[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, Answers Relevance AI Summary Reddit is seeking a senior or staff-level Machine Learning Engineer to improve relevance for its Answers product. The role leads production ML initiatives spanning retrieval, ranking, embeddings, recommender systems, and LLM-powered features. It requires hands-on experience with model development, serving, evaluation, monitoring, and scalable ML infrastructure using Python and frameworks such as PyTorch, TensorFlow, or Hugging Face. The engineer will set technical direction, partner closely with product and data teams, define success metrics, and mentor other engineers. Strong expertise in retrieval-augmented generation, LLM evaluation, latency and cost optimization, and AI safety is particularly relevant.[Apply for this job](#job-application)[View company](https://jobicy.com/company/reddit.md)ShareRemote from[USA](https://jobicy.com/job-region/usa.md)SalaryUSD 216,700–322k / yrDepartment[Software Engineering](https://jobicy.com/categories/engineering.md)EmploymentContractExperienceSeniorPublished5 Nov 2026Apply before5 Dec 2026Listing views36Application 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 “How do I fix a leaky faucet?” to “What should I watch next?”, the Answers Relevance team leverages AI to connect people with the real-world wisdom of Reddit communities. We’re hiring Senior and Staff Machine Learning Engineers to make Answers smarter, more relevant, and more helpful. Our SOTA AI systems helps surface the right conversations, but the heart of Answers is still people helping people ~ Redditors sharing what they know with each other! What You’ll Work On You’ll lead ML projects from idea to production, partnering with Product, Data Science, and Engineering across recommendations, search, messaging, and AI systems. You don’t need experience in every area listed below. A background in a few may make you a great fit! * Recommendations, search, content understanding, or large-scale data and ML infrastructure * SOTA large scale RecSys * Foundational retriever models * RAG systems, retrieval methods, LLM evaluation, or production LLM features * Agentic AI, such as tool calls, MCPs, multi-agent systems, or orchestration * Self-hosted open-weight models such as Qwen or Llama; prompt engineering, fine-tuning, post-training, or prompt optimization * Improving grounding, instruction following, and hallucination; evaluating retrieval and generation quality * Scaling LLM systems, including latency, caching, and cost; safety measures for prompt injection and unsafe inputs * Defining product success metrics, feedback loops, and evaluation approaches, including human review What You’ll Do * Build and improve large-scale ML systems, including retrieval and ranking, embeddings, and LLM-powered features * Set technical direction and deliver measurable user and business impact * Work with partner teams to shape product and technical roadmaps * Evaluate new AI/ML approaches and support responsible LLM development * Mentor engineers and contribute to long-term ML planning Required Qualifications * 4–12 years of experience building, deploying, and operating ML systems in production * Broad knowledge of ML fundamentals and experience applying them in production; expert-level modeling depth isn’t required * Experience developing and productionizing models with TensorFlow, PyTorch, or Hugging Face Transformers * Experience writing production-quality code, including testing, evaluation, and monitoring; Python experience required, Golang experience a plus * Experience with ML systems such as data pipelines, feature engineering, model training and serving, and production monitoring * Strong communication and collaboration skills, and a record of delivering measurable product impact 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 #LI-Remote 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 [https://www.redditinc.com/careers/](https://www.redditinc.com/careers/). 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—$322,000 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. 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/159057-machine-learning-engineer-answers-relevance.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 4/5RelaxedFast-paced ### Autonomy Level 5/5GuidedFull ownership ### Communication Load 5/5IndependentCollaborative AI insightThis is a senior/staff ML engineering position requiring end-to-end ownership of large-scale production systems and technical leadership. The scope combines advanced retrieval and LLM work with measurable product outcomes, cross-functional influence, and mentorship. ## Salary analysis Estimated compensation compared with the broader US market for similar roles. Estimated job median (per year)Market rate$269,350US market range (per year)$210k–$340k0$374.00k AI insightThe yearly pay period is inferred from the job context; it was not explicitly stated by the employer. The employer discloses a base salary range of $216,700–$322,000 USD. The annual period is inferred from the compensation level and US senior/staff full-time-equivalent market conventions because the posting does not explicitly state a cadence. A reasonable estimated US market range for comparable senior/staff production ML engineering roles is $210,000–$340,000 USD per year, excluding equity, commission, and benefits. ## Core skills Skills and capabilities most closely associated with this opportunity. [Machine Learning](https://jobicy.com/jobs?search_keywords=Machine%20Learning.md)[Information Retrieval](https://jobicy.com/jobs?search_keywords=Information%20Retrieval.md)[Learning to Rank](https://jobicy.com/jobs?search_keywords=Learning%20to%20Rank.md)[Recommender Systems](https://jobicy.com/jobs?search_keywords=Recommender%20Systems.md)[Large Language Models](https://jobicy.com/jobs?search_keywords=Large%20Language%20Models.md)[Retrieval-Augmented Generation](https://jobicy.com/jobs?search_keywords=Retrieval-Augmented%20Generation.md)[PyTorch](https://jobicy.com/jobs?search_keywords=PyTorch.md)[TensorFlow](https://jobicy.com/jobs?search_keywords=TensorFlow.md)[Hugging Face Transformers](https://jobicy.com/jobs?search_keywords=Hugging%20Face%20Transformers.md)[Python](https://jobicy.com/jobs?search_keywords=Python.md)[Model Serving](https://jobicy.com/jobs?search_keywords=Model%20Serving.md)[ML Infrastructure](https://jobicy.com/jobs?search_keywords=ML%20Infrastructure.md)[Feature Engineering](https://jobicy.com/jobs?search_keywords=Feature%20Engineering.md)[LLM Evaluation](https://jobicy.com/jobs?search_keywords=LLM%20Evaluation.md)[Production Monitoring](https://jobicy.com/jobs?search_keywords=Production%20Monitoring.md) Sample interview questionsHow would you evaluate a retrieval-and-generation system for Reddit Answers?I would define separate offline metrics for retrieval relevance, answer correctness, grounding, and safety, then combine them with human evaluation for nuanced quality judgments. I would validate improvements through controlled online experiments using engagement, task-success, and negative-feedback signals while monitoring for regressions across query segments. Describe how you would reduce hallucinations in an LLM-powered answers product. I would first improve retrieval quality and source coverage, then require the model to ground claims in retrieved content with citations or attribution where appropriate. I would add evaluation sets for unsupported claims, tune prompts or models for abstention when evidence is weak, and continuously monitor production feedback and safety metrics. What design choices matter when serving LLM features at large scale? Key considerations include model routing, batching, caching, request prioritization, context-window management, latency budgets, and cost observability. I would also design resilient fallbacks, monitor quality versus cost trade-offs, and use asynchronous or staged retrieval where it improves user-perceived latency. How have you taken an ML model from experimentation to production? I would explain the problem and success metrics, establish reproducible training and validation pipelines, and package the model behind a versioned serving interface. Before rollout, I would implement testing, monitoring for data and model drift, canary deployment, and experiment measurement to confirm sustained business and user value. How would you work with product partners when defining success for a relevance initiative? I would translate the product objective into measurable user outcomes and leading ML metrics, then align on trade-offs such as precision, coverage, freshness, latency, and safety. I would communicate uncertainty clearly, propose experiments, and use results to iteratively refine both the roadmap and technical approach. 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