Build AI that talks, negotiates rates, and enables autonomous movement of trucks from pickup to delivery

Demo of AI booking a shipment in 10 minutes by speaking to 96 trucking companies simultaneously

The problem

If Walmart needs to move a truck of avocados from California to Chicago, today they must:

• Speak with 50+ trucking companies
• Check weight and temperature requirements
• Negotiate price and availability
• Do it one call at a time

This process takes hours and thousands of phone calls every day across the industry.

What we’re building

We’re building AI agents that do this work automatically.

• Calls and emails dozens of trucking companies at once
• Checks requirements (weight, temperature, lanes)
• Negotiates prices in parallel
• Books a truck in minutes, not hours

Proof it works

👉 In this demo, our AI spoke to 96 trucking companies simultaneously and booked a shipment in under 10 minutes - https://www.linkedin.com/feed/update/urn:li:activity:7394069447327555584

Why this is exciting

• You’ll work on AI that handles real-world transactions through phone calls
• Real-world, high-stakes work enabling autonomous logistics - think moving a truck from Chicago to Texas, fully coordinated by AI
• Small team, high ownership, fast iteration
• Hard problems that don’t exist in benchmarks

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

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Interview Process

1. 30 mins with Co-Founder (online)
2. Assignment (take-home)
3. 15 mins with Co-Founder (online)
4. Work trial (in-person in SF)
5. Offer