Machine Learning Engineer, Middesk

• 5+ years applied ML experience with proven impact in risk, fraud, trust & safety, compliance, fintech, or other high-stakes domains
• Track record of owning ML models end-to-end — from research and design to deployment, monitoring, and retraining in production
• Strong software engineering skills (Python, ML frameworks, deployment pipelines) and ability to write reliable, production-grade code
• Hands-on experience with ML infrastructure such as feature stores, model management, training/serving pipelines, and monitoring tools
• Comfortable as a senior IC: you can set technical direction, establish best practices, and mentor peers while collaborating effectively across teams
• Experience working cross-functionally with data engineers, platform engineers, and product stakeholders to bring ML systems to life
• Deep expertise in classification challenges such as imbalanced labels, sparse signals, cold start, and production version management

Desirable

• We’re building AI-driven applications that power business onboarding, fraud prevention, and identity verification. With proprietary data assets and deep domain expertise, we’re uniquely positioned to create a new generation of ML-powered solutions for trust and risk
• We’re looking for a hands-on Machine Learning Engineer with strong Data Science expertise to take end-to-end ownership of the ML lifecycle: from feature design and model development, to deployment, monitoring, and iteration in production
• Unlike larger organizations where responsibilities are split, you’ll have the opportunity to own models from concept to production while working closely with product managers, engineers, and data platform teammates who support and amplify your work
• This is a rare chance to join an earlier-stage company where you’ll have broad visibility and influence, and where your ML systems will have immediate and measurable impact on customers
• End-to-end ML ownership: Lead the full lifecycle of ML systems — feature engineering, model design, training, evaluation, deployment, monitoring, and iteration
• Collaborate with a strong team: Work alongside data engineers, platform engineers, and product teammates who ensure you have the infrastructure, data, and context to deliver
• Design & deploy production models: Build high-performance ML applications in risk, fraud, trust & safety, and compliance domains
• Keep models healthy in production: Proactively monitor, detect drift, and retrain to ensure long-term performance and reliability
• Experiment & learn: Drive online experiments, offline evaluation, and counterfactual analyses to prove impact
• Shape ML foundations: Contribute to the feature store, model management, training/serving pipelines, and best practices that scale ML across multiple use cases