About the Role
Discord is looking for a Senior Software Engineer specializing in Machine Learning to join the Revenue ML team. This team partners with revenue product groups, focusing on both consumer revenue and an emerging Ads initiative. This role will specifically contribute to the Ads ML efforts, helping to build and scale ML capabilities in areas such as ads measurement, targeting, and delivery ranking. As part of this team, you will play a critical role in developing foundational ML models that enhance ad relevance, optimize performance, and drive revenue. This is a unique opportunity to work on an early-stage Ads ML platform and have a direct impact on the business's success. The tech stack includes Python, ML frameworks like PyTorch and TensorFlow, large-scale data infrastructure, and real-time ad-serving technologies.
What You'll Be Doing
- 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.
What You Should Have
- 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.
Bonus Skills
- 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.