Job description

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: challenge.percepta.ai - 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: 1. 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. 2. 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 - Experience building agentic or automated data-engineering tooling - Hands-on experience with modern cloud data platforms (e.g., Databricks) and streaming systems (e.g., Kafka) - Experience with health-system data (EHR, claims, ADT feeds, and other operational healthcare datasets) or other complex, regulated enterprise data - Experience using an LLM as a structured-extraction step inside a larger data pipeline, rather than just as a chat interface - Prior startup, founding, or forward-deployed experience ABOUT OUR TEAM As an organization, Percepta is committed to inclusion and belonging, and our people are our greatest investment! 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. In addition to competitive compensation, equity, 401(k) matching, comprehensive health and family benefits, and lunch every day, we offer a culture built on autonomy and trust. We strongly believe that the boldest solutions come from teams who dream bigger together, and that means bringing in people from every kind of background, experience, and path. We are an office-first team that values the impact of in-person collaboration, but we focus on outcomes over outputs—believing that we are each responsible for solving the "whole problem" to make our customers win. If you don't check every box on this description but you care deeply about the problems we're tackling, seeking the truth, and wanting to own outcomes alongside a team that has your back, then we want to hear from you! We know self-doubt keeps great people from applying, so please don't count yourself out before we've had the chance to meet you. Tackling the hardest problems takes a range of perspectives in the room, and we believe that's how we build something truly game-changing. Our company values are more than just words on a page. They inform how our team shows up every day: OUR VALUES 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. Percepta is proud to be an equal opportunity employer and we evaluate every qualified applicant on their merits and give equal consideration for employment regardless of race, color, creed, religion, sex, gender identity, sexual orientation, national origin, disability, veteran or uniformed service status, age, or any other characteristic protected under applicable law. Percepta provides reasonable accommodations to qualified applicants and employees in accordance with federal and applicable state law.

Requirements

Must-haves

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.
As a founding hire, you're not inheriting a playbook — you're writing it.
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.
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-haves

Experience building agentic or automated data-engineering tooling
Hands-on experience with modern cloud data platforms (e.g., Databricks) and streaming systems (e.g., Kafka)
Experience with health-system data (EHR, claims, ADT feeds, and other operational healthcare datasets) or other complex, regulated enterprise data
Experience using an LLM as a structured-extraction step inside a larger data pipeline, rather than just as a chat interface
Prior startup, founding, or forward-deployed experience

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aiawsmlmachine learningllm

Platform: ashby
Effort: quick

Benefits

Benefits detected in the listing.

401(K)