Senior Machine Learning Engineer

Requirements

• ABOUT RETELL AI Retell AI is using first principles to reimagine the call center with cutting-edge voice AI.
• Thousands of companies now utilize Retell’s AI voice agents to handle sales, support, and logistics calls that once required large teams of human agents.
• Instead of basic automation that needs constant human tuning, we’re creating intelligent AI “workers” that can act as frontline agents, QA analysts, and managers — continuously executing, monitoring, and improving customer interactions.
• We’re growing quickly and looking for ambitious builders who want to tackle hard technical problems, move fast, and have real impact at one of the fastest-growing voice AI startups.
• Let’s build the future together. - We’re a top 50 AI app in a16z list: https://tinyurl.com/5853dt2x - #4 on Brex's Fast-Growing Software Vendors of 2025: https://www.brex.com/journal/brex-benchmark-december-2025 - We're also one of the top ranking startups on: https://leanaileaderboard.com/ - Enterprise tech 30: https://www.wing.vc/et30/overview
• ABOUT THE 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 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. KEY
• RESPONSIBILITIES - 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.
• - Level Up Infrastructure – Design and maintain the ML infrastructure needed for fast experimentation, robust training, and continuous deployment.
• YOU MIGHT THRIVE IF YOU - ML Engineer with Real-World
• INTERVIEW PROCESS - Talent Screen (15min): chat with our recruiter to get a better sense of the role, the team, and what it’s like to work here. - Technical Interview (45 min): MLE coding - Technical Interview (45 min): ML questions deepdive - Onsite/Virtual Interviews (3 hrs): Hosted in our office if located in the Bay Area or virtual, with three rounds: 1.