Senior Software Engineer, Machine Learning (Ads)

Discord•San Francisco, CA

9d•$220,000 - $275,000

About The Position

Discord is seeking a Senior Software Engineer specializing in Machine Learning to join their Revenue ML team. This role will focus on the emerging Ads initiative, contributing to ML capabilities in ads measurement, targeting, and delivery ranking. The engineer will develop foundational ML models to enhance ad relevance, optimize performance, and drive revenue, working on an early-stage Ads ML platform with a tech stack including Python, ML frameworks (PyTorch, TensorFlow), large-scale data infrastructure, and real-time ad-serving technologies.

Requirements

5+ years of experience as a Machine Learning Engineer or Data Scientist.
3+ years of experience specifically in Ads ML (ads ranking, personalization, optimization, privacy-compliant user modeling, targeting, or measurement).
Strong proficiency in Python and familiarity with deep learning frameworks such as PyTorch or TensorFlow.
Experience with applied deep learning (e.g transformers, embedding models).
Proven track record of designing, implementing, and scaling ML-driven ad systems in real-world applications.
Experience working with real-time ML inference, A/B testing, and optimization frameworks.
Experience translating ML evaluation results and performance metrics into actionable product roadmap items.
Ability to connect business objectives to ML solutions, with the flexibility to shift focus toward the highest-impact problems as priorities evolve.

Nice To Haves

Strong understanding of performance advertising and how ML impacts revenue and advertiser retention.
Knowledge of ad tech industry standards and ads ecosystem including targeting, retrieval, ranking, pacing, frequency, auction, etc.
Experience with large-scale recommendation systems.
Experience with large-scale data infrastructure and distributed computing

Responsibilities

Design, develop, and deploy machine learning models for ads targeting and ranking.
Develop sophisticated ML solutions such as identity graph to enhance ad targeting.
Build and optimize ad ranking models to serve the most effective ads based on campaign objectives (e.g., app installs, link click).
Improve ads targeting and ranking by leveraging both on-platform and off-platform signals.
Collaborate cross-functionally with product, engineering, and business teams to define and execute on the Ads ML roadmap.
Scale our ML infrastructure to support an increasing number of concurrent ad campaigns while ensuring low-latency decision-making.
Drive research and implementation of state-of-the-art ML techniques in the field of online advertising.