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Staff Software Engineer, Machine Learning (Consumer Revenue)

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Remote from
USA
Salary
USD 272k–340k / yr
Employment
Full Time
Experience
Senior
Published
Apply before
27 Nov 2026
Listing views
28
Application actions
2
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AI Summary

The role, at a glance.

Discord is seeking a Staff Machine Learning Engineer to lead recommendation, ranking, targeting, and personalization systems for consumer revenue products such as Shop, Nitro, subscriptions, and gifting. The role requires deep applied ML expertise, including recommender architectures, deep learning, distributed training, and production-scale data pipelines. This engineer will establish shared ML platforms and libraries while translating experimentation outcomes into product and business roadmap decisions. Success depends on leading ambiguous, organization-wide technical initiatives and aligning multiple product and engineering stakeholders.

Role DNA

A quick view of the complexity, pace, ownership and collaboration implied by the job description.

Job Complexity

5/5
EasyHard

Pace & Pressure

5/5
RelaxedFast-paced

Autonomy Level

5/5
GuidedFull ownership

Communication Load

5/5
IndependentCollaborative
AI insightThis is a senior staff-level role requiring more than eight years of applied ML experience, advanced recommender-system expertise, and delivery of scalable systems from early design through production. The engineer is expected to drive cross-functional, organization-wide initiatives in an ambiguous revenue domain.

Salary analysis

Estimated compensation compared with the broader US market for similar roles.

Estimated job median (per year)Highly competitive
$306,000
US market range (per year)$240k–$360k
AI insightThe yearly pay period is inferred from the job context; it was not explicitly stated by the employer. The employer discloses a US base-salary range of $272,000–$340,000 USD. Although the posting does not explicitly state the cadence, yearly pay is inferred from the range, US full-time status, seniority, and standard US professional compensation conventions. Estimated US market base pay for a staff-level machine learning engineer is $240,000–$360,000 annually; equity and benefits are excluded.

Core skills

Skills and capabilities most closely associated with this opportunity.

Sample interview questions
How would you design a recommendation system for a consumer subscription product with sparse conversion labels?

I would start with clear objectives such as conversion, retention, and user value, then combine implicit engagement signals with purchase events. A two-stage system with candidate generation and a ranking model would support scale, while multi-task learning could jointly optimize short-term conversion and longer-term retention. I would validate offline metrics against controlled online experiments and monitor segment-level effects.

Describe how you would take an ML model from prototype to reliable production service.

I would define data contracts, feature ownership, offline and online evaluation criteria, and monitoring before deployment. The production path would include reproducible training, model versioning, automated validation, staged rollout, latency and quality dashboards, and rollback procedures. I would also establish clear ownership for upstream data and downstream serving dependencies.

What trade-offs do you consider when selecting between a two-tower retrieval model and a transformer-based ranking model?

Two-tower models are effective for large-scale candidate retrieval because embeddings can be precomputed and searched efficiently. Transformer-based models can capture richer contextual interactions and are often more suitable for ranking a smaller candidate set, but they raise serving-cost and latency considerations. I would commonly use both in a multi-stage architecture and optimize the split based on traffic, relevance gains, and infrastructure cost.

How would you evaluate whether a targeting model creates meaningful business value?

I would define a causal experiment with primary metrics such as incremental conversion or revenue, plus guardrails for user experience, unsubscribe behavior, and long-term retention. I would analyze heterogeneous treatment effects across meaningful user segments and ensure the observed improvement is statistically credible. The final decision would balance lift, operational cost, and potential user trust implications.

How do you lead an ML initiative when requirements and ownership are initially unclear?

I begin by aligning stakeholders on the user problem, measurable outcomes, constraints, and decision makers. I then create a phased technical plan that reduces uncertainty early through data investigation, baselines, and small experiments. Regular written updates, explicit interfaces, and documented trade-offs keep partner teams aligned while allowing the plan to adapt to new evidence.

This analysis is generated from the job description. Salary estimates, role characteristics and sample answers are guidance, not employer-provided facts.
Opportunity details

About 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 to hire a Staff Machine Learning Engineer on our Consumer Revenue ML team. This team applies Machine Learning across Discord’s core revenue surfaces — Shop, Nitro, Server Subscriptions, and Gifting — building the ranking, targeting, and recommendation systems that connect users to the right products, subscriptions, and content. If this excites you and you’ve led org-wide initiatives in the past, keep reading!

What You’ll Be Doing

  • 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.
  • Strong expertise in applied deep learning and mainstream RecSys model architecture (e.g. two-tower, transformer-based models, multi-task learning).
  • Strong proficiency in Python and ML frameworks such as PyTorch, JAX, or TensorFlow.
  • A track record of building ML systems from 0→1 in ambiguous, early-stage environments, and taking them to production at scale.
  • Strong product and business intuition, with the ability to translate experiment results into roadmap decisions.
  • Excellent communication and collaboration skills — able to lead cross-functional technical initiatives across multiple verticals and keep stakeholders educated and aligned.
  • The ability to thrive in ambiguous environments, energized by open-ended, technically challenging problems.

What you should have

  • Built internal ML platform/tooling (shared data standards, targeting endpoints, recommender libraries) adopted by multiple product teams.
  • Familiarity with personalized marketing systems — lifecycle targeting, audience segmentation and lookalikes, campaign optimization.
  • Deep expertise in distributed training (e.g. PyTorch on GPU, Ray, Anyscale) and large-scale data processing pipelines (e.g. Chronon, 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 $340,000 + 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, 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.

Apply now >

This job listing has been manually reviewed by the Jobicy Trust & Safety Team for compliance with our posting guidelines, including verification of the company's legitimacy, accuracy of job details, clarity of remote work policy, and absence of misleading or fraudulent content.

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