[All remote jobs](https://jobicy.com/jobs.md)[![Reddit logo](https://jobicy.com/data/server-nyc0409/galaxy/mercury/2020/10/Reddit.jpg)](https://jobicy.com/company/reddit.md)[Reddit](https://jobicy.com/company/reddit.md) # Machine Learning Engineer AI Summary Reddit is hiring a Machine Learning Engineer to design, deploy, and operate production ML systems for consumer and advertising experiences. The role covers the full ML lifecycle, including feature engineering, model training, evaluation, serving, monitoring, and automated retraining. Key problem areas include recommendations, search and ranking, advertising optimization, user and content understanding, and LLM-powered experiences. The engineer will work with large-scale data and distributed systems while partnering with product, data science, infrastructure, and engineering teams. This remote contract position is scoped to Ontario, Canada.[Apply for this job](#job-application)[View company](https://jobicy.com/company/reddit.md)ShareRemote from[Canada](https://jobicy.com/job-region/canada.md)SalaryUndisclosedDepartment[Software Engineering](https://jobicy.com/categories/engineering.md)EmploymentContractExperienceOpen levelPublished5 Nov 2026Apply before5 Dec 2026Listing views20Application actions1Application toolkit ## Make your next move. Prepare your resume, explore your fit, and draft a cover letter for this opportunity. Opportunity details ## About this role. 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 130 million daily active unique visitors, Reddit is one of the internet’s largest sources of information. For more information, visit [www.redditinc.com](http://www.redditinc.com/). At Reddit, machine learning sits at the heart of how millions of people discover, connect, and engage with the world’s largest collection of human conversations. From powering personalized recommendations and search to optimizing advertising systems and marketplace dynamics, our ML engineers tackle some of the most interesting and impactful problems in large-scale applied machine learning. We hire Machine Learning Engineers across both our Consumer and Ads organizations, giving you the opportunity to work on a wide range of high-impact problems across the Reddit ecosystem. We are looking for Machine Learning Engineers who are excited to build systems end-to-end, from research and modeling to production deployment, — and who want to help shape the future of discovery, relevance, and monetization at Reddit. If you love working on complex, real-world ML problems at massive scale, this role is for you. What You’ll Work On As a Machine Learning Engineer at Reddit, you will design and build production ML systems that power core experiences across the platform, including: * Personalized recommendations, search, and ranking systems that help users discover the most relevant content and communities * Intelligent advertising systems including ranking, bidding, measurement, and optimization * Content, Advertisers, and User understanding, from building foundational content/user representations to deriving insightful signals * Large-scale machine learning pipelines, model serving infrastructure, and real-time decision systems * Applied AI and LLM-driven experiences that improve relevance, discovery, and user engagement You’ll work on high-impact systems that operate at internet scale and directly influence user experience, advertiser value, and business outcomes. What You’ll Do * Design, build, and deploy production-grade machine learning models and systems at scale * Own the full ML lifecycle: from problem definition and feature engineering to training, evaluation, deployment, and monitoring * Build scalable data and model pipelines with strong reliability, observability, and automated retraining * Work with large-scale datasets to improve ranking, recommendations, search relevance, prediction, content/user understanding, and optimization systems. * Partner cross-functionally with Product, Data Science, Infrastructure, and Engineering teams to translate complex problems into ML solutions * Improve system performance across latency, throughput, and model quality metrics * Research and apply state-of-the-art machine learning and AI techniques, including deep learning, graph & transformers based, and LLM evaluation/alignment * Contribute to technical strategy, architecture, and long-term ML roadmap Basic Qualifications * 3-5+ years of experience building, deploying, and operating machine learning systems in production * Strong programming skills in Python, Java, Go, or similar languages, with solid software engineering fundamentals * ML Fundamentals: a strong grasp of algorithms, from classic statistical learning (XGBoost, Random Forests, regressions) to DL architectures (Transformers, CNNs, GNNs) * Hands-on experience with modern ML frameworks (e.g., PyTorch, TensorFlow) * Experience designing scalable ML pipelines, data processing systems, and model serving infrastructure * Ability to work cross-functionally and translate ambiguous product or business problems into technical solutions * Experience improving measurable metrics through applied machine learning Preferred Qualifications * Experience with recommender systems, search/ranking systems, advertising/auction systems, large-scale representation learning, or multimodal embedding systems * Familiarity with distributed systems and large-scale data processing (Spark, Kafka, Ray, Airflow, BigQuery, Redis, etc.) * Experience working with real-time systems and low-latency production environments * Background in feature engineering, model optimization, and production monitoring * Experience with LLM/Gen AI techniques, including but not limited to LLM evaluation, alignment, fine-tuning, knowledge distillation, RAG/agentic systems and productionizing LLM-powered products at scale * Advanced degree in Computer Science, Machine Learning, or related quantitative field Potential Teams * Ads Measurement Modeling * Ads Targeting and Retrieval * Advertiser Optimization * Ads Marketplace Quality * Ads Creative Effectiveness * Ads Foundational Representations * Ads Content Understanding * Ads Ranking * Feed Relevance * Search and Answers Relevance * ML Understanding * Notifications Relevance 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 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. During