Asks for

The ad says“Infra: AWS, Docker, Kubernetes; CI/CD with GitHub Actions.”
The ad says“Expertise in observability (OpenTelemetry/Prometheus/Datadog), feature flagging, or A/B platforms.”
The ad says“Infra: AWS, Docker, Kubernetes; CI/CD with GitHub Actions.”
The ad says“Experience with LLM/agent systems, evals, and prompt tooling; RAG, embeddings, vector dbs.”
The ad says“Background in enterprise security and compliance (SOC 2, GDPR)”
The ad says“Deep knowledge of event‑driven systems, streaming, and workflow engines.”
The ad says“Backend: Python, FastAPI”
The ad says“Expertise in observability (OpenTelemetry/Prometheus/Datadog), feature flagging, or A/B platforms.”
The ad says“Background in enterprise security and compliance (SOC 2, GDPR)”
The ad says“Infra: AWS, Docker, Kubernetes; CI/CD with GitHub Actions.”
The ad says“Infra: AWS, Docker, Kubernetes; CI/CD with GitHub Actions.”
The ad says“Experience with LLM/agent systems, evals, and prompt tooling; RAG, embeddings, vector dbs.”
The ad says“Data/ML: MongoDB, S3, Redis; Pinecone”
The ad says“Frontend: React, Next.js”
The ad says“Expertise in observability (OpenTelemetry/Prometheus/Datadog), feature flagging, or A/B platforms.”
The ad says“Expertise in observability (OpenTelemetry/Prometheus/Datadog), feature flagging, or A/B platforms.”
The ad says“Data/ML: MongoDB, S3, Redis; Pinecone”
The ad says“Expertise in observability (OpenTelemetry/Prometheus/Datadog), feature flagging, or A/B platforms.”
The ad says“You’re a systems thinker with deep technical chops: you design clear architectures and write clean, tested Python and TypeScript.”
The ad says“Experience with LLM/agent systems, evals, and prompt tooling; RAG, embeddings, vector dbs.”
The ad says“Frontend: React, Next.js”
The ad says“Data/ML: MongoDB, S3, Redis; Pinecone”
The ad says“Data/ML: MongoDB, S3, Redis; Pinecone”
The ad says“Background in enterprise security and compliance (SOC 2, GDPR)”
The ad says“Messaging: SQS; event‑driven pipelines.”
The ad says“You’re a systems thinker with deep technical chops: you design clear architectures and write clean, tested Python and TypeScript.”
The ad says“Experience with LLM/agent systems, evals, and prompt tooling; RAG, embeddings, vector dbs.”

A little about us…

Knit is the AI-native consumer research platform helping brands automate and accelerate primary research. With our Researcher-Driven AI, we’ve condensed the entire quant + qual research process from weeks into days (sometimes hours!) for 50+ enterprise brands — including Amazon, T-Mobile, Mars, NASCAR, and more. We’re on a mission to scale and democratize world-class research. From survey generation to stakeholder-ready reports, our platform is redefining how insights teams operate — and we need your help to push the limits of what’s possible.

Overview

We’re continuing to build out our NYC engineering team and are looking for exceptional builders who are resourceful, deeply technical, and thrive in high-ownership environments.

As an early member of the team, you’ll play a key role in shaping the core of our platform and contributing to a team that will scale to 20+ engineers over the next 18 months. You’ll build and orchestrate AI agents, explore new UX paradigms, and work on some of the hardest problems in applied AI with meaningful ownership across the stack.

Responsibilities | What you will own...

Primary responsibilities of this role:

Own 0→1 and 1→N features across the stack, from design docs to building high-throughput data pipelines and scalable FastAPI services.
Architect and own the core agent orchestration engine, designing complex, multi‑agent LLM workflows (routing, tool‑use, memory, evaluation) with deterministic fallbacks, guardrails, and auditable actions required by enterprise users.
Build AI‑native, human‑in‑the‑loop UIs. Craft React/Next.js interfaces that let users collaborate safely with agents, with transparency and explainability by design.
Develop the "factory for the factory". Build our experimentation platform: CI/CD for prompts and agents, offline/online evals, drift detection, safety layers, and rollout controls.
Ship fast, safely. You’ll instrument everything for reliability, write concise design docs, add robust tests, and build dashboards and alerts for features you launch.
Partner cross‑functionally with Product, AI, Design, CS; talk to customers and SMEs, and turn ambiguous problems into shipped product.
Obsess over performance, optimizing for latency across the system: caching, model selection, distillation, batching, vector search, and infrastructure efficiency (GPU/CPU).
Operate what you build. This includes on‑call ownership, leading incident response, and driving learning through blameless postmortems.
Level-up the team around you. Mentor and collaborate with other engineers to help make the entire team better.

This role is for you if...

You’re a 0 → 1 builder who thrives in ambiguity and has shipped products from scratch at a startup or in a hyper-growth phase. You've seen both - early-stage hustle and later-stage scale and know how to build for it.
You’re a systems thinker with deep technical chops: you design clear architectures and write clean, tested Python and TypeScript.
You think like an owner: you spot problems, propose solutions, and build them without waiting for perfect specs. Your impact is measured in business outcomes.
You use AI as leverage. Tools like Cursor, Copilot, or Claude Code are integral to your workflow, helping you ship faster while maintaining a high standard for correctness.
You believe speed and quality aren’t opposites and can articulate the tradeoffs you make.
You communicate crisply. You share your ideas effectively and thrive in tight feedback loops with product, design, and customers.
You’ve got grit: hard problems energize you, context switching doesn’t scare you, and you’re comfortable deleting code.

Our Stack

Frontend: React, Next.js
Backend: Python, FastAPI
Data/ML: MongoDB, S3, Redis; Pinecone
Infra: AWS, Docker, Kubernetes; CI/CD with GitHub Actions.
Messaging: SQS; event‑driven pipelines.

Required Skills & Experiences

The successful candidate will be a high-ownership, product-minded engineer who combines strong full-stack fundamentals with experience building AI-native systems, capable of turning ambiguous problems into scalable, reliable features that drive real business impact.

Required:

Experience with LLM/agent systems, evals, and prompt tooling; RAG, embeddings, vector dbs.
Deep knowledge of event‑driven systems, streaming, and workflow engines.
Expertise in observability (OpenTelemetry/Prometheus/Datadog), feature flagging, or A/B platforms.
Background in enterprise security and compliance (SOC 2, GDPR)
Bias to action: short specs, small PRs, daily deploys.
In-person collaboration: we’re building the NYC hub together; whiteboards > long threads.
High ownership: you’ll touch product, talk to customers, and influence the roadmap.

Benefits

Upon joining the Knit team, you will receive a competitive salary + commission plan if applicable to role, Equity Options, Healthcare (medical, dental, and vision), and Additional Coverage, a company laptop and one-time, onboarding Technology Stipend, a 401(k) with company match, flexible time-off, hybrid working, and more!

Salary

In accordance with New York pay transparency requirements, the salary range for this role is $250,000 - $300,000. Final compensation will be determined based on the candidate’s level, experience, and qualifications upon joining Knit.

Our Company Values

We are the Championship Team. This means we:

Are 1% better every day: We approach situations with a growth mindset and ask, “How can we make the business better?” and “What would it take?”
Play to win: We set audacious goals and push ourselves to achieve them with a bias towards action (When we see a need, we take initiative, and hold ourselves accountable to seeing it through).
Keep the main thing the main thing: Identify what has the biggest impact and prioritize to focus on it.

Knit Privacy Policy here.

CCPA disclosure here.