Staff Software Engineer, Machine Learning

1 Month ago • 8 Years + • Research Development • $272,000 PA - $306,000 PA

Job Summary

Job Description

Discord is seeking a Staff Software Engineer for its Discovery/Engagement ML team. This role involves applying Machine Learning to enhance user engagement and personalization. The engineer will be responsible for raising the technical bar, designing and scaling recommender systems, building advanced ML models, and collaborating on ML-driven product roadmaps. The position requires 8+ years of experience in applied Machine Learning, deep expertise in RecSys model architecture, proficiency in Python and ML frameworks like PyTorch or TensorFlow, and experience building scalable ML systems. Strong product intuition and collaboration skills are essential.
Must have:
  • 8+ years in applied Machine Learning
  • Expertise in RecSys model architecture
  • Proficiency in Python and ML frameworks
  • Experience building scalable ML systems
  • Strong product intuition
  • Excellent communication and collaboration skills
Good to have:
  • Experience with whole-page optimization
  • Worked on notification systems
  • Applied reinforcement learning in production
  • Built models for user representation/modeling
  • Expertise in distributed training
Perks:
  • equity
  • benefits

Job Details

Discord is used by over 200 million people every month for many different reasons, but there’s one thing that nearly everyone does on our platform: play video games. Over 90% of our users play games, spending a combined 1.5 billion hours playing thousands of unique titles on Discord each month. Discord plays a uniquely important role in the future of gaming. We are focused on making it easier and more fun for people to talk and hang out before, during, and after playing games.

We’re currently looking to hire a Staff Software Engineer on our Discovery/Engagement ML team. This team applies Machine Learning to unlock impactful and new possibilities that drive user engagement, growth, understanding and personalization. We design and implement end-to-end ML solutions that help users discover and navigate the rich, diverse content across our platform. If this excites you, keep reading!

What You'll Be Doing

  • Raise the technical bar for the team by setting architectural direction, tackling systemic challenges, and identifying opportunities for innovation and efficiency.
  • Design, build and scale robust, high-throughput, low latency recommender systems that power product features used by tens of millions of users every day.
  • Build and deploy advanced ML models, leveraging deep learning, reinforcement learning, and optimization to drive engagement and enhance user experience.
  • Collaborate cross-functionally to shape ML-driven product roadmaps, balancing speed of iteration with long-term system complexity and scalability.

What you should have

  • 8+ years of experience in applied Machine Learning, inclusive Ph.D. or Master’s degree in Computer Science, Machine Learning, Artificial Intelligence, or a related field.
  • Deep expertise in mainstream RecSys model architecture (e.g. two-tower, transformer-based model, multi-task learning, wide and deep etc.).
  • Strong proficiency in Python and ML frameworks such as PyTorch, JAX, or TensorFlow.
  • Extensive experience building performant machine learning systems at scale and have driven execution from ideation to production implementation.
  • Strong product intuition and a passion for building ML applications grounded in user feedback and real-world impact.
  • Excellent communication and collaboration skills—able to lead complex, cross-functional technical initiatives and keep stakeholders aligned through clear updates and problem-solving.
  • The ability to thrive in ambiguous environments, and are energized by tackling open-ended, technically challenging problems.

Bonus Points If You Have:

  • Experience with whole-page optimization in marketplace or multi-objective ranking environments.
  • Worked on notification systems or applied reinforcement learning in production settings.
  • Built models for user representation learning or user modeling to power personalization.
  • Deep expertise in distributed training (e.g. PyTorch DDP, Ray Train) and large-scale data processing pipelines (e.g., Spark, Flink).

This position is US-based and can be remote but if you live in the Bay Area, you’re welcome to work from our beautiful SF office.

The US base salary range for this full-time position is $272,000 to $306,000 + equity + benefits. Our salary ranges are determined by role and level. Within the range, individual pay is determined by additional factors, including job-related skills, experience, and relevant education or training. Please note that the compensation details listed in US role postings reflect the base salary only, and do not include equity, or benefits.

 

Why Discord? 

Discord plays a uniquely important role in the future of gaming. We're a multiplatform, multigenerational and multiplayer platform that helps people deepen their friendships around games and shared interests. We believe games give us a way to have fun with our favorite people, whether listening to music together or grinding in competitive matches for diamond rank. Join us in our mission! Your future is just a click away!

Please see our Applicant and Candidate Privacy Policy for details regarding Discord’s collection and usage of personal information relating to the application and recruitment process by clicking HERE.

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About The Company

Discord is a voice, video and text platform that helps friends come together to hang out, play games and have fun. When Jason Citron and Stanislav Vishnevskiy founded Discord in 2015 they had a hunch that multiplayer gaming would be the future of entertainment and that people would need a communications platform designed for them to talk with their gaming friends. Today, gaming has become the largest form of entertainment in the world, bigger than movies and music combined. It’s the fastest-growing as well. Discord makes it feel like you’re playing in the same room with friends.

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