Scientific Account Executive

Adaptyv Bio

Adaptyv is building an automated lab that lets AI agents run biology experiments. We're entering the era of agentic science where AI models can now design novel proteins, propose hypotheses, and iterate on experimental results. But they can't run the experiments themselves - that's still a manual, months-long process. We're building the infrastructure that gives AI agents access to the physical world. We are one of the fastest growing biotech companies, trusted by leading biopharmas, frontier AI labs, and the techbio companies pushing the field forward. This is a rare chance to help advance some of the most important work happening in biotech today. Our automated lab is powered by a deep software + hardware stack: lab instruments worth millions of USD reverse-engineered into API-controllable hardware, dozens of devices orchestrated through complex workflows, full observability on everything that happens in the lab, processing pipelines for messy physical-world data, and AI systems that troubleshoot production results and accelerate assay development. We’re growing rapidly and are hiring for talented people to scale and support the massive demand for AI-driven wet lab experimentation. ABOUT THE ROLE This is a hybrid sales and customer success role for someone with real scientific depth. You'll be the primary point of contact for customers — from first conversation through ongoing engagement. That means understanding what a protein design team is actually trying to achieve, helping them pick the right assays, making sure their experiments are set up correctly, and translating results back into language that's useful for their programs. The day-to-day: * Running technical sales calls where you discuss binding kinetics, expression systems, and assay design, not just pricing * Onboarding new customers and helping them design their first experiments on the platform * Monitoring ongoing accounts, flagging issues early, and proactively suggesting next steps based on their results * Working with the biology team to troubleshoot when a customer's experiment doesn't go as expected * Identifying expansion opportunities within existing accounts — new targets, new assay types, larger campaigns You won't be handed a script. You'll need to understand the science well enough to earn trust from PhD-level protein engineers and CSOs, while also being commercially sharp enough to close deals and grow accounts. Responsibilities * Own the full customer lifecycle: prospecting, technical sales conversations, deal closing, onboarding, and ongoing account management. * Serve as the technical point of contact for customers. Understand their protein engineering goals and recommend the right experiments (binding, expression, thermostability, enzyme activity). * Understand and explain our assay capabilities in depth: BLI, SPR, cell-free expression, and emerging assay types. Know when each is appropriate and what the data means. * Work closely with the biology team to ensure experiments are designed correctly and results are communicated clearly. * Build and maintain a pipeline of commercial opportunities across customer segments: AI-driven protein design companies, biotech startups, pharma R&D teams, and academic labs. * Develop proposals and quotes tailored to customer needs, from small pilot experiments to large-scale data generation campaigns. * Represent Adaptyv at conferences and industry events. Be able to have deep technical conversations at the booth, not just hand out brochures. * Feed customer insights back to the product and biology teams: what assays are people asking for, what's missing, where are we losing deals. What we're looking for * Scientific background in protein engineering, biochemistry, biophysics, or a related field. You should be comfortable discussing binding kinetics (KD, kon, koff), expression systems, and assay design without needing to look things up. * Experience in a customer-facing technical role: field application scientist, technical sales, scientific account management, or similar. CRO or biotech vendor experience is a strong plus. * Commercially driven. You care about revenue, pipeline, and closing deals, not just having interesting scientific conversations. * Strong communicator who can adapt to the audience: detailed and technical with a scientist, concise and business-focused with a VP. * Self-starter who can manage their own pipeline and priorities without heavy oversight. * Genuine interest in AI-driven protein design and the companies working in this space. You follow what's happening in the field. Application deadline We are reviewing applicants on a rolling basis.

