Come build at the intersection of AI and fintech. At Ocrolus, we’re on a mission to help lenders automate workflows with confidence—streamlining how financial institutions evaluate borrowers and enabling faster, more accurate lending decisions.

Our AI-powered data and analytics platform is trusted at scale, processing nearly one million credit applications every month across small business, mortgage, and consumer lending. By integrating state-of-the-art open- and closed-source AI models with our human-in-the-loop verification engine, Ocrolus captures data from financial documents with over 99% accuracy. Thanks to our advanced fraud detection and comprehensive cash flow and income analytics, our customers achieve greater efficiency in risk management, and provide expanded access to credit—ultimately creating a more inclusive financial system.

Trusted by more than 400 customers—including industry leaders like Better Mortgage, Brex, Enova, Nova Credit, PayPal, Plaid, SoFi, and Square—Ocrolus stands at the forefront of AI innovation in fintech. Join us, and help redefine how the world’s most innovative lenders do business.

Summary

As a Staff Machine Learning Engineer at Ocrolus, you’ll be a hands-on technical leader who helps shape the future of our machine learning systems. This is a high-impact role, entailing strategic responsibility in determining the company’s Machine Learning infrastructure, system architecture, and deployment protocols. You will collaborate across teams to design, scale, and refine models that power core features — from document understanding and OCR to complex NLP and decision intelligence. This role involves the design of scalable Machine Learning solutions, mentorship of engineering personnel, and contribution to the technical and organizational advancement of the AI stack. The ideal candidate will excel in addressing complex challenges, providing guidance to others, and spearheading innovation on a large scale.

What you’ll do:

• Spearhead the Design and Architecture: Lead the design and architecture of robust, scalable machine learning systems that are primed for seamless deployment into production.
• Enhance Productivity: Design and implement essential machine learning infrastructure and tools that support multiple teams, streamlining workflows and improving efficiency across the organization
• Solve Complex Infrastructure and ML Problems: Address complex infrastructure and machine learning challenges that span the organization. Analyze systems to identify and rectify bottlenecks, inefficiencies, and areas for improvement.
• Drive Model Evaluation and Optimization: Lead the development of model evaluation frameworks, optimize data pipelines, and implement continuous training strategies to ensure that models remain accurate and up-to-date.
• Apply ML Expertise to Fintech: Leverage state-of-the-art machine learning models within the fintech domain to automate and enhance document processing.
• Collaborate Across Teams: Work closely with stakeholders from Product, Engineering, and Operations to ensure that goals are aligned and that execution is coordinated and effective.
• Mentor and Guide Engineers: Provide mentorship to engineers within both ML and platform teams, fostering their professional development and contributing to the overall growth of Ocrolus’ technical expertise. Coach and influence others to improve company culture.
• Contribute to Engineering Standards: Play an active role in shaping Ocrolus-wide engineering standards, participate in design reviews (RFCs/ADRs), and promote adherence to best practices. Champion Code Quality and Reliability: Be a vocal advocate for code quality, observability, and system reliability. This includes everything from implementing rigorous A/B testing to setting up real-time monitoring systems.
• Understand how their team and projects fit into the larger business goals. Bring together technical and nontechnical stakeholders towards common objectives, suggest alternative solutions to customer problems, and help teach and support more junior teammates.
• Look for opportunities for process improvements within their team and works with others to implement process changes.
• Find ways to incorporate company values into day-to-day decisions and have ideas on how to build policies/processes that support the improvement of company culture.

Who we’re looking for: (Skill Sets and Qualifications)

• Bachelor’s or Master’s degree in Computer Science, Machine Learning, Applied Mathematics, or a related technical field; PhD preferred.
• 7+ years of experience developing and deploying machine learning models in production environments, with a focus on real-world applications and measurable impact.
• Deep expertise in Python and at least one major ML framework (e.g., PyTorch, TensorFlow); strong proficiency in building, training, and optimizing deep learning models.
• Proven experience in applying ML techniques to computer vision, OCR, or NLP problems, ideally at scale and in latency-sensitive environments.
• Strong understanding of ML system design, including model evaluation, A/B testing, continuous training, and monitoring in production.
• Solid engineering fundamentals — data structures, system design, version control, and testing — with a history of writing clean, maintainable, and scalable code.
• Experience with modern infrastructure tools and cloud platforms (Docker, Kubernetes, Helm, AWS/GCP); comfortable navigating MLOps pipelines and deployment workflows.
• Demonstrated ability to lead cross-functional initiatives, influence architectural decisions, and communicate complex technical ideas to diverse stakeholders.
• Experience mentoring engineers and fostering a culture of high standards, curiosity, and ownership.
  
  

Preferred Attributes:

• Working familiarity with additional programming languages (e.g., Go, Java, or Scala) is a plus.
• Experience operating within regulated industries (fintech, healthtech, etc.).
• Active contributor to open source, research publications, or public tech community.
• Champions a culture of humility, curiosity, and ownership in technical decision-making.

Note:

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