Join zaimler.ai as a Staff Applied MLE to bridge the gap between research and production. You will work directly with the Chief Scientist to extract knowledge from heterogeneous enterprise data and build semantic graphs for real-time inference. This role is pivotal in developing the proprietary domain intelligence that prevents enterprise AI failure.
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Learn about Jack
Staff Applied Machine Learning Engineer at zaimler
As a Staff Applied MLE, you’ll solve the critical "context gap" for Fortune 500 enterprises by building semantic graphs and fine-tuning models that turn fragmented data into machine-optimized intelligence. This is a high-impact, hybrid role in San Mateo for a technical builder ready to define a new infrastructure category alongside a world-class engineering team.
About this role
Role overview
About the company
Technology
AI agents can't reason over data they don't understand. Enterprise data today is fragmented across dozens of systems with no shared context, meaning, or structure, and that's why most enterprise AI is failing. The shift from copilots to autonomous agents is creating an entirely new infrastructure layer, and we're building it.
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. Imagine knowledge graphs that support real-time inference, built for systems that need to reason, not just retrieve.
zaimler was founded by Biswajit Das (ex-VP Engineering, Truera), a Data Infra veteran and former Chief Architect at Visa, and Sofus Macskassy (ex-Director of Engineering, LinkedIn), who built one of the largest knowledge graphs in production in the industry at LinkedIn. We're a small, senior team at the seed stage, deploying with major enterprises across insurance, travel, and technology. If you want to build infrastructure that the next decade of AI runs on, we'd love to talk.
What you'll do
What you will do
- Develop and adapt models for sophisticated knowledge extraction from diverse enterprise data sources to construct and improve high-scale AI Data Graphs.
- Fine-tune large and small language models with domain-specific context to ensure performance and reliability in complex, multi-step enterprise agentic workflows.
- Partner with infrastructure teams to build and evolve training pipelines, evaluation frameworks, and feature stores that support real-time inference and scale.
Who you are
Who this is a fit for
- Holds a PhD or Master’s in Computer Science or ML with 4-10 years of experience deploying NLP or LLM models in production environments.
- Possesses deep technical expertise in at least one core area: knowledge extraction, semantic graph systems, embedding-based retrieval, or LLM fine-tuning and optimization.
- Thrives in high-autonomy startup environments and has a proven track record of building and owning end-to-end AI/ML infrastructure rather than just contributing components.
Why this role
Why this role is remarkable
- Build the missing infrastructure layer between fragmented data and AI agents, solving the "context gap" that causes most enterprise AI pilots to fail.
- Work alongside elite leadership, including a CEO who was Chief Architect at Visa and a Chief Scientist who built LinkedIn’s massive production knowledge graph.
- Impact Fortune 500 customers from day one, shipping high-stakes models that enable autonomous agents to reason over complex, proprietary enterprise data with precision.
Jack & Jill
How Jack & Jill work together
I get to know what you’re great at, then find roles you’d never find yourself.Ok, I'll go first. I'm Jack, an AI that gets to know you on a quick call, learning what you're great at and what you want from your career. Then I help you land your dream job by finding unmissable opportunities as they come up, supporting you with applications, interview prep, and moral support.
I recruit from Jack’s network and make the intro when I spot a great match.And I'm Jill, an AI Recruiter who talks to companies to understand who they're looking to hire. Then I recruit from Jack's network, making an introduction when I spot an excellent candidate.
Meet Jack
Jack gets to know what you're great at and what you want next, then searches 15 million jobs daily and helps you discover roles at companies like this.
How does this work?
Jack’s an AI agent for job searching and career coaching. He works for you.
Jill is the AI recruiter working for the company. She recruits from Jack’s network.
If it’s a match and the company wants to meet you, they’ll make the intro. In the meantime, if you’d like, Jack will send you excellent alternatives.