Senior Machine Learning Engineer

We are looking for a Machine Learning Engineer who is technically proficient in programming to build machine learning systems and maintain high performing machine learning models in production. Designing machine learning systems requires that you, as the machine learning engineer, is able to frame business problems and design machine learning solutions that can effectively bring value to the company. You will prototype, implement and experiment with machine learning methods to solve business use cases. You will lead efforts to automate, architect and optimise machine learning processes and workflows. You will also help define, execute and enforce best practices for test-driven model development processes for the entire Data Science team in MoneyLion.

Responsibilities

Own and lead the end-to-end architecture, orchestration, and delivery of machine learning systems and pipelines, from problem framing to production; Lead technical discussions with MLOps Engineers and Data Scientists to drive sound system design decisions; Research, build and design robust, scalable machine learning systems and models that deliver measurable business value; Design, execute and monitor machine learning model experiments and metrics, drive conclusions and recommendations independently; Monitor, optimise and maintain machine learning solutions in production, proactively identify and resolve systemic issues; Champion and enforce test-driven development and engineering best practices to reduce technical debt across ML systems; Mentor junior ML engineers, providing guidance, code review feedback, and supporting their technical growth

Requirements

Proven track record of developing, deploying and owning machine learning models in production at scale; Adept at problem solving and troubleshooting using both textbook methods and novel viewpoints, able to navigate ambiguity independently; Proficient in Python and SQL; Proficient with machine learning libraries and frameworks; Solid experience building machine learning systems, workflows and pipelines end-to-end; Hands-on experience with AWS, Docker, Kubernetes; Strong communication, collaboration, and stakeholder management skills; Demonstrated ability to mentor others and contribute to team-wide engineering culture

Nice to have

Hands-on experience with scikit-learn, XGBoost, or LightGBM; Familiarity with MLOps tools such as DVC, MLFlow, Metaflow, Seldon, or BentoML

Benefits

Flexible working options; Time off; Competitive pay; Benefits; Well-being programs