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Machine Learning Engineer


New York City, NY, US
  • Job Type: Full-Time
  • Function: Data Science
  • Industry: Enterprise SaaS
  • Post Date: 08/02/2022
  • Website:
  • Company Address: 48 Wall Street Suite 701, , New York, NY, 10005

About WorkFusion

WorkFusion is the leading provider of Intelligent Automation solutions for Fortune 500 enterprises, banks, insurance, and financial services companies. Our AI-enabled digital workers augment traditional teams by performing highly skilled and decision-centric work in operations areas including customer service and onboarding, account opening and identify verification, anti–money laundering programs, and other document-intensive compliance activities.

Job Description

About us:


Our mission is to accelerate the world’s transition to more meaningful work. By automating repetitive, data-intensive tasks and processes at scale, WorkFusion eliminates mundane business activities so companies can serve their customers faster and better. As a world leader in process automation for banking, financial services, and insurance, we offer an unrivaled digital workforce platform that enables enterprises to increase workforce capacity, enhance customer satisfaction, and ensure ongoing compliance. We compete in the world’s fastest-growing software segment and are growing at record pace with customers and team members spanning the globe.


We are looking for a passionate Machine Learning Engineer to work closely with our data scientists, AI researchers and other software engineers to solve the challenges of deploying ML solutions to production on our intelligent automation platform. WorkFusion has an incredible set of customers including top companies in banking, financial services, insurance, technology and entertainment.


Join us if you’d like to build solutions to cutting edge AI and ML problems including state-of-the-art deep learning, federated learning, transfer learning, differential privacy and ML Ops. You will have the opportunity to have a strong influence and high impact on the future of a high growth, venture-backed company. You’ll also get to learn about some of the most important problems in the financial services and insurance industries, including anti-money laudering (AML), customer due diligence and screening (Know Your Customer/KYC), fraud detection and insurance underwriting.


Key Responsibilities:


  • Creating infrastructure for managing the full ML lifecycle, including dataset management, model training, serving, version control, deployment and monitoring
  • Applying state-of-the-art research to automation and document processing models, including deep learning, federated learning and differential privacy
  • Building systems to manage and document our repository of models to support explainability and Model Risk Management (MRM)
  • Bridging model-building and production by translating the work of ML scientists from environments such as Jupyter notebooks to modular, Enterprise-grade software packages
  • Supporting data/AI research scientists to run experiments at scale on our in-house ML Ops platform
  • Working on cross-functional teams including product management, AI/data scientists and engineering to build out technical requirements for our ML products
  • Contributing to model monitoring and deployment capabilities in our ML Ops platform
  • Assisting in vendor evaluations for build vs. buy and partnership opportunities




  • Solid software engineering experience and proficiency in Python
  • High-level understanding of machine learning and deep learning techniques (image and document processing is a plus)
  • Hands on experience with contemporary deep learning tools and frameworks including PyTorch, TensorFlow, KubeFlow, Scikit-learn, Pandas
  • Experience with cloud computing (AWS, Google Cloud, Azure)
  • Familiarity with DevOps tools and container orchestration (Kubernetes, Docker, Jenkins)
  • Experience working with parallel/distributed computing using CPUs/GPUs
  • Strong communication skills and excellent software engineering habits, including documenting code, working with teammates on pull requests, and contributing to team engineering culture and best practices
  • Experience working on Agile, self-organizing teams, writing user stories



  • Location flexibility – we are a remote-friendly company
  • Unlimited PTO
  • High impact work on building and scaling cutting edge AI and ML products including ML Ops and federated learning

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