to own their services end to end and to build solutions with measurable business impact.

About The Role

As a Machine Learning Engineer, you will build and scale production-grade systems for real-time decision-making. You will take ownership of machine learning projects throughout their lifecycle, from understanding a business challenge and designing experiments to developing, deploying, and improving models in production.

This role combines hands-on engineering with applied research. You will develop models that influence commercial outcomes at scale, explore techniques that can improve model performance, and help strengthen the systems and pipelines that support experimentation. You will partner with Product, Engineering, and Risk colleagues to translate business needs into practical, scalable machine learning solutions. This is an opportunity to work on consequential systems where model quality, reliability, and performance matter.

Qualifications

• Commercial experience building and deploying machine learning models, including the ability to develop solutions from first principles.

• Proficiency in Python and SQL for data processing, analysis, experimentation, and model development.

• Hands-on experience with distributed computing environments, such as Apache Spark.

• Experience designing, conducting, and evaluating experiments rather than relying solely on pre-built models or third-party APIs.

• Ability to translate ambiguous business problems into well-defined machine learning approaches and scalable technical solutions.

• Sound engineering judgment and an understanding of the considerations involved in putting models into production.

• Clear communication skills and the ability to collaborate effectively with technical and non-technical partners.

Responsibilities

• Design, implement, deploy, and improve machine learning models that support real-time decision-making.

• Build and optimize scalable machine learning pipelines that enable efficient development and rapid experimentation.

• Investigate and integrate relevant machine learning techniques to improve model accuracy, scalability, and production performance.

• Work across the full model lifecycle, from data preparation and experimentation to deployment, monitoring, and iteration.

• Collaborate with Product, Engineering, and Risk teams to define problems, assess trade-offs, and deliver useful outcomes.

• Own projects end to end, taking accountability for technical quality and results in production.

• Contribute to applied machine learning research and share findings with the AI Lab.

Benefits

The compensation package includes a base salary of $180,000–$200,000, depending on experience, along with an annual bonus and equity. The position is fully remote within the United States. Additional benefits include a four-day working week, flexible working arrangements, and unlimited paid time off. The organization also offers generous parental leave and learning and development support. You will have the opportunity to contribute to high-scale systems with meaningful commercial impact and to work alongside a focused AI Lab team.

Equal Opportunity

Harnham and the organization it represents are committed to providing a professional and inclusive hiring process. Candidates will be considered based on their relevant skills, experience, and ability to perform the role. Applicants from a wide range of backgrounds are encouraged to apply. Reasonable accommodations during the recruitment process can be discussed upon request.