Middesk is looking to solve the problem of business identity verification and risk assessment by building AI-driven applications that power business onboarding, fraud prevention, and identity verification.
Requirements
- 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.
- Deep expertise in classification challenges such as imbalanced labels, sparse signals, cold start, and production version management.
- Experience working cross-functionally with data engineers, platform engineers, and product stakeholders to bring ML systems to life.
- Comfortable as a senior IC: you can set technical direction, establish best practices, and mentor peers while collaborating effectively across teams.
Responsibilities
- Lead the full lifecycle of ML systems — feature engineering, model design, training, evaluation, deployment, monitoring, and iteration.
- Build high-performance ML applications in risk, fraud, trust & safety, and compliance domains.
- Proactively monitor, detect drift, and retrain to ensure long-term performance and reliability.
- Drive online experiments, offline evaluation, and counterfactual analyses to prove impact.
- Contribute to the feature store, model management, training/serving pipelines, and best practices that scale ML across multiple use cases.
- Design & deploy production models
- Keep models healthy in production
Other
- B2B SaaS experience, ideally building ML products for enterprise customers.
- Familiarity with graph, LLM-based feature generation, or AI agent workflows.
- Experience scaling ML across multiple products or risk domains.
- Ability to collaborate effectively across teams
- Ability to set technical direction and establish best practices
