Lead AI/ML Engineer

loom ai; United States; Remote; USD 140,000 - 190,000; Full time

Benefits offered by this job

Direct access to founders

Equity on conversion

Equity opportunity

Job summary

loom ai seeks a results-driven ML engineer to own end-to-end production AI systems for real enterprise data. You will ingest signals in real time, build scoring models, and deploy safe, scalable frameworks with a human gate for critical changes.

You will work directly with founders and enterprise clients, applying RAG, prompt engineering, and model fine-tuning to drive measurable improvements and equity upon conversion. This is a remote role in the United States with growth potential.

Qualifications

• 3–5 years of ML engineering or applied AI — production systems, not notebooks.
• Python fluency; traditional ML and modern AI stacks.
• RAG, prompt engineering, evaluation harnesses, and/or fine‑tuning on domain‑specific data.
• Agent orchestration — systems where models make, route, or compose decisions.
• Full model lifecycle: training, evaluation, shadow, canary, A/B, ongoing tuning.
• Client presence — explain a model to a CFO.

Responsibilities

• Own end-to-end ML platform including data pipelines, models, and deployment frameworks.
• Ingest and structure signals from enterprise data sources in real time.
• Use LLMs to detect patterns and propose retraining needs.
• Collaborate with enterprise clients and domain experts.

Skills

Python fluency

Production ML systems

Prompt engineering

Evaluation harnesses

Domain data understanding

Client-facing communication

Startup mindset

Tools

LightGBM

XGBoost

scikit-learn

LangChain

LlamaIndex

OpenAI API

Anthropic API

Job description

Contract-to-Hire 3–5 Years Remote · US Competitive Rate Equity on Conversion

loom ai sits at the intersection of professional services and product. We build continuously learning AI systems for enterprises in regulated industries. The edge is no longer in who has the most data — it's in who can prompt, fine-tune, evaluate, and orchestrate most effectively. We need someone who owns that end to end.

What We Build

A continuously learning decision loop — Read, Act, Observe, Tune.

The heart of our platform runs in production on real enterprise data across multiple engines simultaneously. This is what you'll own.

Ingest and structure signals from enterprise data sources in real time — the foundation every decision is built on.

Act

Observe

Close cohorts, assemble ground truth from real outcomes, and build the feedback signal that makes the next cycle smarter.

Tune

Use LLMs to detect patterns, draft retraining proposals, and promote models safely — with a human in the gate where it matters.

You’ll own the full stack: the data pipelines that feed it, the models that score it, the frameworks that deploy and promote them safely, and the LLM‑powered layer that finds patterns and proposes what to change next. This runs on real enterprise data, serves real decisions, and gets measurably better over time.

What We Offer

Real ownership. Real clients. Real upside.

Access

Direct access to founders — real ownership from day one

Exposure

Enterprise AI in complex, regulated environments

Path

Full-time conversion based on performance and growth, with equity opportunity

What We're Looking For

Must-Haves

• 3–5 years of ML engineering or applied AI — production systems, not notebooks
• Python fluency; traditional ML (LightGBM, XGBoost, scikit-learn) and modern AI stacks (LangChain, LlamaIndex, OpenAI/Anthropic APIs)
• RAG, prompt engineering, evaluation harnesses, and/or fine‑tuning on domain‑specific data
• Agent orchestration — systems where models make, route, or compose decisions
• Full model lifecycle: training, evaluation, shadow, canary, A/B, ongoing tuning
• Client presence — hold your weight with domain experts, explain a model to a CFO
• Startup mentality — move fast, own your work, don't wait to be told
  • Product instinct — governable and scalable, not just functional
  • Prior consulting or client‑facing applied AI experience