AI Infrastructure Software Engineer

Quick Facts

• Role: Software Engineer, AI Infrastructure

• Focus: ML infrastructure, developer tooling, and production deployment workflows

Description

Build and maintain the infrastructure, services, and deployment workflows that help ML models move from experimentation into reliable production environments. Develop internal tools and automation to improve the developer experience for training, experimentation, evaluation, and iteration, and design systems for evaluation pipelines, orchestration, reproducibility, and performance monitoring. Partner with ML engineers and researchers to remove infrastructure bottlenecks and translate customer and policy requirements into practical technical solutions.

Responsibilities

• Build and maintain ML infrastructure and deployment workflows

• Create internal tools and automation for model training, experimentation, evaluation, and iteration

• Design/optimize systems for evaluation pipelines, experiment orchestration, reproducibility, and monitoring

• Identify and address infrastructure bottlenecks in collaboration with ML teams

• Translate customer requirements and policy-driven workflows into scalable engineering solutions

• Improve reliability, scalability, and observability of ML systems

Requirements

• Experience building and maintaining production-grade systems, services, or infrastructure

• Strong Python skills and comfort working across backend systems, APIs, and infrastructure code

• Familiarity with cloud infrastructure, distributed systems, containerization, or platform engineering

• Knowledge of ML workflows including training, inference, evaluation, and/or deployment

• Ability to collaborate with ML engineers and researchers to implement complex requirements

• Owner mindset to navigate ambiguity and deliver practical solutions

Bonus (Preferred)

• Experience with ML infrastructure, ML platforms, or internal developer tooling

• Familiarity with Kubernetes, Docker, and AWS or GCP

• Experience with ML infrastructure tools such as Ray, Kubeflow, MLflow, or Weights & Biases

• Experience with model serving, distributed training/inference, or large-scale evaluation pipelines

• Exposure to AI safety/security, adversarial testing, or model behavior evaluation

• Experience translating security constraints or policy considerations into technical implementations

Benefits

• Competitive compensation with performance-based bonus and meaningful equity

• 401k with up to 4% matching

• 28 days annual leave (vacation + holidays)

• Health, dental, and vision coverage

• Catered lunches

• Flexible work arrangements

• Visa sponsorship available for exceptional candidates

Compensation

$130,000–$185,000