Infer

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 and have raised from Stellaris Venture partners and 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. A few reasons to join us: We like pushing each other on team to test the limits because that's when you rediscover yourself. We’re paranoid about making customers succeed (we challenge whats already good) We love people who question-challenge-build. We're highly transparent founders to work with & love getting challenged. Finally, we love people who’re interdisciplinary. About the role We're hiring an Applied AI Engineer to own the system that tells us whether our voice agents are getting better, and to keep them getting better on their own. Voice quality is the product. If an agent stutters, hallucinates a quote, or misses a disclosure, we lose trust, deals, and sometimes compliance footing. The system that catches all of that before customers do is the most important infrastructure we will build this year. Today we run thousands of conversations a day with real prospects. We need a harness that scores every change end to end, a benchmark suite that runs against any new model the day it drops, a red-team pipeline that probes our agents for failure modes, and self-improvement loops that feed production failures back into the eval set. This is an evals and infrastructure role with deep LLM work. You will touch audio, but the center of gravity is the harness and the loops around it. Think of the harness as CI for voice conversations: it runs synthetic and real calls through our stack and scores agent behavior at every layer (STT, LLM, tools, TTS, full call outcomes), so we catch regressions before customers do. New models are coming out every few weeks, so the question is not just whether ours is good today, but whether we can tell within a week if a new open source release should replace it. What you'll do Building and maintaining the eval framework that scores voice agent quality across transcription, LLM reasoning, tool use, TTS, and full-conversation outcomes Design voice agent behavior: system prompts, tool use, conversation flow, error recovery, and guardrails for real-time interactions Drive STT and TTS accuracy improvements by comparing providers, tuning configurations, and running rigorous A/B experiments the team can act on. Drive TTS quality improvements voice selection, latency vs. fidelity tradeoffs, prosody, edge cases Curate and grow our evaluation datasets, including hard-case mining from production traffic You'll build benchmarks we can run against any new model in days, run a red-team pipeline that probes for jailbreaks, hallucinated quotes, and compliance failures, Partner with backend engineers to wire eval signals into CI so regressions get caught before they ship Wire eval signals into CI so regressions block merges, and build self-improvement loops where hard cases from production auto-feed the eval set and our prompts optimize themselves over time. What success looks like Day 30 You understand how our agents work across prompts, tools, evals, telephony, and customer systems. You have shipped a v1 of evals with at least one end-to-end metric the team trusts. You are sitting in on customer call reviews and tagging failure modes by hand to learn where the real problems live. You have one new model (open

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 and have raised from Stellaris Venture partners and 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. A few reasons to join us: We like pushing each other on team to test the limits because that's when you rediscover yourself. We’re paranoid about making customers succeed (we challenge whats already good) We love people who question-challenge-build. We're highly transparent founders to work with & love getting challenged. Finally, we love people who’re interdisciplinary. About the role You take ownership of day-to-day engineering along with founder: architecture, code review, hiring, infra decisions, customer technical conversations. IC-first role. You'll ship code, architect systems, and mentor engineers naturally. We're not hiring a manager, we're hiring the best technical leader we can find and letting leadership emerge through respect and technical excellence. You'll help scale the team of engineers without killing our speed. What you'll do Own the architecture and codebase: lead integration of FDE (customer deployment) work back into production systems Make onboarding new engineers straightforward (going from 6 to 12 fast) Design and document architecture for custom integrations and deployments Set standards for code quality, async patterns, and streaming system reliability Operate as IC first write code that others learn from Mentor 1–2 engineers on projects as capacity allows Help define technical strategy Technical interviews, culture fit conversations, onboarding new engineers Must-haves 5–10+ years shipping production systems that scale Deep experience with async/streaming systems (Python, Go, or similar) Shipped something where you felt the constraints Strong opinions on AI/LLM application architecture — streaming, state management, reliability Experience building or operating AI agent systems in production — orchestrating LLM calls, tool use, multi-step reasoning, or conversation flows that run autonomously Familiarity with agent evaluation and quality infrastructure — designing eval harnesses, benchmarking pipelines, regression testing for AI behaviour, or conversation quality scoring across deployments Architecture maturity — thinks about trade-offs, not best practices Comfortable with the full stack from infrastructure to application Evidence of technical leadership — mentoring, architecture decisions, earning respect through code Voice/audio systems or telephony experience Experience bringing structure to codebases Founded or early employee at a 10–50 person startup

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 and have raised from Stellaris Venture partners and 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. A few reasons to join us: We like pushing each other on team to test the limits because that's when you rediscover yourself. We’re paranoid about making customers succeed (we challenge whats already good) We love people who question-challenge-build. We're highly transparent founders to work with & love getting challenged. Finally, we love people who’re interdisciplinary. About the role Our first Founders Associate: the person who builds and owns the operational backbone of the company as we scale. Product works, customers are paying, team is growing but the internal infrastructure hasn't caught up yet. High-leverage, high-trust role. You'll work directly with the founders across finance, ops, people, and investor relations. You won't wait for instructions you'll see what's broken, what's missing, and what's next, and you'll go build it. What you'll do Finance & Ops Build and maintain the company dashboard revenue, pipeline, customer health, unit economics Burn tracking, monthly financial reports, budgets, forecasts, and actuals Run investor reporting (MIS) Manage external partners accountants, legal/tax advisors, auditors, vendors Handle compliance: business filings (India + US), investor governance, SOC 2, TCPA Draft and negotiate contracts, vendor agreements, and operational policies People & Cross-Functional People ops: onboarding, offer letters, employee handbook, expense policies, benefits, offsites Build KPIs and OKR tracking Investor updates, budgets, ROI analyses, internal communications Track and drive cross-functional projects if something doesn't have a clear owner, it's yours What success looks like By day 30: Dashboard live and updated weekly. Budget built. MIS template locked and first report delivered on time. Expense policy drafted. You've audited every revenue line, customer, bank account, and compliance obligation. You understand the business end to end. By day 60: MIS runs on autopilot. Revenue tracked per product, customer, and employee. Governance systematised. Core policies in place. Must-haves High agency and ownership runs toward problems, not away from ambiguity Systems thinking sees how decisions ripple across the company Communication that works across investors, engineers, and insurance agency owners Trusted with sensitive financial and strategic data Bias toward action ships imperfect over waiting for perfect 2–6 years in ops, BizOps, RevOps, or on a founding team Comfortable owning financial data: P&L, burn, budgets, investor reporting Nice-to-haves Consulting + startup background Ex-founding team seen scale from 0-1 to 1-100 Insurance, fintech, or regulated industry experience Hands-on with AI tools — can build dashboards using Claude Code, Cursor, or similar without engineering support People ops experience: onboarding, policy design, benefits administration Exposure to compliance frameworks Built operational infrastructure where none existed dashboards, reporting, policies, processes

