Middesk

Engineer

• Own Technical Direction for Deployments
• Lead architecture and technical design for complex, customer-facing systems and integrations.
• Make and communicate clear trade-offs across speed, reliability, and scalability.
• Identify systemic risks and drive improvements across deployments, not just one-off solutions.
• Establish patterns and standards that other engineers can build on.
• Partner deeply with strategic customers to understand their architecture, workflows, and constraints.
• Design, build, and deploy production-grade integrations using Middesk APIs, data pipelines, and AI workflows.
• Own deployments end-to-end: from architecture and implementation to rollout, monitoring, and iteration.
• Solve ambiguous, high-impact problems that don’t yet have established playbooks.
• Build and operate LLM- and agent-based systems in real customer workflows.
• Design and maintain evaluation frameworks (evals) to measure quality, reliability, and correctness of LLM outputs and agent behavior.
• Create test harnesses, metrics, and feedback loops to continuously improve AI-driven systems.
• Feel comfortable running multiple coding agents or automated workflows in parallel, debugging failures, and reasoning about system behavior.
• Act as a tight feedback loop between customers and core product teams.
• Translate deployment learnings into reusable abstractions, internal tooling, and product improvements.
• Influence roadmap priorities by surfacing recurring patterns, technical gaps, and opportunities for leverage.
• Participate in architecture discussions and contribute to org-wide technical decisions.
• Lead through influence, not authority—setting a high standard for engineering quality and pragmatism.
• Guide engineers through design reviews, code reviews, and hands-on collaboration.
• Help define what “great” looks like for Forward Deployed Engineering at Middesk.

Forward Deployed Engineer

Data Scientist

• 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.

Data Scientist, Machine Learning

Machine Learning Engineer

• 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.