Staff ML Engineer - Build Large-Scale AI Systems

Integral Ad Science, Inc.; San Francisco (CA); On-site; USD 135,000 - 232,000; Full time

Job summary

Integral Ad Science is seeking a Staff Machine Learning Engineer to lead architectural design of core AI/ML services and scale training infrastructure. You will mentor data scientists and engineers while translating complex business problems into robust production systems.

Based on a global platform, you’ll collaborate with product and engineering teams, drive ML engineering standards, and own end-to-end deployment of models from research to production, ensuring low latency and high availability.

Qualifications

• PhD/MS in CS, math, statistics, or equivalent experience.
• 7+ years ML industry experience at Lead/Principal/Staff level.
• Deep expertise in designing complex ML systems, data pipelines, and scalable infra.
• Production-grade coding in Python/Go/Java/C++ with solid software principles.
• Strong experience with ML frameworks such as PyTorch, JAX, or TensorFlow.
• Proven ability to mentor engineers and uplift teams.

Responsibilities

• Define architectural vision for core AI/ML-based services.
• Lead multi-quarter initiatives from inception to deployment.
• Architect and scale deep learning infrastructure for training/production.
• Collaborate with Product, Core Engineering, and exec stakeholders.
• Mentor senior and mid-level data scientists and engineers.
• Set code quality standards and lead architecture design reviews.
• Own CI/CD pipelines for AI/ML services and production rollout.

Skills

Technical leadership

Systems design

ML deployment

Production coding

Mentorship

Cross-functional

ML frameworks

Big data tools

Education

PhD or MS

Tools

Databricks

Spark

Kubernetes

Kafka

Prometheus

CUDA

NCCL

Job description

Integral Ad Science is seeking a Staff Machine Learning Engineer to lead architectural design of core AI/ML services and scale training infrastructure. You will mentor data scientists and engineers while translating complex business problems into robust production systems.

Based on a global platform, you’ll collaborate with product and engineering teams, drive ML engineering standards, and own end-to-end deployment of models from research to production, ensuring low latency and high availability.