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

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 and have raised from Stellaris Venture partners and 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. A few reasons to join us: We like pushing each other on team to test the limits because that's when you rediscover yourself. We’re paranoid about making customers succeed (we challenge whats already good) We love people who question-challenge-build. We're highly transparent founders to work with & love getting challenged. Finally, we love people who’re interdisciplinary. About the role We're hiring our first dedicated DevOps/Infrastructure engineer. You're joining at a moment where we need to stop patching and start building foundations. We shipped our first product in months because we moved fast. Now with capital and a growing customer base, we're scaling deliberately and that means fixing the infrastructure we built to ship. Every day you're unblocking product and backend engineers who are waiting for preview deployments or debugging prod issues that could've been caught in staging. What you'll do Design and maintain AWS IAM roles, policies, and least-privilege boundaries across our accounts Triage and resolve security issues; harden production against common threat vectors Build optimization for cloud services and APIs (AWS, Twilio, LLM inference, TTS) Own the API key and secrets lifecycle issuance, rotation, scoping, revocation Maintain SOC 2 Type II program: controls, evidence collection, audit prep, add to this controls and security Ship preview deployment infrastructure so every PR gets a reviewable environment Must-haves 3–5 years in a DevOps, SRE, or platform engineering role Production AWS experience (IAM, VPC, ECS/EKS or Lambda, RDS, CloudWatch) Infrastructure-as-code with Terraform (or CloudFormation/Pulumi) CI/CD pipeline ownership (GitHub Actions, CircleCI, or similar) including preview/ephemeral environments Containerization with Docker and working knowledge of Kubernetes or ECS Hands-on secrets management (AWS Secrets Manager, HashiCorp Vault, or Doppler) Demonstrated cost-optimization work right-sizing, savings plans, usage dashboards Comfort with a scripting language (Python, Go, or Bash) for automation Security mindset: vulnerability scanning, patching, incident response Nice-to-haves Direct experience with SOC 2 Type I or II — controls, auditor coordination, evidence automation with Vanta/Drata/Secureframe Optimizing spend on LLM/AI APIs (OpenAI, Anthropic) or real-time voice infrastructure (Twilio, Daily, LiveKit) Observability stack work Datadog, Grafana, Prometheus, OpenTelemetry Background supporting low-latency or real-time systems (voice, streaming, WebRTC) Exposure to SSO/identity federation Open-source contributions to infra tooling Prior early-stage startup experience where you wore multiple hats

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 and have raised from Stellaris Venture partners and 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.  A few reasons to join us: We like pushing each other on team to test the limits because that's when you rediscover yourself. We’re paranoid about making customers succeed (we challenge whats already good) We love people who question-challenge-build. We're highly transparent founders to work with & love getting challenged. Finally, we love people who’re interdisciplinary. About the role You'll be a core builder on the team that ships our voice-AI platform owning the slice of the stack where real-time performance, test infrastructure, and production reliability meet. You'll drive down voice bot latency, keep our voice pipeline healthy across version bumps, and build the observability and test loops that let us ship fast (and increasingly let agents ship for us). What you'll do Optimize end-to-end voice bot latency and turn-taking behavior across STT → LLM → TTS pipelines Scale backend infrastructure to handle concurrent voice sessions reliably Build test infrastructure for voice agents that makes correctness easy to verify on every PR paving the way for agent-generated patches Design and maintain routing-setup tests for our voice flows Run monitoring and alerting; triage production issues and close the loop back into tests Enable agent-driven patching of minor production issues, with humans staying in the loop on the right things Must-haves Experience building production backend systems, ideally in real-time, streaming, or low-latency contexts Hands-on experience with async/concurrent systems in python asyncio, event loops, streaming pipelines Deep familiarity with one or more voice/AI building blocks: STT, TTS, LLM streaming, SIP/WebRTC or telephony Proven track record optimizing latency in a production system (profiling, tracing, p95/p99 work) Solid database fundamentals: schema design, indexing, query optimization, and migration discipline Solid test engineering instincts: unit, integration, and end-to-end testing for systems with non-deterministic components Worked with distributed tracing tools (OpenTelemetry, Sentry, Datadog APM) Production observability experience instrumenting services, designing useful alerts, running incident response Comfortable with infra work Docker, AWS, CI/CD Comfort owning a system end-to-end: design, ship, monitor, debug, iterate Early-stage startup experience and the instinct to ship pragmatically Contributed to open-source voice or real-time AI infrastructure Direct experience with voice agents orchestration frameworks Nice-to-haves Built or maintained eval/test harnesses for LLM or agent-based systems Experience with agent-driven workflows in production codebases Background in turn-taking, VAD, or endpointing optimization for conversational AI