Pragmatike - Director of Machine Learning (New York)
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Requirements
• Experience building ML systems for security, fraud detection, or adversarial environments. • Experience working with LLMs in production (evaluation, fine-tuning, retrieval systems, guardrails). • Background in real-time inference systems or high-throughput distributed systems. • Experience making strategic build vs. buy infrastructure decisions. • Previous startup experience in high-growth environments.
Benefits
• $300K – $400K • 0.5% – 1.5% • Upload your resume here to autofill key application fields. • Drop your resume here! • Parsing your resume. Autofilling key fields... • or drag and drop here • Phone / Whatsapp • Yes, I am willing to relocate now or in the near future • No, I am not planning to relocate • US Citizen or Permanent Resident (Green Card) • Currently authorized to work in the US on a valid visa • No current work authorization. Will require employer sponsorship now or in the future • This information is collected for alignment purposes only and is not a binding offer. • We retrained the model when performance dropped. • We monitored metrics manually and adjusted thresholds when needed. • We had automated evaluation pipelines, regression testing, and drift detection in place. • I designed the evaluation framework, defined quality metrics, implemented regression detection, and built feedback loops for continuous improvement. • Build and fine-tune the most advanced model possible to demonstrate performance gains. • Set up cloud ML infrastructure (e.g., SageMaker/Modal), then start experimenting with models. • Establish data pipelines, define evaluation metrics, ship a simple baseline model, and implement monitoring before scaling complexity. • Outsource model development to an external provider while focusing on product integration. • (Links or references encouraged.) • I have mentored junior engineers but have not been responsible for hiring or structuring a team. • I participated in hiring decisions and helped onboard engineers, but did not define the team structure or long-term roadmap. • I helped scale an existing ML team (hiring, mentoring, improving standards), but the function was already established before I joined. • I built or significantly shaped an ML team from scratch — defined roles, hiring profile, interview process, technical standards, roadmap, and culture.
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