Staff Machine Learning Engineer We are seeking a Staff MLE to work onsite in Boston minimum 3 days a week, per company requirement. Please apply if you are a local candidate. The Role We are looking for a Machine Learning Engineering Lead to build and scale our machine learning capabilities. This person will be responsible for designing, developing, and deploying machine learning systems that directly impact core product functionality. As one of the early ML leaders at the company, you will work closely with engineering, product, and data teams to define the roadmap for machine learning and establish best practices for model development and deployment. This role is hands-on, with the opportunity to shape the long-term ML architecture and help build the team over time. Responsibilities • Lead the design and development of machine learning models that power core fintech products such as risk scoring, fraud detection, personalization, or financial forecasting • Build and maintain scalable ML pipelines for data ingestion, training, evaluation, and deployment • Collaborate with product and engineering teams to integrate machine learning into customer-facing features • Establish best practices for experimentation, model monitoring, and model lifecycle management • Design the architecture for ML infrastructure and data pipelines as the company scales • Mentor engineers and contribute to the growth of the ML function • Stay current with advancements in applied machine learning and fintech data applications Qualifications • 6-10+ years of experience in machine learning engineering, applied ML, or related fields • Strong programming experience in Python • Experience with ML frameworks such as PyTorch, TensorFlow, or Scikit-learn • Experience deploying machine learning models into production environments • Strong experience working with large datasets and building data pipelines • Ability to operate in a fast-paced startup environment with high ownership Preferred Qualifications • Experience in fintech, payments, lending, or financial risk modeling • Experience building models for fraud detection, credit risk, or transaction monitoring • Experience with cloud platforms such as AWS, GCP, or Azure • Familiarity with real-time data systems and event-driven architectures • Experience helping build or scale ML teams