Prior Labs

WHO WE ARE Foundation models transformed text and images. Structured data - the largest and most consequential data format in the world - stayed untouched. Tables run every clinical trial, every financial model, every scientific experiment, every business decision, and no one had built a foundation model that truly understood them. Until now. What LLMs did for language, we're doing for tables. The next modality shift in AI is happening, and we're hiring the team that makes it. Momentum. We pioneered tabular foundation models and are now the world-leading organization in structured-data ML. Our TabPFN v2 model was published as a Nature cover story and set a new state of the art for tabular machine learning. Since release we've scaled model capabilities 20x+, passed 3.5M+ downloads and 7,500+ GitHub stars, and are seeing accelerating adoption across research and industry - from detecting lung disease with Oxford Cancer Analytics to preventing train failures with Hitachi to improving clinical-trial decisions with BostonGene. The hardest work is ahead. We're scaling tabular foundation models to millions of rows, thousands of features, real-time inference, and entirely new data modalities, while building the infrastructure to run them in production across some of the most demanding industries on earth. These are open problems no one else is working on at this level. Our team. We're a small, highly selective team of 30+ engineers, researchers, and GTM specialists, with backgrounds spanning Google, Apple, Amazon, DeepMind, Meta, Microsoft Research, G-Research, Jane Street, Goldman Sachs, and CERN. We're led by Frank Hutter, Noah Hollmann, and Sauraj Gambhir, and advised by world-leading AI researchers including Bernhard Schölkopf and Turing Award winner Yann LeCun. We ship fast, do top-tier research, and hold each other to an extremely high bar. What's next. In 2025 we raised €9m pre-seed led by Balderton Capital, backed by leaders from Hugging Face, DeepMind, and Black Forest Labs. The next phase of growth is here, which makes this an ideal time to join. ABOUT THE ROLE We are hiring our first Account Executive to drive commercial adoption of our foundation models for structured data. This is a founding role — you'll shape how we sell, who we sell to, and what good looks like for every AE who joins after you. You'll own the full sales cycle across enterprise and mid-market accounts, working directly with the commercial founder and a team of ex-DeepMind, Palantir, Jane Street, Goldman Sachs and Amazon researchers and operators. This role combines a strong outbound focus with the potential for end-to-end deal ownership. You'll spend most of your time on outreach and discovery with technical buyers (data scientists, ML engineers, technical leaders), while also navigating buying committees, developing proposals, and closing deals. You'll help create the materials and messaging that make our outbound motion successful while driving revenue outcomes. We're looking for someone who loves the craft of outbound and wants to build the engine, not just close deals. OUTBOUND AND PIPELINE GENERATION * Drive outbound outreach to high-potential accounts through email, LinkedIn, and community channels * Execute targeted campaigns and iterate based on what works * Qualify inbound leads and identify opportunities worth pursuing SALES CYCLE OWNERSHIP * Run discovery calls and product demos with data science and ML teams * Develop proposals and business cases tailored to customer needs * Navigate buying committees across technical and business stakeholders * Own opportunities from qualification through signed contract TECHNICAL ENGAGEMENT * Understand our product deeply and speak credibly with data scientists and ML engineers * Communicate technical value in a clear and compelling way * Shape tailored messages and outreach sequences for technical personas BUILDING THE MOTION * Help define qualification criteria, outbound sequences, demo structure, and objection handling - the playbook you help write is what we hire against next * Bring field intelligence back to the team on objections, positioning, and what customers actually care about WHAT YOU BRING * 3+ years in a full-cycle AE role at a data infrastructure, ML tooling, developer tools, or deep tech company * Track record closing enterprise and mid-market deals with technical buyers — data scientists, ML engineers, or engineering leadership * Strong technical literacy - comfortable holding a credible conversation with an ML team * Proven outbound execution and pipeline generation from scratch * Builder mentality - you want to figure out how to sell this, not inherit someone else's playbook * Bonus: hands-on experience with data science or ML workflows, or background selling to ML platform teams WHY THIS ROLE IS EXCITING * First AE at a company with real research credibility, a Nature publication, and production customers with the world's largest enterprises * Direct line to founders - you'll shape commercial strategy, not just execute it * Genuine category creation and bringing frontier AI to industry * Clear path into sales leadership as the team scales LOCATION * This role is specifically located in our New York office Life at Prior Labs We're a small, ambitious team solving one of the hardest problems in AI, and we're just getting started. You'll work closely with world-class researchers and builders who care deeply about the quality of their craft, the impact of their work, and the people they work with. We move fast, we think rigorously, and we take the time to do things right. If you're excited by hard problems, motivated by real-world impact, and want to be part of building something that matters, we'd love to hear from you. We're building our teams in Berlin, Freiburg, and New York and we believe that when you're working on something as hard and exciting as TabPFN, being in the same room matters. Most of our roles are based in one of our offices but great people come from everywhere, and in exceptional cases we're open to remote. This usually involves frequent travel to one of our offices and the whole company comes together regularly for offsites to think, build, and celebrate together. Our Commitments We believe the best products and teams come from a wide range of perspectives, experiences, and backgrounds. That's why we welcome applications from people of all identities and walks of life, especially anyone who's ever felt discouraged by "not checking every box." We're committed to creating a safe, inclusive environment and providing equal opportunities regardless of gender, sexual orientation, origin, disability, or any other trait that makes you who you are. We care about how your data is handled. Read our Recruiting Privacy Notice to see exactly what we collect, why, and how long we keep it.

