Senior Research Engineer

Overview

About Analog

Analog is pioneering the era of physical intelligence. Where digital intelligence transformed our online lives, Analog is transforming the lived world, making cities more adaptive, industries more resilient, services more human centered, and experiences more extraordinary.

At the heart of our approach are living world models, continuously updated, enriched by every capture point, every device, every interaction. Within them resides Ana, the Analog Neural Agent. Ana perceives, adapts, and guides, transforming raw data into foresight and orchestration that improves outcomes across sectors, from safeguarding high-value assets to enhancing human performance

As aSr. Research Engineer, you will be responsible for building AI systems that can perform previously improbable tasks or achieve unprecedented levels of performance. We're looking for people with solid engineering skills (for example designing, implementing, and improving a massive-scale distributed machine learning system), writing bug-free machine learning code, and building the science behind the algorithms employed. The most outstanding deep learning results are increasingly attained at a massive scale, and these results require engineers who are comfortable working in large distributed systems.

Responsibilities

• Past experience in creating high-performance implementations of deep learning algorithms
• Have experience working in large distributed systems
• Provide technical perspective to develop ML best practices and influence engineering culture in the team
• Have in depth knowledge of how the machine learning system interacts with systems around it
• Evaluate the tradeoffs of different ML models/systems and deploy best ML practices
• Experience with Data/Feature Engineering, Training, Evaluation and Deployment of ML models

Qualifications

• Have run at least small-scale ML experiments
• Love figuring out how systems work and continuously come up with ideas for how to make them faster while minimizing complexity and…