Senior Machine Learning Engineer (Growth)
Discord is hiring a Senior Machine Learning Engineer (Growth). It pays $220k-$275k a year and Level rates it ; you can apply on Discord.
Level's read on this job
- Level rates this machine learning engineer role Builds AI ●●●●: the job is building AI systems.
- The listed pay starts 10% above the typical machine learning engineer posting on Level.
- Tools and skills the posting cites include PyTorch, TensorFlow and JAX.
- Level lists 32 other open roles at Discord.
- The role was posted in the last few days.
AI in this role
Discord has a highly engaged community of millions of daily active users who use the platform for many different reasons, but there’s one thing that nearly everyone does: play video games. Discord plays a uniquely important role in the future of gaming, and we are focused on making it easier and more fun for people to hang out before, during, and after playing games.
We're looking for a Senior Machine Learning Engineer to join our Engagement ML team, working as part of the cross-functional Onboarding squad in Discord's Growth organization. You'll design and build 0→1 end-to-end ML solutions that help brand-new users find their place on Discord: the servers, people, and content that turn a first session into a long-term connection to the product. If this excites you, keep reading!
What You'll Be Doing
- Design, build, and scale the recommendation systems that connect new users to communities and content, from candidate generation and ranking through to online serving.
- Solve cold-start personalization for users with little or no history on Discord, turning the limited signals available into recommendations for servers, communities, and content.
- Build and deploy ML models that lift new-user retention, combining purpose-built approaches with shared foundations such as user and server embeddings.
- Collaborate cross-functionally to shape the Onboarding roadmap, balancing fast learning experiments with the long-term complexity and scalability of the systems behind them
What you should have
- 5+ years of experience in Machine Learning, Data Science, or Applied Science, with a track record of shipping recommendation or personalization systems end to end.
- Hands-on experience building modern recommendation systems: retrieval and ranking pipelines, embedding-based retrieval (e.g. two-tower models), and deep learning models (e.g. transformers, multi-task learning).
- Strong proficiency in Python and ML frameworks such as PyTorch, JAX, or TensorFlow.
- Experience taking ML systems from ideation to production at scale, including feature pipelines, model serving, monitoring, and online experimentation.
- Strong product intuition and a passion for building 0→1 ML applications grounded in user feedback and real-world impact.
- Excellent communication and collaboration skills, able to serve as the ML voice on a cross-functional squad and keep stakeholders aligned on trade-offs, priorities, and progress.
- The ability to thrive in ambiguous environments, energized by tackling open-ended, technically challenging problems
Bonus Points If You Have:
- Experience with cold-start or new-user personalization, onboarding flows, or preference elicitation.
- Built user representation models or applied social-graph signals to recommendation problems.
- Worked on growth or engagement problems for consumer products, such as community and content recommendations, user activation, or retention.
- Experience with large-scale recommendation systems, data infrastructure, distributed computing, and vector search.
The base salary range for this full-time position is listed below. This position will also be eligible for equity and benefits. Benefits include medical, dental, and vision insurance; a 401(k) plan; life and disability insurance; paid time off; and paid parental leave. 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 role postings reflect the base salary only, and do not include equity, or benefits. Applications for this role are accepted on an ongoing basis.
Base Salary$220,000—$275,000 USDWhy 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, and helps developers build and grow their businesses. 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!
Discord is committed to inclusion and providing reasonable accommodations during the interview process. We want you to feel set up for success, so if you are in need of reasonable accommodations, please let your recruiter know.
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.
How we rate this
Senior Machine Learning Engineer (Growth) at Discord rates 96 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Also common in machine learning engineer jobs at this level
Pay
- This job starts at
- $220k
- Typical machine learning engineer job
- $200k
This posting starts 10% above the typical machine learning engineer job. See machine learning engineer jobs
Questions you could be asked
- What's a project where you used PyTorch hands-on?
- Walk me through how you've used TensorFlow in your day-to-day work.
- What are the limits of JAX that you've run into, and how did you work around them?
- How would you decide a model or AI system is ready to ship?
- Tell me about a time a model underperformed in production. How did you find out, and what did you change?
Adapt your resume
- List these exact terms on your resume: PyTorch, TensorFlow, and JAX. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.
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