About Lanesurf

Voice AI for booking freight

Tech description:

Job description:

# 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**](https://www.linkedin.com/feed/update/urn:li:activity:7394069447327555584) ### 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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