There has never been a better time to indulge in the science and technology of Artificial Intelligence. Join a high performing, elite, well experienced team (worked at Google, Amazon, Uber and Facebook; key developers of BigQuery, GMail, Amazon Personalize; come from CMU, MIT, IIT, Stanford, Cal and Cornell), working on some holy grail problems in Machine Learning Systems (a next generation enterprise AI/ML platform that can automatically build custom models for a variety of business problems).

We are looking for talented backend software engineers, machine learning software engineers and research scientists to be part of the founding team. You will help build the product that applies unsupervised learning to model the world and automatically creates and manages production grade AI systems. As an initial member of the founding team, you will own a significant chunk of the company, shape its culture and work on state-of-the-art science and technology.

Responsible for 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.

Candidates will need to have a PhD preferably in Artificial Intelligence or Machine Learning. We are looking for people who have done research / published papers in one of the following areas:

• Auto ML
• Neural Architecture Search
• Personalization
• Forecasting

Ideal candidates would be experienced at publishing at top conferences and be able to write code and ideate with the product team.

Responsible for 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. As an ML engineer, they will also work 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.

Candidates will need to have a BS in computer science from an accredited university. In addition, we’re looking for engineers who have the following:

• Working knowledge of software engineering - through job experience or coursework. Experience with Python preferred
• Have at least 6 months of professional work experience in one of the following: ML/AI models in production, neural network algorithms, performance optimization of deep learning systems.
• Coursework or work experience with machine learning algorithms such as classifiers, anomaly detection, and clustering

Responsible for 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. As an ML engineer, they will also work 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.

Candidates will need to have a BS in computer science from an accredited university OR have 5 years of professional work experience. In addition, we are looking for software engineers who have the following:

• 5 years of professional software engineering experience. Some experience with Python is required.
• Have at least 1 year of professional work experience in one of the following: data infrastructure, ML/AI models in production, neural network algorithms, performance optimization of deep learning systems.

Responsible for building, tuning and operating the entire infrastructure that powers Abacus.AI's multi-cloud SaaS products. We have a modern technology stack built on Kubernetes, Spark, TensorFlow, Python, Go, Mysql & Redis

Requirements:

• BS or MS from a top notch CS programs
• 2+ years professional experience in hands-on engineering roles including operating production environments in public clouds: AWS, GCP, Azure
• Strong Linux/Unix systems fundamentals
• Python programming experience in production environments
• Experience with modern cloud environments: containerization, infrastructure-as-code, devops, CI/CD pipelines and automation

Preferred:

• Operating Kubernetes clusters
• Experience with ML Ops: Spark, TensorFlow, GPUs
• Experience with Terraform
• Hands-on experience with network security, databases systems

Responsible for the overall information security posture and compliance program for the Abacus.AI SaaS platform and internal systems.

Candidates will need to have a BS or MS from top notch program with industry experience. We are looking for security engineers who have experience with at least two of the following:

• 5+ years professional experience in hands-on technical roles with emphasis on security and operations
• Successfully implemented one of SOC2, ISO27001, HIPAA, GDPR, PCI, Fedramp compliance programs
• Implementing IDS/IPS, WAF, DAST, SAST systems
• Conducting vulnerability scanning and penetration testing
• Developing information security policies, guidelines and documentation
• Developing security awareness and training programs
• Hands-on experience with network security, firewalls, vpns

Responsible for outbound prospecting and pipeline generation for our AI-driven SaaS solutions. This role focuses on driving top-of-funnel activity, and setting high-quality sales meetings. SDRs who consistently meet targets, will have a clear path to transitioning into an Account Executive role within their first year.

We are seeking candidates who have have at least 2 years of experience in sales at a fast-paced tech company (preferably a startup), in addition to the following:

• High-volume outbound prospecting using Outreach or Salesloft
• Parallel dialers (e.g., Orum, Nooks) to increase call efficiency
• Cold calling, email sequencing, and LinkedIn prospecting to book meetings with enterprise key decision-makers
• Self-driven execution—ability to work independently with minimal supervision
• Problem-solving and adaptability—ability to experiment with new messaging and strategies to improve outreach effectiveness