Skip to content

Principal Machine Learning Engineer, Ads Delivery

315k – 551kSan Francisco, CAPalo Alto, CASeattle, WAHybrid10+ YOE
Summary

Develop backend systems, statistical models, and experiments to optimize Pinterest's ads marketplace, balancing short- and long-term objectives. Requires 10+ years experience, CS/ML degree, and strong engineering/math skills.

About the role

What you’ll do:

  • Build and improve backend systems and statistical models that underlay the marketplace to maximize value for Pinners, Partners and Pinterest.
  • Define and implement experiments to understand long term Marketplace effects.
  • Develop strategies to balance long and short term business objectives.
  • Drive multi-functional collaboration with peers and partners across the company to improve knowledge of marketplace design and operations.

What we’re looking for:

  • Degree in Computer Science, Machine Learning, Statistics or related field.
  • 10+ years of professional experience as a hands-on engineer and technical leader leading multiple projects.
  • Strong software engineering and mathematical skills with knowledge of statistical methods.
  • Hands-on experience with large-scale online e-commerce systems is a plus.
  • Background in computational advertising is preferred.
Skills
Machine LearningStatistical ModelsBackend SystemsAuction Mechanism DesignComputational AdvertisingSoftware EngineeringStatistical MethodsE-commerce SystemsExperiment DesignMarketplace Dynamics
Similar roles at this salary range
All ML Engineering jobs →
Anthropic

Staff Software Engineer, Inference

Build and maintain distributed inference systems serving Claude to millions of users. Design intelligent routing, autoscaling, and high-performance infrastructure across diverse AI accelerators.

320k – 485kSan Francisco, CA +2ML EngineeringHybridAWSGCP
OpenAI

Researcher: Agent Post-Training, API & Power-Users

Improve agentic model capabilities for API and power users by designing experiments, building evals from real workflows, and driving post-training interventions from discovery through launch.

295k – 445kSan Francisco, CAML EngineeringHybridRLLLMs
Nuance Labs

Member of Technical Staff — RL Research

Own RL and post-training infrastructure for omni foundation models. Build and scale rollout, reward, and policy systems from 0→1 for real-time audiovisual AI.

300k – 400kSeattle, WAML EngineeringOn-siteRLPPO
Datadog

Staff Applied Scientist - Dashboards

Staff Applied Scientist defining evaluation strategy and quality metrics for Datadog's AI-native Dashboards product. Owns ML/GenAI evaluation systems, builds datasets and harnesses, and drives improvements in retrieval, tool selection, and agent performance.

276k – 345kNew York, NYML EngineeringHybridGenerative AITool Selection
Nuance Labs

Member of Technical Staff — Pretraining Infra

Own and scale the distributed training infrastructure for large-scale omni model pretraining across GPU clusters, covering job orchestration, parallelism, GPU communication, data loading, and performance optimization.

300k – 400kSeattle, WAML EngineeringOn-siteNCCLMegatron