Job Details

This range is provided by Franklin Fitch. Your actual pay will be based on your skills and experience — talk with your recruiter to learn more.

Base pay range

$220,000.00/yr - $320,000.00/yr

Direct message the job poster from Franklin Fitch

We're looking for a Director of Machine Learning Engineering to lead the design, deployment, and scaling of ML systems that power the future of fintech. You'll own the strategic direction of our ML roadmap, guide a high-performing engineering team, and partner with product and data leaders to drive innovation across the business. This is a rare opportunity to combine deep technical expertise with organizational leadership in a high-impact, early-stage environment.

What You'll Do

• Define and own the machine learning strategy, aligning it with product, risk, and business objectives
• Lead, mentor, and grow a team of ML engineers and data scientists, fostering a culture of technical excellence and experimentation
• Oversee the full ML lifecycle—from research and prototyping to production deployment, monitoring, and iteration
• Build and scale ML infrastructure and platforms, enabling reproducibility, monitoring, and rapid experimentation
• Partner with executives, product managers, and business stakeholders to identify opportunities where ML provides competitive advantage
• Establish and enforce best practices in MLOps, data governance, and responsible AI (fairness, explainability, compliance)
• Drive continuous improvements in model performance, system reliability, and customer impact
• Represent ML engineering internally and externally, setting the technical vision for how we use AI to reshape fintech

Who You Are

• Proven leader with 7+ years in ML/AI engineering and at least 3+ years in a leadership role
• Strong track record of building, scaling, and leading ML engineering teams in fast-paced environments
• Deep technical expertise in ML modeling, distributed systems, and production-scale deployment
• Proficient in Python, modern ML frameworks, and cloud platforms (AWS/GCP/Azure)
• Experienced in real-time decisioning systems, data pipelines, and large-scale APIs
• Strong understanding of MLOps platforms (MLflow, Kubeflow, Airflow, SageMaker, Vertex AI) and best practices
• Knowledge of fintech-relevant domains

  risk modeling, fraud detection, affordability, and predictive analytics

  
  

• Excellent communicator able to bridge technical and business teams, influencing product direction and company strategy
• Entrepreneurial mindset: thrive in high-ownership, early-stage environments, with the ability to scale both technology and teams

Note: This refinement removes boilerplate and non-relevant postings while preserving the core responsibilities and qualifications for the role.