Applied AI EngineerYou9ll report to the Engineering Manager for Tools and Growth. The Tools team owns Arcade9s tool catalog - thousands of tools across many services, growing faster than any human can review by hand. The next leap in agent quality lives inside this team9s work, and you9ll be the applied-AI seat that pushes it forward.
Three real problems define the role.Agentic tools vs. deterministic tools. Most tools today are deterministic: call X API with Y arguments, get Z result. That model breaks down for entire classes of agent work - research a topic, summarize a thread, decide which of three accounts to act on. Agentic tools, the ones that internally reason, plan, or call models are the answer, but the design space is wide open. When is agentic better than deterministic? How do you make an agentic tool fast, reliable, and debuggable? You9ll set the bar for what these look like at Arcade.
Agents that build tools. The toolkit catalog is too big for hand-crafting to scale. We need agent harnesses that can take a vendor9s API and produce a high-quality toolkit - design, code, eval, docs with a human in the loop only where the human is actually needed. There9s early work on this already. You9ll take it from a prototype into the production pipeline that produces the next thousand tools.
Workflows that compose tools. Individual tools solve narrow problems. Real customer outcomes: 4close the quarter,4 4triage the inbox,4 4stand up the integration4 need many tools, chained, with the right control flow. We need to figure out what the right primitive looks like above the tool layer, and you9ll lead that design.
The most honest thing we can say about this work: most of the problems you9ll be solving didn9t exist three months ago. There9s no prior art. There9s no known solution. If that9s the part of the job that makes you nervous, this isn9t the right role. If that9s the part that makes you lean in, it is.
We do real experiments. We form hypotheses. We publish learnings. Research is part of the job. But the role is built around shipping. If you want to spend six months proving an idea in a notebook before anything reaches a customer, this isn9t the right role. If you want to ship the experiment and the writeup in the same quarter, it is.
What You9ll Do- Design and ship agentic tools that go beyond deterministic API wrappers - and define the patterns the rest of the Tools team will use to build more.
- Build the agent harness that automates tool creation - take a vendor9s API, produce a high-quality toolkit end-to-end, keep humans in the loop only where humans add real value.
- Design workflows that compose tools into higher-level abstractions customers can actually point at outcomes (4triage this inbox,4 4close out this account4) rather than individual API calls.
- Bring applied-ML rigor to tool design - evals, model-aware iteration, retrieval, tool description tuning, response shaping. Make decisions defensible with data.
- Run model-aware experiments across Claude, GPT, Gemini - agentic tool behavior diverges across models in ways nobody else is studying, and we should.
- Set the technical bar for what 4good tool-building4 looks like as the team scales - your patterns get inherited by every toolkit author after you.
- Contribute back to the MCP and agent ecosystem where the conversation about agentic tools is forming.
Required Skills- 5+ years software engineering experience, with at least 2 years shipping production ML or applied-AI systems. Formal title matters less than the work.
- Strong Python.
- LLM application depth - prompting, retrieval, tool use, agent design. You9ve built non-trivial agent systems and know where the rough edges are.
- Experience designing or composing multi-tool / multi-agent workflows that produced real outcomes.
- You9ve built evals at scale - not 4I ran a benchmark once,4 but a measurement system real engineering decisions were made against.
- Statistics fluency - significance, confidence intervals, A/B test design. You can defend whether a small delta is real or noise.
- Comfort across multiple frontier models and reasoning about their behavioral differences.
- A do-er, not a researcher-in-residence. You9d rather ship a working v0.5 next week than a polished v2.0 next quarter.
- Comfort with ambiguity - early team, narrow charter that will expand. You make good decisions with incomplete data.
- An insatiable desire to ship.
Bonus Points- You9ve built agents that build software (codegen agents, harness-style systems, meta-agents).
- Prior work on tool-use specifically - BFCL, C4-bench, ToolBench, MCP eval work, or equivalent.
- MCP ecosystem familiarity - extra bonus if you9ve filed an issue against the spec.
- You9ve worked on agent frameworks (LangChain, CrewAI, AutoGen, Mastra) and have opinions about where they get tool use and workflow composition wrong.
- Prior experience at an API platform, integrations-heavy product, or developer tools company.
Compensation and BenefitsThis role offers a competitive salary, equity, and benefits. Compensation is aligned with the range below and determined based on a candidate9s background, experience, and performance.
Salary Range $179,000-240,000 USD
