Senior Machine Learning Engineer

The Company is using the first principles to reimagine the call center with cutting edge voice AI.

Since launching 18 months ago, thousands of companies now utilize Company’s AI voice agents to handle sales, support, and logistics calls that once required large teams of human agents. Backed by Y Combinator, Alt Capital, and other leading investors, we have scaled to $36M ARR with a team of 20 people, up from $5M at the start of 2025.

Our vision for 2026 is to build a modern CX platform where entire contact centers are powered by AI. 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:
• #4 on Brex's Fast‑Growing Software Vendors of 2025:
• We're also one of the top ranking startups on:

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 The Company, 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.

• Train & Tune Models – Fine‑tune LLMs and audio models to maximize speed, accuracy, and production‑readiness—pushing the frontier of real‑time AI voice experience.
• 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 iteration.
• Level Up Infrastructure – Design and maintain the ML infrastructure needed for fast experimentation, robust training, and continuous deployment.

• Full‑Stack Machine Learning 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‑Optimized – 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.

• Base Salary: $225,000 - $325,000
• Equity:
  Offers equity
• Location:
  
  Redwood City, CA
• Visas:
  The Company is open to sponsoring work authorization for qualified candidates, including H1B/H‑1B, TN, L‑1, E‑3, F‑1 (OPT/CPT), and O‑1 visa

• 100% coverage for medical, dental, and vision insurance
• $70/day Door Dash credit for unlimited breakfast, lunch, dinner, and snacks
• $200/month wellness reimbursement (gym, fitness classes, etc.)
• $75/month phone bill reimbursement

• Best Offer Upfront:
  Choose from three cash‑equity balance options, no negotiation needed
• Top 1% Talent:
  Above‑market pay (top 5 percentile) to attract high performer
• High Ownership:
  Small teams, >$1M revenue/employee, and significant equity potential
• Performance‑Based:
  Offers tied to interview performance, not experience or past salary