Staff Applied Machine Learning Engineer

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. Deeply hands-on, but able to zoom out and set the agenda.

We're looking for someone who has been here before. Built the infra, shaped the strategy, and knows what good looks like at every layer of the ML stack.

What You Will be Doing

• 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

Prior Experience

• 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, not just contributing to it
• 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)