Machine Learning Engineer

Reddit is a community of communities. It’s built on shared interests, passion, and trust, and is home to the most open and authentic conversations on the internet. Every day, Reddit users submit, vote, and comment on the topics they care most about. With 100,000+ active communities and approximately 126 million daily active unique visitors, Reddit is one of the internet’s largest sources of information. For more information, visit . Reddit has a flexible workforce! If you happen to live close to one of our physical office locations our doors are open for you to come into the office as often as you'd like. Don't live near one of our offices? No worries: You can apply to work remotely in any country in which we have a physical presence. About the Role Reddit is building a dedicated Ads ML Efficiency function to make model training and inference materially faster, cheaper, safer, and more scalable. This person will be a key senior engineer on that team, owning meaningful efficiency work across training systems, inference and serving paths, launch-readiness tooling, and reusable optimization capabilities for Ads ML. This role sits at the intersection of ML modeling, systems optimization, and engineering leverage. The engineer will partner closely with ranking teams, serving owners, and ML Platform to identify important bottlenecks, land measurable efficiency wins, and help build the mechanisms that make those wins repeatable. What you’ll do Independently own high-value optimization initiatives across training, inference, or launch-readiness for important Ads ML workloads. Diagnose bottlenecks in real production systems using profiling, benchmarking, and observability rather than intuition-first debugging. Build performance tooling, optimization playbooks, observability hooks, guardrails, or efficiency primitives that help more than one team or workload over time. Improve launch-safety and efficiency readiness by contributing to load testing, fallback readiness, latency and cost visibility, and operational confidence for heavy models. Work with model owners and platform teams to land pragmatic fixes while helping the team gradually standardize repeated solutions. Contribute to the team’s technical direction by surfacing patterns, tradeoffs, and opportunities for reuse or automation. Mentor less-experienced engineers through code, debugging, measurement rigor, and strong execution habits. What we’re looking for Deep ML systems experience close to real production models and workloads, not just generic infra exposure. Direct hands-on experience improving training or serving efficiency with measurable outcomes. Strong technical judgment across model-level, runtime-level, and infrastructure-level optimization choices. Ability to own complex projects end to end and collaborate effectively across team boundaries. Good customer and platform instincts: can solve concrete bottlenecks while keeping maintainability, adoption, and future reuse in mind. Strong communication: able to explain tradeoffs clearly to engineers and partner teams. Nice-to-have Experience with GPU training or serving migrations. Experience with PyTorch, distributed training frameworks, or kernel/runtime optimization. Experience building launch certification, efficiency benchmarking, or cost observability systems. Experience in organizations where platform and applied modeling responsibilities are split across multiple teams. Experience with model compression or deployment optimizations such as quantization, pruning, distillation, or checkpoint optimization. Benefits: Comprehensive Healthcare Benefits and Income Replacement Programs 401k with Employer Match Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support Family Planning Support Gender-Affirming Care Mental Health & Coaching Benefits Flexible Vacation & Paid Volunteer Time Off Generous Paid Parental Leave Pay Transparency: This job posting may span more than one career level. In addition to base salary, this job is eligible to receive equity in the form of restricted stock units, and depending on the position offered, it may also be eligible to receive a commission. Additionally, Reddit offers a wide range of benefits to U.S.-based employees, including medical, dental, and vision insurance, 401(k) program with employer match, generous time off for vacation, and parental leave. To learn more, please visit . To provide greater transparency to candidates, we share base salary ranges for all US-based job postings regardless of state. We set standard base pay ranges for all roles based on function, level, and country location, benchmarked against similar stage growth companies. Final offer amounts are determined by multiple factors including, skills, depth of work experience and relevant licenses/credentials, and may vary from the amounts listed below. The base salary range for this position is: $216,700 - $303,400 USD In select roles and locations, the interviews will be recorded, transcribed and summarized by artificial intelligence (AI). You will have the opportunity to opt out of recording, transcription and summarization prior to any scheduled interviews. 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