Middesk is looking to solve the problem of streamlining customer workflows, focusing on business onboarding, by building AI-driven applications that leverage their proprietary identity data assets and deep domain expertise.
Requirements
- 5+ years applied ML experience, with direct impact in risk, fraud, trust & safety, compliance, or adjacent high-stakes domains.
- Proven track record of shipping ML models from research to production in external-facing products.
- Expertise in classification with real-world challenges, for example: imbalanced labels, sparse signals, cold start, and production version management.
- Hands-on ML infrastructure experience: feature stores, model management, ML training/serving pipelines.
- Comfort as a senior IC: setting technical direction, mentoring peers, and establishing best practices.
Responsibilities
- Build risk & fraud ML applications: Deliver production ML models in fraud, trust & safety, KYB, and compliance domains, with measurable impact on customer workflows.
- Tackle hard data problems: Work on classification problems with extreme class imbalance, sparse signals, and 'cold start' label challenges.
- Innovate in feature engineering & labeling: Use graph-based techniques, weak supervision, LLMs, and AI agents to improve signal extraction and automate labeling process.
- Establish ML infrastructure foundations: Partner with platform engineering team to design feature services, model training pipeline, model serving standards, and orchestration to scale multiple ML use cases.
Other
- B2B SaaS experience, ideally building ML products for enterprise customers.
- MLE/engineering collaboration experience, or direct MLE work on ML pipelines and services.
- Familiarity with graph, LLM-based feature generation, or AI agent workflows.
- Experience scaling ML across multiple products or risk domains.
- Degree requirements not specified, but experience and skills are emphasized.
