Senior AI/ML Engineer - Remote, Production ML Pipelines

Wizerr AI; California; On-site; USD 150,000 - 210,000; Full time

Job summary

Wizerr is hiring a Senior AI/ML Engineer to own data and ML pipelines, working directly with the lead technologist on the systems that power the product. You will build the pipelines that ingest, structure, and drive the components of our AI infrastructure.

We expect hands-on experience with production LLM work over the last 1–2 years, strong Python/SQL skills, and a track record of end-to-end ownership. This role is fully remote with async-first collaboration.

Qualifications

• 5+ years in ML or data engineering with hands-on production LLM work in the last 1–2 years.
• Strong Python and SQL for large, evolving datasets.
• Deployed and operated real workloads on AWS and/or GCP.
• Track record of end-to-end ownership, not just notebooks or demos.
• Clear written communication; async-first documentation.
• Messy, domain-shaped data experience welcomed.

Responsibilities

• Own data and ML pipelines: ingest, clean, and structure data from heterogeneous sources.
• Build applied AI features: retrieval and structured extraction from technical docs, agentic reasoning over the graph.
• Improve search and ranking: refine matching, scoring, and ranking logic.

Skills

ML / Data Engineering

Python

SQL

AWS

GCP

Ownership

Communication

Tools

AWS

GCP

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 [e-mail] 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.