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AI Enablement Engineer- Developer Experience

Job in Somerville, Middlesex County, Massachusetts, 02145, USA
Listing for: Tulip Interfaces
Full Time position
Listed on 2026-08-22
Job specializations:
  • Software Development
    AI Engineer (Applied/Software), Backend Developer
Salary/Wage Range or Industry Benchmark: 130000 - 180000 USD Yearly USD 130000.00 180000.00 YEAR
Job Description & How to Apply Below

This role is located in Somerville, MA - We are a hybrid work environment and are in the office 3+ days/per week. Tulip , the leader in AI-native frontline operations, is helping companies around the world equip their workforce with composable, connected apps, leading to higher quality work, improved efficiency, and end-to-end traceability across operations. Tulip’s cloud-native, no-code platform, powered by embedded AI, is driving the digital transformation of industrial environments through composable, human-centric solutions that go beyond disrupting the Manufacturing Execution System (MES) category.

A spinoff out of MIT, Tulip is headquartered in Somerville, MA, with offices in Germany, Hungary, Singapore, Israel and Japan. Tulip has been recognized as a World Economic Forum Global Innovator, a 2024 Deloitte Technology Fast award winner, one of Energage’s Top Workplaces USA, and one of Built In Boston’s “Best Places to Work” and “Best Midsize Places to Work.”

About You You're a builder who's been inside the LLM/agentic AI world, and you've turned that experience into tools that engineers rely on. You understand the full process of shipping an agent into production: prompting, tool and MCP design, reliability, evals, the UX of human-AI handoffs, and all the plumbing in between. You thrive when the problem is fuzzy and the timeline is tight.

You find the highest-leverage thing, move fast, and bring engineers with you. You're technically sharp and deeply curious about developer workflows; you want to understand why something slows engineers down, not just what feature to ship. You excel on a small team that can move fast and have direct influence over how engineers at the company work day to day.

You think agentic AI and developers working together is one of the most interesting opportunities of this decade, and you want your fingerprints on the tools, standards, and platform that make that possible here. You're not here to watch it happen, you're here to build it.

Key Responsibilities
  • Partner with engineering teams to identify the highest-leverage AI opportunities in the developer workflow: from code generation and review to testing, debugging, and internal tooling.
  • Build and maintain the internal agentic AI platform engineers rely on daily: skills, plugins, MCP servers, and integrations across our tech stack (source control, CI/CD, observability, ticketing, and more)
  • Own foundational architecture and standards for how agents and model releases are evaluated, and deployed internally: including reliability, evals, efficiency, and secure tool access patterns
  • Build and improve the onboarding, documentation, and discovery layer that helps engineers find and adopt the right AI tools (skills catalogs, guides, internal wikis) rather than reinventing them
  • Stay plugged into agentic AI and developer-tooling trends externally to shape internal standards and roadmap decisions
  • Meaningfully contribute to organizational AI enablement for engineering, including internal learning programs, office hours, and change management as new tools roll out
  • Instrument and measure adoption and impact of internal AI tooling, and use that data to prioritize what to build next
What Skills or Experience Do I Need?
  • 5+ years of software engineering experience, ideally with a focus on developer tooling, platform engineering, or internal-facing/DevX systems
  • Meaningful hands-on experience building with AI/LLMs and shipping agentic solutions. Whether that's a career focus, or a more recent but deep pivot into the space
  • Proven ability to build internal tools that make powerful AI capabilities accessible to engineers, including the judgment to know when to build vs. buy, and when to standardize vs. let teams experiment
  • A natural collaborator who earns trust with engineers quickly, understands real engineering pain points, and co-creates solutions that get adopted rather than shelved
  • Strong full-stack proficiency (Type Script and related ecosystem), deep familiarity with API design and integration (RESTful services, MCP, and similar), and comfort working across a modern engineering tech stack
  • Deep hands-on experience with LLMs, prompt…
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