Senior Applied AI/ML Engineer

Squad is a generational opportunity to rethink how recruiting works, and our path to doing so is significantly de‑risked. We recently raised a large seed round from top tier investors.

• 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, spending almost nothing to get here.
• An AI‑first business model: Our approach is distinctly enabled by AI, but our business will get stronger (not commoditized) as foundation models improve. We are building durability through cross‑sided network effects that will compound over time.
• Top‑tier investors: With the traction and vision to support conviction in our model, we raised significant funding from top‑tier investors to build an exceptional team of engineers and operators.
• Our number one priority is scaling to market demand. We are looking for individuals who are high horsepower, high throughput, and hyper‑resourceful to help us increase capacity and grow. We move fast and need to move faster.
• While we have a specific initial focus, our long‑term goal is huge in scope. We have an unbelievable amount to figure out and build in the next year, and a near‑infinite universe of challenges to dive into as we expand our view.

Role Description

As one of the first engineers at Squad, you will own the applied AI systems that power our marketplace end‑to‑end: candidate‑job matching, submission scoring, cross‑matching, and internal workflow automation. You will work on real production use cases of LLMs and other ML techniques that directly move core business metrics—placement quality and speed, recruiter and company throughput, and operational efficiency. This is a builder role for someone who wants to be early at an AI‑native marketplace, not a research or platform position.

• This role is less about talking to recruiters directly and more about understanding how the system behaves in the wild—where it works, where it breaks, and how to make it smarter. You’ll partner closely with the founder and internal ops team to turn messy, real‑world feedback into productized AI systems.
• You’ll have end‑to‑end responsibility for AI projects: definition, design, experimentation, implementation, launch, and iteration. You will own decisions like when to fine‑tune vs. prompt engineer, when to use off‑the‑shelf models vs. custom approaches, and how to evaluate impact in production.

Qualifications

• Strong understanding of Pattern Recognition and Neural Networks
• Educational background or work experience in Computer Science
• Proficiency in Software Development practices and methodologies
• Experience in Natural Language Processing (NLP) and applied AI concepts
• Strong problem-solving and critical-thinking abilities
• Ability to thrive in a collaborative, on-site work environment
• A passion for working in a fast-paced, entrepreneurial setting
• Master’s degree or higher in a related field is a plus

• 60% applied AI/ML work (model selection, prompting, fine‑tuning, evaluation, experimentation)
• 40% full‑stack/product implementation (APIs, backend services, data pipelines, UI integration)
• This is a demanding, high‑autonomy role. You’ll work directly with the founder to shape the AI roadmap, make technical decisions, and ship fast in an environment without a dedicated ML platform team.

Matching & Scoring

• Build and refine our candidate‑job matching and scoring systems to increase placement quality and speed.
• Ship calibration features so recruiters can preview how AI will score a submission before they submit.
• Create AI systems that compare new candidates to previous rejections and surface what’s different or promising.
• Build smarter cross‑match notifications—ranked by fit and business value, not just binary matches.
• Internal Automation
• Automate internal marketplace operations, replacing manual review steps with intelligent systems.
• Build AI‑powered flagging for stuck candidates or submissions that need ops attention.
• Create systems that learn from ops overrides and feedback to improve model accuracy over time.
• External‑Facing AI Features
• Build AI‑generated candidate summaries based on resume, notes, and transcripts.
• Ship role Q&A and retrieval‑based assistants that answer recruiter questions about any role in real time.
• Power AI‑suggested roles for each candidate in a recruiter’s pool and one‑click submit for cross‑matched candidates with pre‑filled data.
• Platform & Experimentation
• Design and maintain evaluation and experimentation protocols to measure model quality and product impact in production.
• Build tooling and infrastructure to accelerate AI development and iteration (offline evaluation, A/B testing hooks, analytics).
• Stay on top of emerging AI methods and make pragmatic decisions about which models and techniques to adopt.
• Collaborate with the founder and team to prioritize the AI roadmap, balancing speed of growth and long‑term durability.
  

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

A. Early stage at high growth company or founding team experience is positive. Recruiting/ talent experience would be great too but not a must

• Early-stage startup experience at an AI-native company
• Top-tier school: Stanford, MIT, CMU, Berkeley, or equivalent
• Experience with LLM APIs, embeddings, vector databases, fine-tuning
• HR or recruiting tech experience

• Intro Call (30 min): High-level screening and getting them excited about Squad
• Technical Interview (60 min): System design of a real AI problem
• Behavioral Deep-Dive (60 min): Background, experiences, values
• On-Site (full day): Work through real problems together in person, and get a feel for how we collaborate

• Location: In‑person in NY; relocation support available for strong candidates
• Lunch at the office every day and dinner at the office after 7 pm.
• Compensation: $170K - $230K + equity
• 1-2 hires across varying levels of seniority

• Week 1: Get deep into the current AI systems—how scoring and matching work today, where they’re accurate, and where they fail. Talk to the ops team to understand where they manually intervene or override the model. Align with the founder on the highest‑leverage problems to tackle first.
• Weeks 2–4: Ship meaningful improvements to the scoring/matching model and related features. From there, you own the AI roadmap and drive it forward, with the founder as a thought partner rather than a project manager.

• 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.
• Has shipped LLM/AI product features in production that moved a business metric; comfortable owning the full loop without a platform team; makes pragmatic tradeoffs (fine‑tune vs prompt, off‑the‑shelf vs custom) and iterates based on messy real‑world feedback

• ML engineers who have only worked on platforms/infra without owning end‑user product features.
• Data scientists focused purely on analysis and dashboards, not production systems.
• Pure ML researcher primarily interested in publishing rather than shipping product.
• Experience only with clean datasets and well‑defined offline problems, with little exposure to production constraints.
• Needs perfect data and mature infrastructure before building anything
• Built RAG applications as the highlight (table stakes now)
• Only big company experience