ML Engineer

About Fonzi

Fonzi is an AI recruiting company that connects ML engineers with hiring managers at 250+ pre‑vetted startups in NYC and San Francisco. Companies request to interview you and submit salary bids upfront. The process includes a 5‑minute application, profile optimization, interview requests, competing salary bids, and coordination via an iMessage agent and human support.

How It Works

• 5‑minute application – we reach out, learn what you’re building and what excites you, and optimize your profile.
• Hiring managers review the cohort and request interviews with candidates they want.
• Companies submit competing salary bids directly to you.
• You only interview where you’re genuinely interested.
• Completely free – iMessage agent + humans in the loop available for questions, scheduling, and coordination.

Who We’re Looking For

• Have trained, fine‑tuned, evaluated and deployed models in production, not just in notebooks.
• Strong in Python and PyTorch, comfortable with data pipelines around models.
• Experience with LLMs (fine‑tuning, RL, evals, inference optimization) or classical ML at scale (ranking, recommendations, computer vision).
• Able to move between research ideas and production code.
• Thrive in startup environments where the problem is ambiguous and the team is small.
• 2‑8 years of experience, ideally with at least one high‑growth company.

What You’ll Build

• Fine‑tuned and domain‑specific models for healthcare, legal and fintech.
• Eval pipelines and reinforcement learning loops that make models measurably better.
• Inference and serving optimization for latency and cost.
• Computer vision and perception systems.
• Personalization, ranking and recommendation models serving real users.

Compensation

$180,000 to $300,000+ base salary depending on experience and role. Many positions include meaningful equity at pre‑seed through Series B stage companies.

Interview Process (Typical)

• Screening conversation looking for communication skills, cultural fit and genuine curiosity about AI.
• Technical deep‑dive on modeling choices, evals and a past project.
• Coding or pairing session on ML coding, data work or model debugging.