AI Engineering Tech Lead

What's Addressable?

Addressable empowers Web3 marketers by merging blockchain and social media data to create targeted audiences, launch impactful campaigns, and measure performance. Join our dedicated team for a challenging role with growth potential.

So, what's the job?

We are looking for an AI Engineering Tech Lead to join our R&D team. You will play a crucial role in making AI a team-level capability at Addressable — owning the internal platform, tools, and practices that let every engineer, researcher, and data practitioner work effectively with AI, and using that platform to raise the floor on how we ship.

You will:

• Own and evolve Addressable's internal AI development platform - the agents, integrations, and conventions our R&D team uses daily - treating it as a product measured by adoption and impact.
• Use AI to bring consistent structure to the development cycle: how work is scoped, tested, and reviewed. Build the mechanisms that make engineering rigor the default, reducing production regressions and rework.
• Identify high-leverage AI opportunities across R&D beyond the coding cycle - research, data workflows, documentation, onboarding - and build or integrate the capability.
• Run the AI operations that make the platform work: model selection and routing, context engineering, local inference for data-sensitive work, evaluation, cost management, and security posture.
• Track the AI ecosystem as it evolves, test emerging tools against real Addressable work, and decide what to bring in, build, or skip.
  
  

• Not product AI work - product AI belongs with the teams building those products.
• Not an ML research role - research owns research; this is a parallel discipline, not the same one.
• Not a people-management role, though it may grow one over time.
  
  

• 6+ years of software engineering experience, with time spent at a senior or staff level.
• A track record of building internal developer platforms that engineers actually adopt, production AI/LLM systems with real context engineering and evaluation, or ideally both.
• Hands-on, daily experience with Claude Code and Cursor - you've used them on real codebases, have opinions about where they shine and where they break, and can teach others how to get the most out of them.
• Experience building custom agents, commands, hooks, or MCP servers on top of Claude Code or similar coding platforms.
• Hands-on experience with the broader AI tooling stack - LLM APIs, agent frameworks, MCP, RAG, evaluation pipelines.
• Strong systems thinking. You care about APIs, reliability, observability, and integration points as much as prompts and models.
• Opinionated about engineering rigor. You see AI as a way to raise the floor on how teams work, not lower it.
• A highly motivated self-starter, comfortable making build-vs-buy decisions under uncertainty and measuring impact afterwards. Able to influence peers across R&D without formal authority.
  
  

• Experience leading adoption of internal engineering tooling in a growing R&D organization.
• Experience with local model deployment or on-prem AI infrastructure.
• Experience with LLM evaluation frameworks and LLM-as-a-judge quality systems.