Lead Sales and Product Engineer

The Biological Computing Co. (TBC) is harnessing the brain’s intelligence to evolve how we compute. Our platform integrates living neurons with modern AI systems, creating frontier models that are more stable, scalable and dramatically more efficient. We are the first to deploy neuron-based alternatives to brute force scaling, with applications across computer vision, generative video, world models and next-generation AI infrastructure.

TBC’s neuroscientists, computational AI engineers and neurobiologists come from institutions such as Apple, John Hopkins, Meta, MIT, and Stanford.

We are hiring a Lead Sales and Product Engineer to help accelerate TBC’s SOTA AI research and technology into models and applications that developers, customers and partners can immediately understand and use. You will lead the integration of product/engineering and sales/marketing. You will build the applications of our AI infrastructure and neural-based algorithm products: demos, sample apps, docs, marketplace listings, partner integrations, technical website content and GTM automation.

This is a builder role: you will not just advance sales deals; you will shape how we win them.

This role is ideal for someone who loves pre‑sales, building, and problem solving, and who is energized by ambiguous environments where the playbook is still being written. You should be comfortable building well‑designed applications, writing full‑stack code, demonstrating technical products, presenting to customers and moving quickly in an early‑stage startup environment where accelerating frontier AI is our team’s mission. In this role, you will:

• Design, build, and present compelling demos of TBC's AI models and products for prospects, customers, partners, and at events.
• Build sample applications, reference implementations, and prototypes that help customers understand and adopt TBC technology.
• Create and publish repos, demos, docs and sample code on model marketplaces such as Hugging Face and developer platforms such as Git Hub.
• Demo at AI events locally and globally, representing the company on stage, at booths, hackathons, and AI meetups.

• Run technical discovery, scope and write up use cases, and deliver implementations.
• Own technical clarity during customer evaluations by answering questions model behaviorAPIs, infrastructure, benchmarks, and deployment.
• Write proposals, responses, and technical validation materials during deal cycles.
• Capture customer and developer feedback, identifying product gaps, documentation needs, and adoption blockers.
• Partner with Engineering and Product to improve the product’s usability, platform quality and reliability, and increase customer satisfaction.

• Create and evolve our developer platform with infrastructure partners AWS/GCP/MSFT: the surface area on which developers discover, evaluate, and ship our AI technologies.
• Build internal tools, automations, and workflows that scale up sales execution by automating lead generation, qualification, and CRM operations.
• Create customer‑facing tools that make TBC's technology easier to evaluate, deploy, and support.

• Have experience in a pre‑sales sales engineering, post‑sales solutions consulting, forward deployed engineer, or developer‑relations/evangelist role.
• Have experience publishing repos, using and modifying open‑source code, writing applications and documentation.
• Strong with front‑end frameworks, backend APIs, integrations, data tools and developer infrastructure tools.
• Can present beautifully to these personas: developers, business users and executives.
• Have product‑to‑GTM experience and know how to turn a technological capability into a usable demo or application.
• Can hold an opinionated conversation about at least two domains: frontier AI models, AI infrastructure, neuroscience, quantum computing, or biological computing.

• Experience working at a native AI app or AI infrastructure startup, or startups in general.
• A track record of technical communication: talks, blog posts, or developer community building.
• Have a working knowledge of cloud infrastructure for AWS, GCP or Azure, and can work with partners on deploying our AI models.
• Experience shipping AI/LLM‑powered applications, or enterprise infrastructure or enterprise applications.