AI/ML Engineer

Lead AI/ML Engineer

Contract-to-Hire · Remote (US) · 3+ Years Experience · Competitive Rate, Commensurate with Experience · Equity on Conversion

About loomAI

loomAI sits at the intersection of professional services and product. We deliver like a software company, with the depth and accountability of a consultancy. We help enterprises in regulated industries — particularly insurance and financial services — deploy AI in ways that are meaningful, measurable, and built to scale. Our proprietary accelerator, Lumen, brings consistency and speed to every engagement, turning what most firms treat as bespoke work into repeatable, outcome-driven delivery.

The Role

Foundation models have changed the leverage point in AI. The edge is no longer in who has the most data — it's in who can prompt, fine-tune, evaluate, and orchestrate most effectively.

You'll work directly with the founders, build production systems that learn continuously, and engage with clients as a peer — earning trust in rooms with domain experts and business leaders alike.

This is not a role for someone who needs a playbook. It's for someone who writes it.

What You'll Build

The heart of our platform is a continuously learning decision loop — **Read, Act, Observe, Tune**. It runs in production on enterprise data, across multiple decision engines simultaneously, in environments where compliance, auditability, and accuracy aren't optional.

Each cycle, the system reads signals, acts on them with a scored decision, observes what actually happened, and tunes itself based on real outcomes. The loop closes automatically — 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. That's what you're building.

What You'll Do

- Own the full ML lifecycle — data pipeline design through model training, evaluation, deployment, and continuous tuning

- Design and implement RAG pipelines, evaluation harnesses, and fine-tuning workflows on top of foundation models

- Architect and orchestrate agentic systems that execute decisions at enterprise scale

- Build and maintain production classifiers — knowing when a fine-tuned foundation model is the right call and when a gradient-boosted classifier is

- Implement shadow/canary/A/B deployment patterns and safe model promotion tooling

- Develop ground truth assembly logic and verdict frameworks that feed retraining cycles

- Engage directly with clients — explain model architecture, confidence scoring, and system behavior to technical teams and business leaders

- Contribute to loomAI's Lumen accelerator — identifying what should be productized and made repeatable across engagements

- Be a thought partner to the founders — push back, propose better paths, drive decisions forward

Must-Haves

- 3–5 years of hands-on ML engineering or applied AI experience — production systems, not notebooks

- Fluency in Python; experience with traditional ML frameworks (LightGBM, XGBoost, scikit-learn) and modern AI stacks (LangChain, LlamaIndex, OpenAI/Anthropic APIs)

- Demonstrated experience with RAG, prompt engineering, evaluation harnesses, and/or fine-tuning on domain-specific data

- Experience with agent orchestration — building systems where models make, route, or compose decisions

- Ability to design data pipelines that ingest, normalize, join, and persist structured enterprise data at scale

- Deep understanding of model lifecycle management — training, evaluation, shadow deployment, canary promotion, A/B, ongoing tuning

- Strong client presence — you hold your weight with domain experts, explain a model to a CFO, and earn trust in a room

- Startup mentality — you move fast, own your work, and don't wait to be told

Strong Plus

- Salesforce ecosystem experience — Agentforce, Data Cloud, Einstein, Apex, Flow

- Financial services background

- Experience with versioned policy/rule deployment patterns

- Product instinct — you think about what makes a system governable and scalable, not just functional

- Prior consulting or client-facing applied AI experience

Who You Are

- You think in feedback loops — systems that learn and improve, not point solutions

- Equally comfortable in a client room and a code editor

- You hold both paradigms: Era 3 rigor and Era 4 leverage — and you know when to use which

- You see messy enterprise data as a puzzle, not a blocker

- Interested in taking a risk on something early because you believe in where it's going

- High standard, no hand-holding required

What We Offer

- Competitive rate, commensurate with experience

- Direct access to founders and real ownership of meaningful production work from day one

- Exposure to enterprise AI in complex, regulated environments

- A clear path to full-time conversion based on performance and company growth, with equity opportunity upon conversion

How to Apply

We're not taking resumes first. We want to see how you think.

Record a 2–3 minute video** responding to this prompt:

You've built a model that scores a decision — routes a customer, scores their fit, or composes an offer. It goes live. 60 days in, how do you know if it's working? How do you assemble ground truth, and how do you decide when and how to retrain?*

Ground rules:

- 2–3 minutes max

- Your own words — don't script it

- Be specific — tell us what you'd actually do, including what you'd skip at an early stage

- Have an opinion — show us how you think

Then email:

- Your video link (Loom, YouTube unlisted, or Google Drive)

- Your CV

- Your LinkedIn profile

- One line on why loomAI

To: [email protected]

Subject:** Lead AI/ML Engineer Application — [Your Name]