Staff Applied Machine Learning Engineer

Job Overview

zaimler is the context infrastructure for the agentic era: a platform that automatically discovers domain knowledge, maps relationships, and gives AI agents the semantic understanding to operate with precision at scale. We're growing and deploying with major enterprises across insurance, travel, and technology.

About the Role

This is a staff-level role for a seasoned AI/ML engineer who can operate at the intersection of research and production. You'll serve as the technical bridge between our research and applied engineering teams — translating cutting-edge ideas into robust, scalable systems while setting the strategic direction for how we train, evaluate, and deploy models.

Key Responsibilities

• Bridge research and applied engineering, ensuring ideas move from concept to production with rigor and speed
• Own training and evaluation infrastructure, including tuning, modeling pipelines, and evaluation frameworks
• Build and evolve feature stores that serve both model development and production workloads
• Optimize agentic workflows end-to-end for performance, reliability, and scale
• Set technical strategy for how zaimler trains, evaluates, and deploys models as the platform grows
• Partner closely with leadership to define the ML roadmap and make key architectural decisions

Required Qualifications

• PhD preferred; Master's with exceptional experience considered
• 8–10 years of experience in ML or AI engineering, with meaningful time in platform or lab environments
• Track record of building and owning AI/ML infrastructure end-to-end
• Deep experience with training pipelines, evaluation frameworks, and feature store design
• Hands-on experience optimizing agentic or multi-step LLM workflows in production
• Has operated as a lead or Head of AI at a startup or within a high-autonomy team
• Strong strategic instincts; able to set direction, make tradeoffs, and communicate them clearly

Nice to Have

• Experience on both the AI infra and applied research sides of an organization
• Familiarity with Knowledge Extraction, NLP, or semantic graph systems
• Experience with GPU optimization, vLLM, Ray, or similar serving and training tools
• Background working with or alongside research teams (lab, academic, or industry)