Job Description
Most AI roles build on top of models. This one builds what makes them actually work.
Were hiring ML Infrastructure Engineers to tackle a hard real-world problem understanding whats happening on live job sites using wearable devices large-scale video and AI.
This isnt clean benchmark data.
Its messy continuous real-world input flowing from device edge cloud at scale.
Youll be working across:
High-throughput video pipelines handling millions of hours of data
Training and inference systems for multimodal / LLM-based models
GPU infrastructure and performance optimisation
Hybrid environments spanning edge on-prem and cloud
The role is end-to-end. Ingestion through to deployment.
Youll be building the systems that make applied AI viable outside the lab.
The team comes from top AI and infrastructure companies with strong funding and a clear technical roadmap. This is a systems challenge as much as an ML one.
San Francisco (on-site). $250k$350k base strong equity.
If youve built ML or data infrastructure at scale and care about real-world constraints this is worth a conversation.
All applicants will receive a response.
Required Experience:
IC
Job DescriptionMost AI roles build on top of models. This one builds what makes them actually work.Were hiring ML Infrastructure Engineers to tackle a hard real-world problem understanding whats happening on live job sites using wearable devices large-scale video and AI.This isnt clean benchmark dat...
Job Description
Most AI roles build on top of models. This one builds what makes them actually work.
Were hiring ML Infrastructure Engineers to tackle a hard real-world problem understanding whats happening on live job sites using wearable devices large-scale video and AI.
This isnt clean benchmark data.
Its messy continuous real-world input flowing from device edge cloud at scale.
Youll be working across:
High-throughput video pipelines handling millions of hours of data
Training and inference systems for multimodal / LLM-based models
GPU infrastructure and performance optimisation
Hybrid environments spanning edge on-prem and cloud
The role is end-to-end. Ingestion through to deployment.
Youll be building the systems that make applied AI viable outside the lab.
The team comes from top AI and infrastructure companies with strong funding and a clear technical roadmap. This is a systems challenge as much as an ML one.
San Francisco (on-site). $250k$350k base strong equity.
If youve built ML or data infrastructure at scale and care about real-world constraints this is worth a conversation.
All applicants will receive a response.
Required Experience:
IC
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