Staff Software Engineer, Ads ML Inference Infrastructure

Pinterest • San Francisco, WA

2d • Hybrid

About The Position

Staff Software Engineer, Ads ML Inference Infrastructure The Ads ML Inference Infra team owns the online inference and feature serving systems that power real-time model scoring and delivery for all Ads models at Pinterest. The team is looking for a staff engineer with strong hands-on experience in large-scale ML inference systems, as well as capabilities in solving ambiguous technical problems and driving strategic, cross-functional efforts.

Requirements

• BS (or higher) degree in Computer Science or a related field.
• ~8+ years of relevant industry experience designing and operating large-scale, production ML or distributed infra systems .
• Deep knowledge of at least one programming language ( Java, C++, Python ).
• Deep experience with distributed systems or recommendation / ads serving infrastructure (e.g., request routing, online storage, caching, feature serving, APIs).
• Hands-on experience with at least one deep learning framework ( PyTorch or TensorFlow ) and bringing models from offline experimentation to production.

Nice To Haves

• Experience with model / hardware accelerator libraries (e.g., CUDA, quantization, distillation, low-precision inference).
• Experience with inference optimization and serving frameworks such as Triton, vLLM, or Dynamo .
• Proven track record of leading complex projects , setting technical direction, and collaborating across functions and orgs ; experience mentoring and coaching other engineers.

Responsibilities

• Lead and drive efforts to build next-generation model inference and feature serving systems that power up to 100x larger models and directly uplevel Pinterest’s monetization business.
• Design and optimize low-latency, high-throughput inference pipelines to meet strict SLOs while improving performance, efficiency, and cost .
• Partner with Ads ML and product teams to productionize new model architectures (including LLMs and multi-stage ranking models) and scale them reliably to global traffic.
• Evolve the online feature platform (feature computation, caching, and retrieval) to improve coverage, freshness, and consistency for Ads models.
• Evaluate and integrate new technologies (e.g., GPU acceleration, model compression, Triton, vLLM, Dynamo ) to advance our inference stack.
• Build strong partnerships with other infra and ML teams to improve end-to-end reliability, observability, and developer velocity for Ads ML.
• Mentor and coach other engineers, guiding them through technical decisions, system design, and career development.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level