Prior Labs

WHO WE ARE Foundation models transformed text and images. Structured data - the largest and most consequential data format in the world - stayed untouched. Tables run every clinical trial, every financial model, every scientific experiment, every business decision, and no one had built a foundation model that truly understood them. Until now. What LLMs did for language, we're doing for tables. The next modality shift in AI is happening, and we're hiring the team that makes it. Momentum. We pioneered tabular foundation models and are now the world-leading organization in structured-data ML. Our TabPFN v2 model was published as a Nature cover story and set a new state of the art for tabular machine learning. Since release we've scaled model capabilities 20x+, passed 3.5M+ downloads and 7,500+ GitHub stars, and are seeing accelerating adoption across research and industry - from detecting lung disease with Oxford Cancer Analytics to preventing train failures with Hitachi to improving clinical-trial decisions with BostonGene. The hardest work is ahead. We're scaling tabular foundation models to millions of rows, thousands of features, real-time inference, and entirely new data modalities, while building the infrastructure to run them in production across some of the most demanding industries on earth. These are open problems no one else is working on at this level. Our team. We're a small, highly selective team of 30+ engineers, researchers, and GTM specialists, with backgrounds spanning Google, Apple, Amazon, DeepMind, Meta, Microsoft Research, G-Research, Jane Street, Goldman Sachs, and CERN. We're led by Frank Hutter, Noah Hollmann, and Sauraj Gambhir, and advised by world-leading AI researchers including Bernhard Schölkopf and Turing Award winner Yann LeCun. We ship fast, do top-tier research, and hold each other to an extremely high bar. What's next. In 2025 we raised €9m pre-seed led by Balderton Capital, backed by leaders from Hugging Face, DeepMind, and Black Forest Labs. The next phase of growth is here, which makes this an ideal time to join. ABOUT THE ROLE We are hiring our first Account Executive to drive commercial adoption of our foundation models for structured data. This is a founding role — you'll shape how we sell, who we sell to, and what good looks like for every AE who joins after you. You'll own the full sales cycle across enterprise and mid-market accounts, working directly with the commercial founder and a team of ex-DeepMind, Palantir, Jane Street, Goldman Sachs and Amazon researchers and operators. This role combines a strong outbound focus with the potential for end-to-end deal ownership. You'll spend most of your time on outreach and discovery with technical buyers (data scientists, ML engineers, technical leaders), while also navigating buying committees, developing proposals, and closing deals. You'll help create the materials and messaging that make our outbound motion successful while driving revenue outcomes. We're looking for someone who loves the craft of outbound and wants to build the engine, not just close deals. OUTBOUND AND PIPELINE GENERATION * Drive outbound outreach to high-potential accounts through email, LinkedIn, and community channels * Execute targeted campaigns and iterate based on what works * Qualify inbound leads and identify opportunities worth pursuing SALES CYCLE OWNERSHIP * Run discovery calls and product demos with data science and ML teams * Develop proposals and business cases tailored to customer needs * Navigate buying committees across technical and business stakeholders * Own opportunities from qualification through signed contract TECHNICAL ENGAGEMENT * Understand our product deeply and speak credibly with data scientists and ML engineers * Communicate technical value in a clear and compelling way * Shape tailored messages and outreach sequences for technical personas BUILDING THE MOTION * Help define qualification criteria, outbound sequences, demo structure, and objection handling - the playbook you help write is what we hire against next * Bring field intelligence back to the team on objections, positioning, and what customers actually care about WHAT YOU BRING * 3+ years in a full-cycle AE role at a data infrastructure, ML tooling, developer tools, or deep tech company * Track record closing enterprise and mid-market deals with technical buyers — data scientists, ML engineers, or engineering leadership * Strong technical literacy - comfortable holding a credible conversation with an ML team * Proven outbound execution and pipeline generation from scratch * Builder mentality - you want to figure out how to sell this, not inherit someone else's playbook * Bonus: hands-on experience with data science or ML workflows, or background selling to ML platform teams WHY THIS ROLE IS EXCITING * First AE at a company with real research credibility, a Nature publication, and production customers with the world's largest enterprises * Direct line to founders - you'll shape commercial strategy, not just execute it * Genuine category creation and bringing frontier AI to industry * Clear path into sales leadership as the team scales LOCATION * This role is specifically located in our Berlin office Life at Prior Labs We're a small, ambitious team solving one of the hardest problems in AI, and we're just getting started. You'll work closely with world-class researchers and builders who care deeply about the quality of their craft, the impact of their work, and the people they work with. We move fast, we think rigorously, and we take the time to do things right. If you're excited by hard problems, motivated by real-world impact, and want to be part of building something that matters, we'd love to hear from you. We're building our teams in Berlin, Freiburg, and New York and we believe that when you're working on something as hard and exciting as TabPFN, being in the same room matters. Most of our roles are based in one of our offices but great people come from everywhere, and in exceptional cases we're open to remote. This usually involves frequent travel to one of our offices and the whole company comes together regularly for offsites to think, build, and celebrate together. Our Commitments We believe the best products and teams come from a wide range of perspectives, experiences, and backgrounds. That's why we welcome applications from people of all identities and walks of life, especially anyone who's ever felt discouraged by "not checking every box." We're committed to creating a safe, inclusive environment and providing equal opportunities regardless of gender, sexual orientation, origin, disability, or any other trait that makes you who you are. We care about how your data is handled. Read our Recruiting Privacy Notice to see exactly what we collect, why, and how long we keep it.