Squad - Applied AI Engineer

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 Candidate data is poor and getting noisier (self-reported, outdated, now AI-inflated).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. 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. Key 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. High ownership, comfort with ambiguity, bias toward shipping 3+ years of software engineering experience, with a track record of shipping full-stack AI/LLM features in production Proficiency with modern AI/ML technologies (LLM APIs, embeddings, vector databases, fine-tuning) Experience in a high-intensity, high-velocity environment (e.g., seed or Series A/B startup, high-growth tech company, 0→1 product team) De Circle