Found Description
Most AI products are wrappers. We're building the real thing, an agent that takes on genuine tasks for everyday users: running errands, managing workflows, holding context across long and complex conversations. Reliable by design, not by luck.
We're small, we move fast, and the ML layer is the product. We need someone to own it.
The role
You'll bridge research and production, taking ideas and turning them into systems that run at scale, stay reliable, and get better over time. Full-stack ML ownership: from raw data to deployed model.
Day to day that looks like:
Building end-to-end pipelines across data, training, evaluation, and inference
Adapting and fine-tuning models with modern techniques: LoRA, QLoRA, SFT, DPO, distillation
Architecting inference systems that hold up under real latency and cost constraints
Creating data pipelines that produce high-quality synthetic and real-world training data
Running evaluation that goes beyond benchma...
We're small, we move fast, and the ML layer is the product. We need someone to own it.
The role
You'll bridge research and production, taking ideas and turning them into systems that run at scale, stay reliable, and get better over time. Full-stack ML ownership: from raw data to deployed model.
Day to day that looks like:
Building end-to-end pipelines across data, training, evaluation, and inference
Adapting and fine-tuning models with modern techniques: LoRA, QLoRA, SFT, DPO, distillation
Architecting inference systems that hold up under real latency and cost constraints
Creating data pipelines that produce high-quality synthetic and real-world training data
Running evaluation that goes beyond benchma...
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