What the role involves
About us
Infer is building the operating system for insurance agencies. We make AI agents(including voice agents) that handle the work agencies have always done by hand: qualifying inbound leads, helping producers during live calls, auditing calls after, running renewals, and bringing churned customers back.
Our long bet is that AI eventually sells insurance directly. Agencies are the wedge because that is where the work, the data, and the customer relationships actually live. Get good there, and the rest follows.
We are a YC company, ~$6M raised from YC, Stellaris Venture Partners, Ahead VC & others. Founders are: Vaibhav, Urvin and Suneel. Vaibhav was an architect and AI researcher(at Purdue) now a licensed insurance agent. Urvin worked at BCG, is a surfer with six pack abs. Suneel is an IITian and a philomath.
Why join us(we can give you many reasons)
- We're highly transparent founders to work with, even when things go down.
- We like pushing each other to test the limits because that's when you rediscover yourself.
- We default at trusting people and being good at heart (we crack bad jokes, banter, make fun of ourselves & try to build a company that won’t be killed by AGI)
- Finally, we love people who’re interdisciplinary
About the role
We're hiring our first US Forward Deployed Engineer to own customer deployments end to end. You'll work directly with insurance agencies, build integrations into the systems they already use, and turn messy real-world workflows into AI agents that work in production.
Building an AI agent is only one part of the job. The harder part is making it work inside an agency's actual operating system.
Some insurance systems have clean APIs. Some have outdated docs. Some have no API at all. You'll decide what to build, what to automate, what to work around, and what should become reusable for the next account.
A lot of insurance work is not written down. It lives with the agency owner, the senior CSR, or the producer who has been doing it for 15 years. Your job is to extract that, turn it into system design, and ship it.
What you'll do
You'll own deployments from scope to go-live. That means working with agency owners and ops leads, understanding how they actually run, building connections into their existing systems, getting our AI agents handling real calls, and finding the next thing we can take over inside the account.
What success looks like
By day 30
- You understand how our agents work across prompts, tools, evals, telephony, and customer systems.
- You are joining customer calls and implementation workstreams.
- You are starting to own active customer relationships.
- You have shipped a few improvements, such as prompt changes, tool fixes, or new eval cases.
By day 60
- You are diagnosing and resolving customer issues directly.
- You are shipping quick fixes that unblock go-lives.
- You are delivering small customizations that make our agents fit how agencies actually operate.
- You have found a second use case inside at least one account, scoped it with the customer, and started building it.
- Most accounts start with one product. Expansion comes from constant discovery and a tight cadence with the customer, not a quarterly check-in.
By day 90
- You have built or specced something that makes the next deployment materially faster.
- This could be a reusable connector, config layer, eval harness, runbook, or platform improvement.
- You are spotting failure patterns across accounts and turning them into product fixes.
- The same problem does not get solved twice because you have fed the fix back into the platform.
What we're looking for
- Engineering chops. At least 2 years shipping production code, fluent in Python, comfortable with APIs and multiple systems talking to each other. Familiarity with voice or telephony stacks (Twilio, or similar) is a strong plus.
- Production AI experience. You have shipped at least one AI application to real users and you know what it takes to make an LLM behave the same way on call 1,0