Founding Applied AI Engineer - Data

Who We Are Percepta’s mission is to transform critical institutions with applied AI. We care that industries that power the world (e.g. healthcare, manufacturing, energy) benefit from frontier technology.

To make that happen, we embed with industry-leading customers to drive AI transformation. We bring together:

• Forward-deployed expertise in engineering, product, and research
• Mosaic, our in-house toolkit for rapidly deploying agentic workflows
• Strategic partnerships with Anthropic, McKinsey, AWS, companies within the General Catalyst portfolio, and more

Our team is a quickly growing group of Applied AI Engineers, Embedded Product Managers and Researchers motivated by diffusing the promise of AI into improvements we can feel in our day to day lives.

Percepta is a direct partnership with General Catalyst, a global transformation and investment company.

About The Role We’re hiring one of the founding members of Percepta’s data team — a role that lives across the full spectrum from data engineering to data science to ML engineering. You won’t be boxed into one of those; the best person here has a center of gravity in one and real range across the others.

The Job Has Two Halves, And You’ll Do Both

• Be the data person. Build the pipelines, models, analysis, “data packs,” and ontology that turn messy enterprise data into something AI can actually use — and do it fast, inside real customer environments.
• Build the product around that. Build the tooling, abstractions, and increasingly agentic/automated systems that make the first half faster and compounding across every customer we work with. This is where you set the taste and help form our strategy for how Percepta does data — not as a one-off, but as something that gets better every time we do it.

As a founding hire, you’re not inheriting a playbook — you’re writing it.

What You’ll Do

• Turn high-value use cases from ideas to production. Work directly with customer operators and Percepta engineers to actually drive transformation through data and AI. Own the full lifecycle of a model in production — featurize the data, stand up the serving pipeline, and build the retraining/monitoring loop, not just stop once a prediction exists
• Bring the data to the AI. Build end-to-end pipelines — spanning streaming and batch sources alike — that turn fragmented, messy enterprise data into high-leverage, AI-ready assets
• Bring the AI to the Data. Integrate LLMs directly into production pipelines (e.g., structured extraction from unstructured text like clinical notes), owning prompt quality and evaluating output reliability
• Build the internal product and tooling that makes data work faster and repeatable across customers. Deliver value for customers and build product leverage at the same time.
• Make the call. Form and socialize technical opinions on data models, storage, orchestration, and infra tradeoffs.

What We’re Looking For

You might come from any point on the spectrum — a strong data engineer; a software engineer who’s done real data work; someone who’s done data science and software; or an ML engineer who now wants to build more. What’s common: you can build in ambiguity, you form opinions and ship, and you care about building leverage, not just outputs.

• Strong experience around some combination of Data Science, Data Engineering, and Machine Learning
• Comfort designing systems that reconcile real-time streaming data and batch/warehouse data under real latency constraints
• Genuine fluency reading and questioning ML model evaluation metrics (AUC, precision/recall, confusion matrices) — enough to catch a model that looks good on paper but wouldn’t hold up in production
• A sharp, demonstrated instinct for data skepticism — you naturally question whether a clean-looking number is actually right, and you dig into the raw records rather than trusting an aggregate
• Fluency using AI-assisted coding tools as a primary workflow to move fast, paired with the judgment to verify and defend your own analysis without leaning on AI
• A product instinct for the second half of the job — you want to build the thing that makes the work easier, not just do the work
• Intuition for what modern AI/ML and LLM systems actually need from data (features, retrieval, context, embeddings)
• High ownership and strong communication — you’re comfortable embedded directly with customer teams

Nice To Have

• Exper…