Bio Protocol

• Validate AI outputs using computational methods - simulations, structural analysis, database cross-referencing • Translate AI predictions into executable protocols for cloud labs (Emerald, Strateos) or technical specs for CROs • Build and maintain validation pipelines that catch errors before they waste lab resources • Manage the "hand-off" between digital predictions and physical experiments • Transform JSON outputs from our agents into experimental protocols and validation workflows

• Own Bio’s portfolio-level scientific direction • Review projects launching on Bio Protocol, maintain a clear portfolio view of what's strongest and most promising • Translate AI outputs into lab-validated science • Work closely with Bio's AI team to take AI-generated hypotheses and move them into experimental validation • Design and oversee the processes that turn computational insights into real, testable scientific results • Build scalable processes to assess scientific credibility, risk, and potential across projects. • Develop and execute a strategy to bring world-class researchers into Bio's ecosystem. • Represent Bio's scientific credibility externally.

• Own the social media strategy and content calendar for scientific projects, such as the peptide agent project as well as Bios, primarily on X (Twitter) and LinkedIn • Create thought leadership content that positions the project as a serious scientific endeavor - technical blog posts, discovery announcements, case studies, and explainer threads • Translate complex peptide science and AI research workflows into accessible, engaging social content that resonates with researchers and the scientific community • Develop and produce visual content including protein folding animations, data visualizations, and scientific graphics to accompany posts • Coordinate with scientific leads (e.g. our Head of AI) to surface real discoveries and breakthroughs worth amplifying • Support co-marketing efforts with partner organizations (e.g. Lightfold, Adaptive Bio) and coordinate cross-posting with the Bio main account and BIOS • Monitor engagement metrics, experiment with formats, and iterate on what drives attention in the scientific and AI research community • Use AI tools to scale content production - not to replace quality, but to multiply your output

• Ship deployed, user-facing MVPs fast - comfortable with "disposable code" to test market value • Build connectors and integrations for messy external data sources (Fitbit, Whoop, EHRs, public bio-databases) • Translate complex, non-deterministic AI outputs into clean, actionable UI/UX • Prototype new use cases and validate them with real users • Free up the core team to focus on the "Brain" while you own the "App"

• Architect and build high-performance AI agent infrastructure from the ground up • Design and implement on-chain coordination systems (funding mechanisms, governance, research incentives, token flows) • Build fault-tolerant backend services for agent orchestration, data pipelines, and integration with external systems (cloud labs, bio-databases, CROs) • Optimise system performance, throughput, and reliability under real-world research workloads • Lead technical decisions around system design, data models, and scaling strategy • Collaborate closely with product and science teams to translate research requirements into production systems • Establish best practices for code quality, testing, observability, and incident response • Mentor other engineers as the team grows

• Own customer acquisition across all channels: paid social, paid search, SEO, email, content, and website • Plan, execute, and optimize paid campaigns with a focus on CAC, ROAS, and conversion • Continuously test and iterate - creatives, audiences, landing pages, messaging - to improve performance • Build and optimize the end-to-end customer journey from first touch to repeat purchase • Develop email/SMS lifecycle marketing to drive retention and LTV • Analyze performance data daily and make rapid decisions to scale what works • Collaborate with BioDAOs to craft compelling product stories and launch campaigns • Manage and grow organic channels including social media, SEO, and community • Support international fulfillment setup as we scale into new markets

• Build and maintain web applications that serve researchers and the science community • Design and implement APIs, databases, and backend services • Collaborate with designers and product to ship features users love • Contribute to web3 infrastructure and smart contract development • Help shape technical direction and engineering culture as we grow

• Design and execute manual test plans for new features and releases • Build and maintain automated test suites (unit, integration, end-to-end) • Perform exploratory testing to find edge cases and unexpected issues • Integrate testing into CI/CD pipelines to catch regressions early • Test AI agent outputs for accuracy, consistency, and reliability • Collaborate with engineers to define test coverage and quality standards • Document bugs clearly and work with the team to prioritize fixes • Help establish QA best practices as the team grows

• Build and manage our Discord communities - creating channels, setting up bots, designing onboarding flows, and keeping conversations active • Host community events like AMAs, journal clubs, voice chats, and Twitter/X Spaces • Create programming that keeps members engaged and coming back - whether that's highlighting member research, running workshops, or sparking discussions around hot topics • Connect people - identify emerging voices, facilitate introductions, and help members find collaborators • Moderate discussions and maintain a welcoming, inclusive environment • Track community health metrics and experiment with new formats to drive engagement

• Build agent capabilities for planning, tool use, memory, and context management, and ship them into production. • Integrate agents with internal and external tools and data sources (retrieval systems, structured datasets, lab/biomed APIs, spreadsheets, search), with robust schemas and safeguards. • Develop quality and evaluation systems, including unit, regression, and scenario/benchmark tests, telemetry, and automated scoring. • Collaborate with scientists to analyze failure modes and improve performance. • Partner with the knowledge and ontology team to ensure outputs are source-traceable and compliant with provenance standards. • Implement safety measures, guardrails, and sandboxed execution for risky operations. • Optimize performance and reliability through profiling, idempotency, retries, rate limiting, and uptime management. • Instrument data pipelines for supervised fine-tuning and reinforcement learning when needed. • Contribute to the agent platform, including services, APIs, orchestration, CI/CD, and observability.