[Remote] Machine Learning Engineer at Rad AI

United States

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$150,000 – $178,000Compensation

Junior (1 to 2 years)Experience Level

Full TimeJob Type

UnknownVisa

Healthcare, Artificial Intelligence, Medical ImagingIndustries

Requirements

3+ years of industry experience in ML Engineering in cloud-native environments
In-depth knowledge of Python and Javascript/Typescript (preferable), or other modern languages in the ML domain
Experience with infrastructure and DevOps tools such as Kubernetes, Docker, and Ansible
Experience in distributed systems, storage systems, and databases
Strong knowledge of cloud computing platforms such as AWS (preferable), GCP, and Azure
Experience with monitoring, tracing, and logging tools such as Cloudwatch, NewRelic, Grafana, etc
Familiarity with infrastructure-as-code tools such as Terraform (preferable), Pulumi, Cloud Formation, etc

Responsibilities

Implement and maintain the infrastructure that supports machine learning applications, services, and workflows
Build, maintain, and improve the ML platform that supports continuous integration, continuous delivery, and continuous training for machine learning models
Develop fullstack, cloud-native services and serverless architectures to build scalable and resilient systems
Plan, design, and develop components in the data pipeline to enable various machine learning models in production
Write code that meets internal standards for security, style, maintainability, and best practices for a high-scale HIPAA web environment
Deploy and maintain the full ML platform stack including monitoring and observability, data analytics, backend integration with customer-facing products, and the full model R&D lifecycle
Work with Product Management, Research, and Engineering to iterate on new features and address inefficiencies across the AI/ML infrastructure

Skills

Machine Learning

MLOps

Generative AI

Language Models

Software Development

Infrastructure Development

Rad AI

AI-driven software for radiology workflows

About Rad AI

Rad AI enhances radiology workflows using artificial intelligence to improve efficiency and accuracy in radiological practices. Its main product, Omni Reporting, automates routine tasks, ensures follow-up on incidental findings, and improves reporting accuracy. This software integrates seamlessly into existing workflows, making it easier for radiologists to manage their tasks. Unlike competitors, Rad AI emphasizes data security and patient privacy, being SOC 2 Type II and HIPAA certified. The company's goal is to provide reliable AI-driven solutions that streamline healthcare processes and improve patient outcomes.

San Francisco, CaliforniaHeadquarters

2018Year Founded

$76.8MTotal Funding

SERIES_BCompany Stage

AI & Machine Learning, HealthcareIndustries

51-200Employees

Benefits

Health Insurance

Health Savings Account/Flexible Spending Account

401(k) Retirement Plan

Paid Holidays

Remote Work Options

Unlimited Paid Time Off

Risks

Emerging competition from companies like DeepMind could overshadow Rad AI's offerings.

Rapid AI technology evolution requires Rad AI to continuously innovate.

AI-driven automation may face resistance from the medical community.

Differentiation

Rad AI's Omni Reporting won 'Best New Radiology Software' by AuntMinnie.

Rad AI integrates AI with FHIRcast for enhanced radiology workflow interoperability.

Rad AI is a pioneer in using large language models for radiology report generation.

Upsides

Rad AI achieved a 48% increase in radiograph reporting efficiency at RANT.

Rad AI raised $50M in Series B funding, boosting its expansion capabilities.

Strategic collaboration with AGFA HealthCare enhances Rad AI's market position.

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