Full-Time
Staff Applied MLE
zaimler
1-10 employees
AI readiness platform for enterprise data
No salary listed
San Mateo, CA, USA
In Person
 AI & Machine Learning (3) |
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- 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
- 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
- Experience on both the AI infrastructure and applied research sides of an organization
- Familiarity with Knowledge Extraction, Natural Language Processing, 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)
- We value builders over résumés. If this role excites you but you don't check every box, we still want to hear from you. zaimler is an equal opportunity employer.
Zaimler builds an enterprise data platform that makes data AI-ready by simplifying and organizing complex datasets for advanced applications. The platform ingests and structures data, automates taxonomy creation and knowledge-graph representations, and enables scalable preparation for machine learning and business intelligence. Led by veterans from LinkedIn, Meta, and Visa, Zaimler targets large-scale enterprises with an emphasis on AI readiness and scalable data infrastructure, backed by venture funding and early design partnerships. Its goal is to become the standard platform that lets large organizations unlock the value of their data through AI-ready infrastructure and scalable AI applications.
Company Size
1-10
Company Stage
N/A
Total Funding
N/A
Headquarters
N/A
Founded
N/A
Simplify's Take
What believers are saying
- Series A funding accelerates scaling of context infrastructure for enterprise AI.
- $10 trillion TAM in enterprise data preparation and AI readiness.
- Strong design partner validation signals product-market fit in mid-market segment.
What critics are saying
- 23 unfilled roles including MLEs delay product deployment versus better-staffed competitors.
- Databricks Unity Catalog directly competes on ontology automation and data governance.
- Lack of revenue visibility beyond design partners threatens runway sustainability.
What makes zaimler unique
- Auto-inferred ontology enables semantic reasoning without manual taxonomy creation.
- Founding team built knowledge graphs at LinkedIn, Facebook, and Visa.
- Runtime context graph unifies fragmented enterprise data for AI applications.
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Benefits
Health Insurance
Flexible PTO
Company Equity