AI Engineer

At Thesis, we believe everyone should have the opportunity to meet their potential. We build nootropic supplements and customer experiences that help people find the right support for their goals.

Across Thesis and Stasis, we're also changing how our company works. Our teams use AI to develop creative, research customers, build digital experiences, analyze performance, and reduce repetitive operational work. We're looking for an engineer who can turn those experiments into reliable systems that people use every day.

We're looking for an AI Engineer to build the agents, integrations, and internal tools that help a small team accomplish more. You'll work with Data, Engineering, Growth, Creative, and Operations to find valuable problems, prototype solutions, and own them through deployment, adoption, and ongoing improvement.

This is a hands-on software engineering role focused on applied AI. You'll build with existing models, connect them to our tools and knowledge, and make their outputs useful and trustworthy. You should be as comfortable debugging an API integration as designing an agent workflow, and care about whether your work saves time, improves decisions, or helps the business grow.

We already have AI-assisted workflows and a shared Claude skill and knowledge system. You'll build on that foundation, making successful workflows easier to reuse and bringing production engineering to the parts that need it. You won't be starting from a blank page, and you won't be limited to maintaining what's already there.

• Design and ship agents that retrieve information, use tools, and complete multi-step tasks across our business systems.
• Connect workflows across tools such as Slack, Google Workspace, Asana, Shopify, and Klaviyo through APIs, webhooks, and MCP integrations.
• Make long-running workflows dependable with persistent state, retries, duplicate prevention, clear approvals, and recoverable failures.

• Partner with Creative and Growth to improve the path from customer research and performance insights to briefs, scripts, creative variants, and testable landing pages.
• Build workflows that combine brand knowledge, approved product claims, asset libraries, and creative performance data while keeping human review where it matters.
• Help teams learn from prior experiments so each new brief or test benefits from what we've already discovered.

• Extend our shared skills, knowledge base, retrieval, and context-management systems so AI tools can find and use the right source material.
• Partner with our data team to connect AI workflows to governed analytics in Metabase and Hex, backed by Snowflake, rather than inventing competing definitions of business metrics.
• Use SQL and data validation to investigate issues, verify outputs, and make analytical workflows easier for non-technical teammates to use.

• Build evaluations using real business tasks. Test accuracy, task completion, tool behavior, failure recovery, latency, and cost before scaling a workflow.
• Add monitoring, useful logs, version control, automated tests, and clear documentation. Own failures after launch and fix their causes.
• Protect customer and company data, separate brand and account contexts, and preserve human approval for consequential actions.
• Choose the simplest approach that works, including ordinary code or deterministic automation when an LLM adds no value.

• Work directly with teammates to understand their workflows, ship usable interfaces, and help them adopt what you build.
• Create reusable skills and integrations rather than isolated prompts that only their author can operate.
• Evaluate new models and tools through practical experiments, then explain what is worth adopting and why.
• Measure outcomes such as time saved, adoption, output quality, and faster experiment cycles, not just the number of automations shipped.

• Experience shipping software that real users depend on, with ownership beyond the initial prototype.
• Hands-on LLM application development using Anthropic, OpenAI, or comparable APIs, including structured outputs, tool calling, retrieval, and multi-step workflows.
• Strong Python or TypeScript skills, sound software design, and comfort working with APIs, authentication, databases, and asynchronous jobs.
• Practical understanding of RAG, context management, and MCP. You can explain the tradeoffs and show when a simpler approach is better.
• Ability to build evaluations, diagnose unreliable behavior, and improve an AI system using evidence rather than prompt tweaking alone.
• Working SQL proficiency and experience validating data against established reporting or warehouse models.
• Product judgment and clear communication. You can turn an ambiguous request into a focused solution, explain technical tradeoffs, and work effectively with non-technical partners.
• Curiosity, initiative, and follow-through. You experiment independently, learn quickly, and take responsibility for whether your work is useful.

Experience with Claude Code, Codex, shared agent skills, or agent orchestration platforms such as OpenClaw is a plus. So is experience with ecommerce, consumer subscriptions, creative production, multimodal generation, or analytics tools such as PostHog and Metabase. We don't expect you to have used every tool in our stack.

You'll work on problems with visible business impact, from reducing manual reporting to making creative production and customer research more effective. You'll partner closely with the people doing the work, test with real inputs, and keep improving the system after it ships.

We value people who can move quickly without confusing a convincing demo with a dependable product. Bring strong engineering fundamentals, good judgment about where AI helps, and a willingness to get into the details.

Tell us about an AI system you built that people actually use. What problem did it solve, what did you personally build, how did you evaluate it, and what failed along the way? Share a demo, repository, or architecture walkthrough where possible. Please don't include confidential information from another employer.

Meet Your Potential: Keep learning, seek honest feedback, and help others grow.

Own Outcomes: Take responsibility for results, act with urgency, and follow through.

Lead with Science and Data: Seek the truth through evidence and use it to make better decisions.

Create Exceptional Experiences: Show up for teammates and customers with care, high standards, and a sense of belonging.

Our salary ranges reflect competitive compensation for our size and industry and are one part of a total rewards package that includes benefits and equity. The base salary hiring range for this position is $160,000–$200,000. Final compensation will be based on the candidate's location, skill level, and experience.