Applied AI Engineer

**Squad** Applied AI Engineer; $170K - $250K + equity; In-person in NY

**About Company** Squad is using AI to accelerate how the world's best talent finds the right problems to solve.

The bottleneck in recruiting isn't sourcing. It's data. Candidate data is poor and getting noisier (self-reported, outdated, now AI-inflated). But recruiters hold high-quality, real-time signal because they talk to candidates every day. That's why **companies spend >$800B/year on recruiting**. The problem is this signal is fragmented and unusable at scale.

**Squad's first product aggregates signals from hundreds of recruiters and uses AI to instantly match their candidates to roles**, giving companies unprecedented reach without the usual pain of managing external partners.

We give recruiters a business-in-a-box that lets them earn more while doing what they do best: building relationships and knowing their candidates deeply. In return, we get proprietary data. Our AI structures signal from real recruiter-candidate context that doesn't exist on LinkedIn or resumes. Every submission, every rejection, every hire feeds a **self-improving AI matchmaking engine**.

We believe the future of recruiting is human authenticity paired with AI efficiency. Great recruiters have a critical role to play, and Squad is built to empower them.

Long-term, Squad becomes the infrastructure layer for how talent meets opportunity.

**Why you should join us**

Squad is a generational opportunity to rethink how recruiting works, and our path to doing so is significantly de-risked. We have: - **Exponential growth: **We have product-market fit and are growing rapidly. Squad supports hundreds of candidates, recruiters, and employers every day. We've grown 10x in under a year with a 3-person team. - **AI-first business model.** Our approach is distinctly enabled by AI, and our business gets stronger as foundation models improve. We're building durability through cross-sided network effects that compound over time. Every submission, rejection, and hire feeds a self-improving matchmaking engine. - **Real ownership.** It’s still ‘day 0’. You'll shape what Squad looks like and how far we get. Your input will define our vision, product, team, and execution. What you build now becomes the foundation for everything that follows. - **Top tier investors: **We've raised over $5M from FJ Labs (Airbnb, Uber, Dropbox), Link Ventures (Mercor, Liquid AI), Golden Ventures (Faire, Boardy), and angels. - **Massive ceiling:** The recruiting industry is >$800B and broken. We're building the infrastructure layer for how talent meets opportunity. The scope is as big as you want it to be.

Market demand is pulling us forward. We're looking for people who are excited about high ownership, high velocity, and high impact.

There's an enormous amount to build - and the scope only gets bigger. You will never be bored.

**If we succeed**, every company finds the right people the moment they need them. Every person finds meaningful work without friction. And **the pace of innovation changes forever.**

**About the Role** As the first engineer at Squad, you'll own one of the hardest challenges in tech: predicting which humans will thrive where.

We've already solved the cold start problem. We have hundreds of users generating thousands of data points every day. The foundation is there. Now comes the fun part: endless room to build, and you'll shape what that looks like.

You'll take full ownership of anything AI at Squad - technical vision, execution, and the decisions that shape what this company becomes. This isn't a role where you inherit a roadmap. You'll collaborate directly with the founder to figure out what to build, how to build it, and who else we need on the team.

Predicting human fit is one of the hardest recommendation problems out there - sparse feedback, subjective preferences, high stakes. Most hiring AI trains on the same public data everyone else has. We have proprietary signal from both sides of the market that no one else sees. No one has built on a foundation like this before.

**Responsibilities** - Building recommendation systems that predict candidate-job fit - and get smarter with every hire, rejection, and feedback loop - Designing AI agents that automate end-to-end recruiter and ops workflows - Fine-tuning models on proprietary hiring data no one else has access to - Evaluation and simulation environments to measure and improve match quality - And much more which you'll define, across matching, automation, and infrastructure.

**Required Skills** A. 4+ years of engineering experience, with meaningful time spent shipping AI/LLM features in production B. Experience driving AI projects end-to-end - from model selection and data pipelines to deployment and real-world iteration C. Proficiency with modern AI/ML technologies (LLM APIs, embeddings, vector databases, fine-tuning) and a strong foundation in full-stack or backend web development

**Logistical Info** - **Location:** New York, Flatiron office (in-person) - **Compensation:** $170K - $250K + equity - **Number of openings:** Up to 2 - **Other:** Health insurance; Unlimited PTO; Lunch daily and dinner after 7pm (usually fresh healthy food from Eataly); Visa support possible

**Ideal Background** - Applied AI / full-stack engineer at an AI-native startup (3-5 years) who built core matching, recommendation, search, or automation features - Product engineer who owned AI features at a product company (4-6 years) and can point to shipped, user-facing systems - Technical founder or founding engineer who built an AI-powered product 0→1 - Full-stack engineer who went deep on AI/LLMs (4-6 years) and has shipped LLM features in production

**Examples Companies:** - _Recruiting / HR Tech:_ Engineers here already understand recruiter workflows, ATS/CRM patterns, and the pain points Squad is solving. E.g. Ashby (Series C), Gem (Series C), Dover (Series B), Pave (Series C), Findem (Series B), Juicebox (Series A) Handshake (Series F) - _CRM / Dashboard-Heavy:_ Squad's core product is a recruiter dashboard/CRM. Engineers from these companies know how to build power-user interfaces with complex workflows. Attio (Series B), Clay (Series B), Affinity (Series C), Scratchpad (Series B), Lavender (Series A), Koala (Series A), Unify (Series A) - _Unsexy B2B (Legal, Accounting, Fintech Infra):_ Elite engineers building for power users in less glamorous verticals, more likely to be excited about building in recruiting. Pilot (Series C), Puzzle (Series B), Pulley (Series B), Harvey (Series C), EvenUp (Series C), Merge (Series B), Finch (Series B), Vanta (Series C), 11x (Series A), Sierra (Series B), Linear (Series B)

**Example Candidates** - [Oliver Brady](https://www.linkedin.com/in/oliver-brady-0bb008178/) — Top school, Jane Street, Harvey (high growth AI startup) + very relevant work at Mercor **Green Flags** - Experience in a high-intensity, high-velocity environment (seed or Series A/B startup, high-growth tech company, 0→1 product team) - Top-tier school: Stanford, MIT, CMU, Berkeley, or equivalent - HR or recruiting tech experience (but only at company with high-caliber)

**Red Flags** - Data scientists focused purely on analysis and dashboards, not production systems - Pure ML researcher/specialists - Only big company experience at slow moving companies - ML engineers who have only worked on platforms/infra without owning end-user product features

- Mid-level profiles (2-3 yrs) — client has passed on these despite strong AI backgrounds; this seat needs the 4-6 yr shipped-AI-features range (Sep 2026 screening rejection). **Interview Process** 1. Intro Call (30 min): High-level screening and getting them excited about Squad 2. Technical Interview (60 min): System design of a real AI problem 3. Behavioral Deep-Dive (60 min). In person. 4. On-Site (full day): Work through real problems together in person, and get a feel for how we collaborate