the interview, we will collect the following categories of personal information: Identifiers, Professional and Employment-Related Information, Sensory Information (audio/video recording), and any other categories of personal information you choose to share with us. We will use this information to evaluate your application for employment or an independent contractor role, as applicable. We will not sell your personal information or disclose it to any third party for their marketing purposes. We will delete any recording of your interview promptly after making a hiring decision. For more information about how we will handle your personal information, including our retention of it, please refer to our [Candidate Privacy Policy for Potential Employees and Contractors](https://redditinc.com/policies/candidate-privacy-policy). Reddit is proud to be an equal opportunity employer, and is committed to building a workforce representative of the diverse communities we serve. Reddit is committed to providing reasonable accommodations for qualified individuals with disabilities and disabled veterans in our job application procedures. If, due to a disability, you need an accommodation during the interview process, please let your recruiter know. Show more [Apply now >](https://jobicy.com/jobs/159046-machine-learning-engineer-8.md) > Annual salary information is not provided for this position. Explore salary ranges for similar roles in our [Salary Directory ›](https://jobicy.com/salaries.md) * ![Upload CV](data:image/svg+xml;base64,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) ### Upload your resume now To unlock remote work opportunities and be discovered by global employers. 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. ## Role DNA A quick view of the complexity, pace, ownership and collaboration implied by the job description. ### Job Complexity 5/5EasyHard ### Pace & Pressure 5/5RelaxedFast-paced ### Autonomy Level 4/5GuidedFull ownership ### Communication Load 4/5IndependentCollaborative AI insightThe role requires 3–5+ years of production ML experience and ownership of scalable, low-latency systems handling internet-scale data. It combines advanced modeling knowledge with strong software, infrastructure, and cross-functional execution skills. ## Salary analysis Estimated compensation compared with the broader US market for similar roles. Estimated job median (per year)Market rate$165,000US market range (per year)$130k–$210k0$231.00k AI insightThe yearly pay period is inferred from the job context; it was not explicitly stated by the employer. No employer salary was disclosed. Estimated annual market compensation is shown in USD for an experienced machine learning engineer building production systems at a large technology platform; this is an AI estimate, not verified employer compensation. ## Core skills Skills and capabilities most closely associated with this opportunity. [Machine Learning](https://jobicy.com/jobs?search_keywords=Machine%20Learning.md)[Python](https://jobicy.com/jobs?search_keywords=Python.md)[PyTorch](https://jobicy.com/jobs?search_keywords=PyTorch.md)[TensorFlow](https://jobicy.com/jobs?search_keywords=TensorFlow.md)[Recommendation Systems](https://jobicy.com/jobs?search_keywords=Recommendation%20Systems.md)[Search Ranking](https://jobicy.com/jobs?search_keywords=Search%20Ranking.md)[Feature Engineering](https://jobicy.com/jobs?search_keywords=Feature%20Engineering.md)[Model Serving](https://jobicy.com/jobs?search_keywords=Model%20Serving.md)[MLOps](https://jobicy.com/jobs?search_keywords=MLOps.md)[Distributed Systems](https://jobicy.com/jobs?search_keywords=Distributed%20Systems.md)[Data Pipelines](https://jobicy.com/jobs?search_keywords=Data%20Pipelines.md)[LLM Evaluation](https://jobicy.com/jobs?search_keywords=LLM%20Evaluation.md)[Deep Learning](https://jobicy.com/jobs?search_keywords=Deep%20Learning.md)[Spark](https://jobicy.com/jobs?search_keywords=Spark.md)[Kafka](https://jobicy.com/jobs?search_keywords=Kafka.md) Sample interview questionsDescribe a production ML system you owned from initial problem framing through deployment.I would explain the business objective and success metrics, how I defined labels and features, selected a baseline, evaluated offline performance, and launched the model behind a controlled experiment. I would also describe the serving architecture, monitoring for latency and drift, and the iteration process based on online outcomes. How would you improve a feed-ranking model when offline metrics improve but user engagement does not? I would first validate logging, experiment assignment, and metric definitions. Then I would investigate objective mismatch, selection bias, feature freshness, calibration, and segment-level effects, and test alternative objectives that better represent long-term user value rather than optimizing a narrow offline proxy. What practices do you use to make ML pipelines reliable and reproducible? I use versioned data and features, deterministic training configurations where possible, automated validation checks, model and experiment tracking, CI/CD, and clear rollback procedures. I also monitor pipeline health, data quality, training-serving skew, model performance, and infrastructure latency after release. How would you approach serving a low-latency model for a high-traffic recommendation system? I would establish latency and throughput budgets, separate offline candidate generation from online ranking, and use efficient feature retrieval and caching. Depending on model complexity, I would consider model compression, batching, optimized runtimes, fallback behavior, and continuous measurement of p50, p95, and p99 latency alongside relevance metrics. How do you evaluate whether an LLM-powered product feature is ready for production? I would define task-specific quality, safety, latency, and cost metrics; build a representative evaluation set; and combine automated evaluation with targeted human review. I would launch gradually with guardrails, logging, red-team cases, and monitoring for regressions, harmful outputs, and changes in user behavior. Next step ## Apply now. Follow the employer’s application method and review Jobicy’s safety guidance before sharing personal information. 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