Retell is building the new standard for automating call center communications, including sales, support, customer engagement, and retention calls.

Backed by Altman Capital, Y Combinator, and top investors, we've raised a $4.7M seed round and hit $7M ARR in 12 months.

Check us out on the Top Lean AI Native Companies Leaderboard at leanaileaderboard.com.

Retell AI - API That Turns Your LLM Into A Human-Like Voice Agent

Retell AI Basics: Everything You Need to Start Building Voice Agents

Why This Company

Twenty years ago, every business found itself needing a website, the new gateway to the world. Platforms like WordPress and Squarespace rose to become the standard for getting businesses online.

Today, we’re at the start of a new shift. Every business that relies on phone interactions can now have an AI voice agent of their dreams. A tireless and empathetic receptionist, salesperson, debt collector, or appointment reminder that works 24/7. Operational bottlenecks vanish overnight. Doubling your business no longer means doubling your team. Customer experience no longer suffers. (Ever tried calling an East Coast bank after 3 p.m. PT?) But companies aren't ready. Platforms don't exist.

At Retell, we're building the new standard for building AI voice agents. A seamless UI, a comprehensive toolkit, effortless integrations, and a thriving builder community, we're creating everything businesses need to deliver exceptional phone interactions on every single call.

This shift is happening, so why not be part of the team that defines it?

Why This Role

This is a hands-on, high-ownership role for ML engineers who want to build production models that actually ship—and perform under real-world constraints. As a Founding Senior Machine Learning Engineer at Retell, you’ll work across the ML stack to power human-like voice agents that handle millions of real-time phone conversations.

You’ll fine-tune large language models and audio models, evaluate them with rigorous benchmarks (and human feedback), and deploy them into latency-sensitive, high-traffic systems. You’ll own model performance end-to-end—from training pipelines to post-deployment monitoring—and shape our ML strategy alongside the founding team.

If you’re excited by hard technical challenges, fast iteration, and the opportunity to define how voice AI works at scale, this role is a rare chance to do it from the ground up.

Position: Founding Senior Machine Learning Engineer

Job type: Full-time 70hr/week (50 hr/week onsite with flexible hours + 20 hr/week work from home)

Salary: $200K - $310K

Equity: 0.50% - 1.0%

Bonus: $20k - $100K

Location: San Carlos, CA, US

US visas: Sponsors Visa & Greencard

Benefits: 100% medical, dental, vision insurance covereage. Unlimited breakfast, lunch, dinner, and snacks. Gym, daily communte fee reimbursed. Internet, phone bill covered.

Who You Are

• ML Engineer with Real-World Experience – You’ve trained and shipped models in production. Bonus if you’ve worked with LLMs or audio models.
• Fluent in Modern ML Stack – You know your way around Python, PyTorch, and today’s ML tools—from training pipelines to evaluation benchmarks.
• Execution-Oriented – You move fast, take ownership, and focus on solving real problems over perfect ones.
• Startup-Ready – You’re adaptable, resilient, and energized by ambiguity and fast-changing priorities.
• Clear Communicator & Team Player – You collaborate well across functions and push decisions forward.

What You’ll Do

• Train & Tune Models – Fine-tune LLMs and audio models to maximize speed, accuracy, and production-readiness—pushing the frontier of real-time AI voice experiences.
• Benchmark & Evaluate – Build datasets, define rigorous metrics, and measure model performance across high-impact voice AI tasks to guide development.
• Deploy to Production – Work closely with engineering to ship models, monitor them in the wild, and ensure they stay fast, reliable, and accurate at scale.
• Run Human Evaluations – Build scalable pipelines to collect structured human feedback, benchmark subjective quality, and inform model iterations.
• Level Up Infrastructure – Design and maintain the ML infrastructure needed for fast experimentation, robust training, and continuous deployment.

Our Compensation Philosophy

• Our Strongest Offer, Upfront
  We give you our best offer from the start. No need to negotiate or match other companies offer — instead, we offer three clear compensation options, each with a different balance of cash and equity. You pick what fits you best.
• Designed for Talent Density
  We’re building with the top 1%, not the average. That’s why our compensation is set far above market — to attract people who raise the bar for everyone around them.
• Scale Through Code, Not Headcount
  We aim for >$1M in revenue per employee. That means smaller teams, fewer layers, and higher ownership per person — both in terms of scope and equity.
• Performance > Background
  We don’t anchor to other companies, past salaries, or industry ranges. Your offer is based entirely on your performance in our interview process. That’s it.
• Experience Doesn’t Dictate Pay
  Your offer is based solely on how well you perform in the interview process. We don’t factor in past salaries, titles, or number of years worked. You can earn our highest tier with zero years of experience — if you prove you’re ready for it.

Interview Process

• Online Assessment – We’ll start with a quick HackerRank assessment: one machine learning-focused coding question that takes about 25–30 minutes. You’ll have 7 days to complete it on your own time.
• Phone Interview 1 – A 30-minute live coding session focused on ML coding. We'll use CoderPad and Python with standard ML libraries.
• Phone Interview 2 – A 30–45 minute session diving into data structures and algorithms. You’ll again use CoderPad and can choose your preferred programming language.
• Onsite Interviews – These take 1–4 hours and are conducted in person in the San Francisco Bay Area. Please bring a computer for any hands-on portions. For remote candidates, we’re happy to arrange virtual sessions. There are up to four rounds:
  • ML Fundamentals Interview – A deep dive into machine learning concepts and your previous ML projects.
  • DS&A Coding Session – A focused interview on data structures and algorithms, using CoderPad.
  • ML System Design Interview – You'll walk through how you'd approach and architect real-world ML problems at scale.
  • Behavioral Interview – A conversation about your past experience, how you work, and how you think through challenges.