Senior AI/ML Engineer

wizerr ai United State
Remote
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AI Summary

Design and develop AI/ML pipelines, build applied AI features, and improve search and ranking. Own data and ML pipelines, and deploy to production. 5+ years of experience in ML or data engineering, with hands-on production LLM work.

Key Highlights
Own data and ML pipelines
Design and develop AI/ML pipelines
Build applied AI features
Key Responsibilities
Own data and ML pipelines
Ingest, clean, and structure domain data from messy, heterogeneous sources
Build the pipelines everything else runs on
Build applied AI features
Retrieve, structured extraction from technical documents, agentic reasoning over the graph — backed by real evaluation, not vibes
Improve search and ranking
Iterate on the matching, scoring, and ranking logic that decides what customers see
Ship to production
APIs customers and internal teams can call — with the reliability, observability, and evaluation that implies
Run it on cloud
Cross-stack when needed
Technical Skills Required
Python SQL AWS GCP
Nice to Have
Background in electronics, EE, semiconductors, or another deep technical domain
Parametric / faceted search, product catalogs, or structured retrieval at scale
Designed evaluation frameworks for LLM pipelines
Exposed internal tools as agent-callable APIs

Job Description


We are hiring a Senior AI/ML Engineer at Wizerr.


About Wizerr

Wizerr is the intelligence layer for AI infrastructure hardware - the components and systems that go into AI factories and AI devices. The trillion-dollar AI buildout runs on physical components — power, memory, optics, signal integrity.


Wizerr ELX is our component intelligence graph that works at the intersection of large-scale structured data, LLMs, and a real domain ontology — and we ship to

paying customers every week.


The Role

You'll own a meaningful slice of our data, ML, and applied AI surface — working directly with the lead technologist on the systems that power the product. The pipelines that ingest and structure component data. The LLM-driven reasoning customers actually pay for.

Hands-on, production-focused. Concept to deployment to keeping it healthy at 3am. Strong ownership, minimal hand-holding.


What You'll Do

  • Own data and ML pipelines. Ingest, clean, and structure domain data from messy, heterogeneous sources. Build the pipelines everything else runs on.
  • Build applied AI features. Retrieval, structured extraction from technical documents, agentic reasoning over the graph — backed by real evaluation, not vibes.
  • Improve search and ranking. Iterate on the matching, scoring, and ranking logic that decides what customers see.
  • Ship to production. APIs customers and internal teams can call — with the reliability, observability, and evaluation that implies.
  • Run it on cloud. AWS and/or GCP. Pick up new services when the problem calls for it.
  • Cross-stack when needed. Drop into backend or frontend to ship a feature end-to-end.


What We're Looking For

  • 5+ years ML or data engineering, with hands-on production LLM work in the last 1–2 years.
  • Strong Python and SQL. Comfort designing for performance on large, evolving datasets.
  • Deployed and operated real workloads on AWS and/or GCP.
  • A track record of owning things end-to-end — not notebooks, demos, or handoffs.
  • Comfort with messy, domain-shaped data.
  • Clear written communication. We're async-first; you'll document your thinking and drive your own work.


Nice to Have

  • Background in electronics, EE, semiconductors, or another deep technical domain.
  • Parametric / faceted search, product catalogs, or structured retrieval at scale.
  • Designed evaluation frameworks for LLM pipelines.
  • Exposed internal tools as agent-callable APIs.


How We Work

Small, senior team. Fully remote, async-first, global. We use AI tooling aggressively in our own workflow and ship into production every week.


How to Apply

Email shine@wizerr.ai with your resume or LinkedIn, and a short note about an AI or ML pipeline you've actually shipped — what you owned, and what you learned.


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