Multicloud API ML Engineer for Scalable AI

United States Digital Space LLC; San Francisco (CA); On-site; USD 180,000 - 250,000; Full time

Job summary

the company in San Francisco is actively hiring Machine Learning Engineers to extend our API platform into cloud-native environments, with a focus on AWS-hosted Codex, model customization, and post-training workflows.

You will work across training, evaluation, data pipelines, model behavior, and API/infrastructure integration, collaborating with Research, Applied, Safety Systems, and partner teams to improve reliability and scalability.

Qualifications

• 7+ years of professional ML/infra engineering experience.
• Hands-on with deep learning models, transformers, and production AI systems.
• Strong Python/Rust coding and production-quality software.

Responsibilities

• Partner with strategic customers and internal teams to define target model behaviors, diagnose failure modes, and translate real-world needs into training, evaluation, and system requirements.
• Build and scale production ML systems for model customization, post-training, and fine-tuning-as-a-service workflows.
• Investigate whether training and customization workflows are producing the intended outcomes, and identify changes to data, evaluation, training, or infrastructure that improve performance.
• Partner with backend and infrastructure engineers to integrate ML capabilities into AWS-native API environments.
• Feed learnings from partner deployments back into the platform by proposing and implementing improvements to post-training systems, tooling, APIs, and developer workflows.
• Work closely with Research and Applied teams to bring model improvements, training workflows, and evaluation best practices into production.

Skills

ML engineering

Python

Rust

Education

Master's or PhD in Computer Science, ML, or related field

Tools

PyTorch

TensorFlow

AWS

Kubernetes

Job description

the company in San Francisco is actively hiring Machine Learning Engineers to extend our API platform into cloud-native environments, with a focus on AWS-hosted Codex, model customization, and post-training workflows.

You will work across training, evaluation, data pipelines, model behavior, and API/infrastructure integration, collaborating with Research, Applied, Safety Systems, and partner teams to improve reliability and scalability.