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New York, New York, United States
Onsite
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.
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.
Build end-to-end pipelines and models that turn fragmented, messy enterprise data into high-leverage, AI-ready assets
Structure and normalize noisy datasets — defining the data packs and ontology that our AI engineers build on top of
Build the internal product and tooling that makes data work faster and repeatable across customers, so each engagement compounds rather than starts from zero
Work directly with operators and product/AI engineers to turn high-value use cases into production data workflows
Form strong technical opinions on data models, storage, orchestration, and infra tradeoffs — and make the calls
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, Machine Learning.
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
Experience building agentic or automated data-engineering tooling
Hands-on experience with modern cloud data platforms (e.g., Databricks)
Experience with health-system data (EHR, claims, and other operational healthcare datasets) or other complex, regulated enterprise data
Prior startup, founding, or forward-deployed experience
We’re working against an incredibly ambitious mission. It won’t be easy, but it will likely be the most fulfilling work of your career. If this excites you, let's chat, even if you don't meet all of the qualifications above.
Dream bigger: We have the unique privilege of taking on the most ambitious problems and we should chase them with optimism, responsibility, and genuine belief that we can make it happen. We have to embrace the hard things when no one else will.
Heart in the game: What we're doing matters and we have to give a shit. Internally, that means fixing badness when you find it. Externally, it means honoring the trust our customers place in us with their most important problems. This isn’t a 9-5, nor is it a job we’re ever going to monitor your hours. We promise to put work in front of you that matters and in return, we ask you to promise to care.
Win for the customer: Everyone is an engineer and the job of an engineer is to deliver outcomes, not outputs. Everything we do—the products we build, the partnerships we launch, the strategy we set—exists to make our customers successful. Delivery is the strategy.
Make the call: Organizations are only as strong as the pace at which they make decisions. Everyone at Percepta should feel empowered to commit and shape the ambiguity in front of them. But "make the call" cuts both ways: make the decision and make the phone call. High-agency decision-making only works with high-bandwidth communication and we commit to never operate in silos.
Intensity with kindness: We believe in excellence in execution, candor in feedback, ruthlessness in prioritization, and survivalist urgency. We also believe you don't need to be an asshole to deliver on any of this. The trust built through shared kindness and vulnerability is what makes the intensity sustainable.
