Abacus.AI

Education Requirements:

• PhD in Artificial Intelligence or Machine Learning

Other Requirements:

• Research / published papers in Auto ML, Neural Architecture Search, Personalization, or Forecasting

• Experience publishing at top conferences

• Able to write code and ideate with the product team

Responsibilities:

• Coming up with new techniques in unsupervised learning, dataset augmentation and deep reinforcement learning that can be applied to automating various parts of the AI development workflow

Education Requirements:

• BS or MS from top notch CS programs

Experience Requirements:

• 4+ years of industry experience

Other Requirements:

• Excellent backend / systems software engineers

• Experience building at least one of: Large scale backend systems used by consumer or high volume enterprise services, Cloud data processing platforms in production, Large scale machine learning pipeline infrastructure

• Experience building production applications which use ML/AI is a plus

Responsibilities:

• Building out a cloud runtime for an AI engine that automates various aspects of an ML/AI system workflow including feature pipelines, model training and a real-time multi-tenant inference system

Education Requirements:

• BS in computer science from an accredited university

Experience Requirements:

• 6 months of professional work experience in ML/AI models in production, neural network algorithms, or performance optimization of deep learning systems

Other Requirements:

• Working knowledge of software engineering

• Experience with Python preferred

• Coursework or work experience with machine learning algorithms such as classifiers, anomaly detection, and clustering

Responsibilities:

• Implementing various algorithms to do automated feature extraction and dataset augmentation, optimizing runtimes of neural network algorithms and building higher-level abstractions for various common AI/ML techniques

• Working cross functionally amongst other engineers, on common ML operation tasks such as ML data management and training and model deployment, as well as build systems that are scalable