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Posted 2 weeks, 2 days ago

Reddit

Staff MLE

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Roles

Staff MLE

Compensation

USD 230000 - 322000

$230K - $322K base + equity

Salary period
yearly
Equity
equity

Tech stack

LLM Python PyTorch Go NLP C++ Java TensorFlow

Location

Remote (US)

Work setup

Employment
full-time
Level
Senior
Remote policy
Remote (US) (ideally east coast)
Remote scope
country-limited
Timezones
East coast timezone preferred for overlap
Visa
unclear

Role details

Responsibilities

  • Join the Ads Content Understanding team to understand what people are talking about from reddit content
  • Own the knowledge graph for the entire company
  • Produce interpretable signals used for RAG type products or as features for models
  • Provide leadership as a TL role for the signals pod
  • Work across a mix of modeling, engineering, and leadership
  • Provide technical leadership and mentorship to MLEs and SWEs doing ML work in ACU, acting as de facto tech lead for content understanding and signals: driving design reviews, setting technical standards, and uplifting the team’s modeling and systems craft.
  • Develop evaluation systems and quality monitoring systems for content understanding signals, using SOTA LM-judge practices.
  • Drive operational excellence for ACU’s ML systems by defining SLOs, alerting, and dashboards for key signals (coverage, latency, precision/recall, cost)
  • Build and evolve content understanding capabilities for commercial conversations (e.g., reviews vs. recommendations vs. comparisons vs. Q&A; sentiment and stance; product entities and categories) and operationalize them as robust signals that power contextual and shopping ads, auto-targeting, new formats, and insights products.
  • Lead design and implementation of signals pipelines and produce an ACU signals registry. Partner with platform teams and other content understanding teams to ensure efficient, reliable serving at Reddit scale.
  • Drive LLM and modern ML best practices within ACU: define when to prompt, finetune, or distill; design evaluation and safety harnesses; and lead at least one major distillation effort to replace external APIs with in-house models.
  • Operate across the full ML lifecycle (problem framing, data, modeling, evaluation, deployment, monitoring, and oncall), designing scalable, resilient MLOps pipelines and championing responsible AI (bias, safety, explainability) for ACU’s models and signals in production.

Requirements

  • Leadership experience
  • Domain experience with content understanding
  • Good fit for a mix of modeling, engineering, and leadership
  • 7+ years of relevant MLE experience delivering production ML systems (models + pipelines + serving) at scale, ideally in large-scale content understanding domains, or Ads.
  • Demonstrated Staff-level technical leadership: has driven architecture decisions, standards, and design reviews across multiple teams, and has aligned PMs, DSs, and engineers on shared ML systems or platforms without direct people-management authority.
  • Excellent communication skills, with the ability to explain complex technical trade-offs to PMs, DSs, and other engineering teams, especially in ambiguous, cross-team problem spaces like Seekers/Searchers monetization.
  • Strong track record building and shipping NLP / Language models / content understanding models to production (e.g., classifiers, encoders, sequence or session models), with clear business outcomes (e.g., CTR/ROAS uplift, safety improvements). Experience with commercial or intent modeling is a strong plus.
  • Practical experience using LLMs in production for labeling, evaluation, or distillation (e.g., LM-as-judge, prompt-based classifiers, LLM-generated labels distilled into smaller models), including managing quality, cost, and latency trade-offs.
  • Deep experience with PyTorch, TensorFlow, or similar, and production-quality code in Python (and ideally one statically typed language like Go/Java/C++). Comfortable owning training, evaluation, and deployment code end-to-end.
  • Experience designing ML systems and pipelines: offline training, feature pipelines (batch/streaming), online serving, monitoring, and experimentation for high-traffic surfaces.

Application

Apply here: https://job-boards.greenhouse.io/reddit/jobs/7851761

Portfolio
not required
GitHub
not required
Cover letter
not required
Apply flow
ats

Company context

Product
Ads Content Understanding; knowledge graph; interpretable signals; commercial intent understanding
Industry
Ads
Stage
YC 1

Description

We are looking for a staff MLE to join the Ads Content Understanding team. This team looks at all of the reddit content in order to understand what people are talking about. We own the knowledge graph for the entire company, and we also produce a number of interpretable signals that are then used either for RAG type products or as features for models. This is a horizontal/platform team, all of our customers are internal, and most of them are within the ads space, but we’re doing some interesting things with organic shopping experiences on reddit. There’s a lot of upside to this role, we’re still nascent on our ‘commercial intent’ understanding (think - is this post comparing tokyo vs kyoto, or is this post at a later stage in the trip planning journey, it is comparing hotels in kyoto). The job posting does a solid job of describing the responsibilities and expectations for this role. It’s a mix of modeling, engineering, leadership. Please apply if you think there’s a good fit, the ideal candidate has both leadership experience as well as domain experience with content understanding. Team is roughly split into two pods, knowledge graph (mostly EU eng) and signals (mostly US eng). This would be a TL role for the signals pod, so east coast timezone would have the most overlap with our team and partner teams. We also have an open Senior MLE headcount for this signals pod, and are expecting more headcount in Q3.

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