--- title: 'AI Platform Engineer at Nabla Bio' canonical: 'https://feeny.ai/job/ai-platform-engineer-nabla-bio-boston-x7wjbrnnvzyz' type: 'job' last_seen: '2026-09-09' --- # AI Platform Engineer at Nabla Bio - **Company:** Nabla Bio - **Location:** Boston, MA - **Employment:** full-time - **Work type:** onsite - **Posted:** 2025-08-25 - **Last confirmed live:** 2026-09-09 - **Apply:** https://jobs.gem.com/nabla-bio/am9icG9zdDoA8CiE8OU6Z5HZSl6XVtSD ## Job description ## The Role We’re hiring a senior engineer to build core infrastructure and tools that connect our AI models to real-world use. You’ll work across the full stack — front-end, back-end, and ML infrastructure — to build systems that close the gap between idea and implementation for our AI and wet-lab scientists. Your work will enable faster design cycles, tighter feedback loops, and more seamless collaboration across disciplines. This is a high-impact, hands-on role that directly supports our scientists, collaborators, and pharma partners. You’ll build APIs, dashboards, training and inference pipelines that turn frontier AI models into production-grade tools for drug design. Another focus will be accelerating scientific workflows with LLMs — building systems where AI helps propose and learn from experiments to drive faster cycles of discovery. It’s a rare opportunity to engineer this loop with real-world, large-scale experimental feedback (1 million drug designs measured every month). See our papers for examples of our work [[1](https://www.biorxiv.org/content/10.1101/2025.01.21.633066v1.full.pdf)][[2](https://www.biorxiv.org/content/10.1101/2025.05.28.656709v1.full.pdf)], and their coverage in [Science Magazine](https://www.science.org/content/article/ai-conjures-potential-new-antibody-drugs-matter-months) and [Endpoints News](https://endpoints.news/nabla-bio-moves-closer-to-ai-created-antibodies/). This is an in-person role in Cambridge, MA. You will: - Build and maintain user-facing applications (UIs, APIs, and dashboards) that reduce the idea-to-implementation gap in drug design workflows - Develop robust ML training and inference systems to accelerate experimentation during model development and expose our models through scalable back-end services - Collaborate closely with scientists to understand pain points and ship high-leverage tools, including AI-augmented experiment planning and analysis - Own full-stack development from prototype to production, including deployment and observability - Leverage modern AI dev tooling to move fast and stay focused on high-value work ## Qualifications - 5+ years of experience as a full-stack, ML infra, or platform engineer - Experience building backend systems that serve ML models in production - Strong frontend development skills (React, TypeScript, etc.) - Deep fluency in Python; familiarity with cloud-native tools and containerization (Kubernetes, Docker) - Strong product taste, sense, and ability to partner with wet-lab scientists and AI researchers - High agency and a track record of shipping quickly with quality ## What We Offer - A fast-moving environment where you can build and ship tools that impact real drug programs - The opportunity to shape how cutting-edge AI and LLMs are used to accelerate scientific discovery - Access to modern ML models and wet-lab infrastructure for rapid experimentation - A small, focused team where your engineering decisions have outsized impact - Highly competitive salary, equity, and benefits package ## About Nabla Bio ## Company Overview - **One-liner**: Nabla Bio uses AI and large-scale wet-lab experimentation to design antibodies against previously undruggable disease targets. - **Entity Type**: Private (Seed-stage; $37M total funding) - **Headquarters**: Cambridge, Massachusetts, USA - **Founded**: 2020 (Y Combinator Summer 2020) - **Founders**: Surge Biswas and Frances Anastassacos ## Core Business - **Industry**: AI-powered drug discovery / Biotechnology (Antibody therapeutics) - **Target Customers**: B2B – large pharmaceutical companies (AstraZeneca, Bristol Myers Squibb, Takeda) - **Mission**: "Make drug development a true design discipline — reducing trial-and-error so better medicines can reach patients faster and with greater confidence." ## Products & Services - **Generative Drug Design Platform**: Combines de novo AI antibody design with high-throughput, human-relevant wet-lab testing (binding, developability, cellular function, and in-vivo performance). The integrated dry/wet-lab system is built and owned in-house to enable an iterative design loop. ## Market Standing - **Valuation/Market Cap**: Not publicly available - **Key Metric**: $37M in total seed funding (backed by Radical Ventures, Khosla Ventures, and Zetta Venture Partners) - **Notable Investors/Partners**: Radical Ventures, Khosla Ventures, Zetta Venture Partners, Y Combinator. Partners include AstraZeneca, Bristol Myers Squibb, and Takeda. - **Growth Signals**: Revenue-generating since early stage; secured high-profile pharma partnerships (Takeda deal announced October 2025); continuing to hire top ML and synthetic biology talent. ## Competitive Advantages - **Integrated dry/wet-lab engine**: Owns and controls the data, AI models, and experimental validation in one loop, rather than just providing software. - **Focus on undruggable targets**: Aims to double the number of disease-relevant drug targets accessible to the industry. - **Patient-relevant testing**: Wet-lab assays measure properties that matter in vivo, not just computational predictions. ## Strategic Focus - Scaling the platform to design drugs with pre-specified properties (binding, safety, manufacturability). - Deepening partnerships with top pharma companies to move designed molecules toward clinical development. - Expanding the frontier of measurable disease targets. ## Why Work Here - **Culture**: "Fully in-person and based in Cambridge, MA." The team values deep platform investment, high scientific standards, side-by-side collaboration, and learning quickly from results. - **Work environment**: On-site only (Riverside Technology Center, Cambridge). The company emphasizes focus, urgency, and a "problems rather than prestige" mentality. - **Team size**: ~22 employees (as of latest data), tight-knit mix of wet-lab and dry-lab scientists. - **Open roles**: AI Platform Engineer, AI Scientist, and Applied AI/Bio Scientist – all based in Boston, MA. ## Sources 1. [nabla.bio](https://www.nabla.bio/) 2. [nabla.bio/careers](https://www.nabla.bio/careers) 3. [jobs.gem.com/nabla-bio](https://jobs.gem.com/nabla-bio) 4. [builtin.com/company/nabla-bio](https://builtin.com/company/nabla-bio) 5. [ycombinator.com/companies/nabla-bio](https://www.ycombinator.com/companies/nabla-bio) ## Other roles at Nabla Bio - [AI Scientist](https://feeny.ai/job/ai-scientist-nabla-bio-boston-841p9j67tmnh) — Boston, MA - [Applied AI/Bio Scientist](https://feeny.ai/job/applied-ai-bio-scientist-nabla-bio-boston-fvzjj9kynby1) — Boston, MA - [AI Platform Engineer](https://feeny.ai/job/ai-platform-engineer-0g-labs-china-4aw93c4vf6tb) — China - [AI Platform Engineer](https://feeny.ai/job/ai-platform-engineer-onepay-united-states-y94x83p5r581) — United States - [AI Platform Engineer](https://feeny.ai/job/ai-platform-engineer-satoshilabs-prague-cwt4cpsbfv5j) — Prague, Czech Republic - [AI Platform Engineer](https://feeny.ai/job/ai-platform-engineer-tessera-labs-in-the-m446aa6jfrdt) — IN the, United States - [AI Platform Engineer](https://feeny.ai/job/ai-platform-engineer-airbyte-san-francisco-rkcjgemmf3zg) — San Francisco, CA - [AI Platform Engineer](https://feeny.ai/job/ai-platform-engineer-tailor-tokyo-68ysb9yfks1n) — Tokyo, Japan - [AI Platform Engineer](https://feeny.ai/job/ai-platform-engineer-qube-research-technologies-mumbai-kem2fhrscqp0) — Mumbai, India - [AI Platform Engineer](https://feeny.ai/job/ai-platform-engineer-paypay-card-remote-gq37w2h6ezqp)