Forward Deployed Engineer (AI Solutions) - Atrix

Build quick prototypes and workflow experiments for GTM initiatives

Translate customer needs into clear product requirements

Triage bugs, uncover feature gaps, and partner with engineering to implement fixes or enhancements

Shape product roadmap with insights drawn from real-world usage

Own business-sense QA and accuracy validation for customer deployments

Identify risk, propose guardrails, and define scalable validation processes

Enable AI evals to ensure high trust and quality of all workflows

Create internal SOPs for deployment, QA, template configuration, and customer onboarding

Own 3–5 enterprise accounts end-to-end within 6 months

Identify patterns across accounts and surface scalable templates or product opportunities

Become a trusted technical advisor to medical, clinical, and commercial stakeholders

Background in life sciences, healthcare, or real-world data

Data science experience

Work experience at healthcare AI startups or technical consulting firms

Experience deploying AI in regulated or enterprise settings

🚀 Mission-Driven Impact Your work will help life sciences teams deliver the right treatment to the right patient—faster. Real-world clinical outcomes start with better tools, and you’ll be building them.

👨‍⚕️ Customer-Centric & High-Stakes You won’t be building in a vacuum. You'll work directly with top-10 pharma companies to understand how AI can transform medical workflows—this is high-impact, high-visibility work.

🧱 End-to-End Ownership Own major parts of the stack, shape technical architecture, and influence product direction from day one.

🌱 Early Stage, High Growth We're small and moving fast. You’ll help shape both the technology and the culture as we scale.

🧠 Tech Meets Trust Design AI-native workflows that scientific and medical teams trust and depend on daily.

📈 Clear Growth Path Opportunity to grow into a Lead Frontend or Fullstack Tech Lead role as we expand the engineering team.

Instead of a traditional cover letter and resume, we’d like you to complete a short task to show how you work:

Use an AI tool (ChatGPT, Claude, Gemini, etc.) to generate 3–5 short, fake customer interaction notes . These should look like quick snippets from a customer support chat (e.g., a frustrated user, a neutral inquiry, a positive message).

Using the same AI tool, create:

This should include:

The sentiment categories you choose (e.g., Positive, Neutral, Negative).

Short definitions for each category.

1–2 criteria the evaluator/model should look for.

How you used the AI tool.

What prompts you wrote.

Any challenges you encountered (e.g., model over-explaining, unclear sentiment boundaries).

Send us your:

Customer interaction notes

Sentiment rubric (checklist)

LLM usage notes

These can be in a Google Doc, Word Doc, or PDF.