What we’ll work on

Train & Tune Models

Fine-tune transcribers and speech models for real-time voice agents operating on live phone calls.

• Enable real time transcriber fine-tuning based on caller context
• Improve transcription accuracy for domain-specific language under noisy conditions
• Fine-tune interruption models on domain-specific conversations
• Post-Train speech models for intonations, pacing and naturalness and avoiding robotic cadence

LLM optimization

• Structuring modules, and policies that compose cleanly
• Optimizing LLM outputs for brevity, correctness, and timing
• Reducing drift across long, multi-turn conversations
• Evaluating changes against real call outcomes, not just text metrics

Evaluation & iteration

You’ll help define how we measure quality across:

• Transcription accuracy where it actually matters
• Voice naturalness as judged by listeners
• Conversation efficiency and completion

You can be a great fit, if:

• 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.

Details

• Cash + Equity
• Location: San Francisco, CA, US

A meeting lands on your calendar

If they’re a yes, I book the chat. You show up — that’s the whole job-hunt.