Forward Deployed Research Engineer
At a glance
Mid-level Solutions & Sales Engineering role at Composio. San Francisco · full-time.
Pay not stated
Growth Roles summary, based on the employer's posting.
What you'll do
- Take enterprise accounts from discovery through specifications, implementation, quality checks, and release
- Write production code in the toolkit monorepo and customer SDKs, including composite tools and schema changes
- Convert complex enterprise APIs into agent-ready tools, then build evaluations showing whether models choose correctly
- Test toolkits and create repeatable QA harnesses instead of relying on manual checks
- Lead weekly customer meetings, covering summaries, delivery estimates, escalations, and per-customer patches
What you bring
- You have shipped production integrations involving enterprise APIs, OAuth 2.0, consent, scopes, and multi-workspace tokens
- You have owned a named enterprise account from its first conversation through launch, turning unclear requests into specifications
- You have built with language models and for them, treating the agent-tool boundary as an unresolved research area
- You can make and defend architecture decisions with a customer's principal engineers
- Production Python experience is useful, with TypeScript as an additional advantage
Who this fits
This suits a mid-level engineer who combines hands-on integration work with direct technical ownership of enterprise customers. You should be comfortable explaining complex ideas under deadline, building trust, and acknowledging uncertainty. The role is onsite in San Francisco and includes customer meetings, production shipping, and account ownership.
From the employer
At Composio, we are building infrastructure that allows agents to communicate with the tools you use for work including Github, Gmail, Notion, Salesforce, etc. We are a small team of engineers wrangling problems from context to search, that help us provide the most capable bridge between your agents and your tools. We raised a $25M Series A from Lightspeed with angels like Guillermo Rauch (CEO of Vercel), Dharmesh Shah (CTO of Hubspot), and Gokul Rajaram. We've 3x'd our ARR this year, and our customers range from your friends in the YC batch to Wabi, Glean, Zoom and many more.
the action layer for AI agents is the least-solved part of the stack. this role works at that frontier: embedded with the customers pushing hardest, turning open research questions into tools their agents use, shipping the code yourself.
THE FRONTIER YOU'LL WORK ON
• turning an 800-endpoint enterprise API into a tool surface a model can navigate. tool granularity, composite tools, description engineering, scope design. no textbook exists; you write it in production.
• building the evals that tell us an agent picked the right tool. the field has no good answer; your harnesses become ours.
• working directly with the engineers defining what agents can do, and feeding what breaks back into the product.
WHAT YOU'LL DO?
• own enterprise accounts end to end: discovery, spec, implementation, QA, release.
• ship production code in our toolkit monorepo and customer SDKs: custom and composite tools, schema and scope changes, per-customer patches.
• turn large enterprise APIs into agent-usable tool surfaces, and build the evals that prove the model picks the right tool.
• QA toolkits, and build the harnesses that make QA repeatable instead of artisanal.
• run weekly customer syncs: the summaries, ETAs, and escalations that keep an engagement coherent.
• operate per-customer tool-patching as self-service, not escalation.
• dogfood Composio on your own workflows.
"MUST HAVES"
if you are very good, nothing is a must per-se
• applied integration engineering
– you ship production code, not demos.
– you live in enterprise APIs and auth: OAuth 2.0, consent models, scope design, multi workspace tokens. you know the edge cases from scars.
– you make architecture calls under pressure and defend them to a customer's principal engineers.
• customer technical ownership
– you've taken a named enterprise account from first call to live.– you turn vague asks into specs, and push back when the ask is wrong.
• ai native
– you have built with the language models.
– you have built for the language models, and treat the agent-tool interface as an open research problem.
• taste - finger feel for good developer experience.
• typist - you explain complex ideas clearly under deadline.
• human - you build trust and admit what you don't know.
OPTIONAL
• years of Python in production (Typescript a plus).
• API platform, iPaaS, or integration-heavy background.
• prior FDE / solutions-architect role selling to big tech.
• frontier output: MCP servers, tool-use evals, agent-tooling writeups.
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