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Staff Machine Learning Engineer - Search

Warner Bros. Discovery

Staff Machine Learning Engineer - Search

full-timePosted: Aug 2, 2026Updated: Sep 3, 2026CA San Francisco 153 Kearny Street

Job Description

Welcome to Warner Bros. Discovery… the stuff dreams are made of.Who We Are… When we say, “the stuff dreams are made of,” we’re not just referring to the world of wizards, dragons and superheroes, or even to the wonders of Planet Earth. Behind WBD’s vast portfolio of iconic content and beloved brands, are the storytellers bringing our characters to life, the creators bringing them to your living rooms and the dreamers creating what’s next…From brilliant creatives, to technology trailblazers, across the globe, WBD offers career defining opportunities, thoughtfully curated benefits, and the tools to explore and grow into your best selves. Here you are supported, here you are celebrated, here you can thrive.Staff MLE, Search & Personalization, Content Discovery Location: Seattle, San Francisco Bay Area, New York About You: We’re looking for a Staff Machine Learning Engineer to lead the design and evolution of ML and AI driven Search algorithms for our global streaming app, HBO Max. This role will own the end-to-end search algorithm innovation from retrieval and ranking to personalization and experimentation, impacting how millions of users discover content globally. You will operate at the intersection of ML, systems, and product, bringing both deep expertise in search/relevance systems and strong technical leadership. You’ll help define the roadmap, drive architecture decisions, and elevate the team’s ability to build and iterate on high-quality search experiences. Responsibilities: Lead the design and development of large-scale model driven search algorithms including retrieval, ranking, and query understanding Define and evolve the technical strategy for search, balancing relevance, latency, and scalability Drive end-to-end ML systems: data pipelines, feature engineering, model training, experimentation, and serving Partner closely with product, data, and infrastructure teams to shape and execute on the search roadmap and user experience Mentor engineers and data scientists and raise the bar on algorithm development, system design, ML and engineering practices Identify and drive high-impact opportunities across search and personalization Motivate, inspire and create a culture of experimentation and data-driven innovation while constantly striving to be an advocate for doing what is right for our customers Requirements: 8+ years of industry experience, with 4+ years as tech lead experience (preferred) Deep expertise in search and/or ranking algorithms(e.g., retrieval, LTR, semantic search, query understanding, entity recognition) Strong experience building large-scale ML systems in production Proven ability to lead technical direction and influence across teams Experience with online experimentation and metrics-driven development Strong programming skills in Python (Java/Go a plus) Experience with distributed systems and cloud platforms (AWS/GCP/Azure) Solid understanding of data fundamentals (SQL, pipelines, feature stores) Excellent communication skills and ability to work cross-functionally Nice to Have Experience with recommender systems or personalization Experience with NLP techniques Familiarity with vector search / ANN (Faiss, ScaNN, etc.) Experience in streaming, marketplace, or content discovery platforms How We Get Things Done…This last bit is probably the most important! Here at WBD, our guiding principles are the core values by which we operate and are central to how we get things done. You can find them at www.wbd.com/guiding-principles/ along with some insights from the team on what they mean and how they show up in their day to day. We hope they resonate with you and look forward to discussing them during your interview.Championing Inclusion at WBDWarner Bros. Discovery embraces the opportunity to build a workforce that reflects a wide array of perspectives, backgrounds and experiences. Being an equal opportunity employer means that we take seriously our responsibility to consider qualified candidates on the basis of merit, without regard to race, color, religion, national origin, gender, sexual orientation, gender identity or expression, age, mental or physical disability, and genetic information, marital status, citizenship status, military status, protected veteran status or any other category protected by law.If you’re a qualified candidate with a disability and you require adjustments or accommodations during the job application and/or recruitment process, please visit our accessibility page for instructions to submit your request.In compliance with local law, we are disclosing the compensation, or a range thereof, for roles in locations where legally required. Actual salaries will vary based on several factors, including but not limited to external market data, internal equity, location, skill set, experience, and/or performance. Base pay is just one component of Warner Bros. Discovery’s total compensation package for employees. Pay Range: $192,570.00 - $357,630.00 salary per year. Other rewards may include annual bonuses, short- and long-term incentives, and program-specific awards. In addition, Warner Bros. Discovery provides a variety of benefits to employees, including health insurance coverage, an employee wellness program, life and disability insurance, a retirement savings plan, paid holidays and sick time and vacation.If you’re a qualified candidate with an arrest or conviction record, please know that your application will be considered in accordance with the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act.

Locations

  • CA San Francisco 153 Kearny Street
  • Suite S200, WA Bellevue 205 108th Avenue NE
  • NY New York 230 Park Avenue South

Skills Required

  • search and/or ranking algorithmsintermediate
  • large-scale ML systems in productionintermediate
  • online experimentationintermediate
  • distributed systemsintermediate
  • recommender systemsintermediate
  • NLP techniquesintermediate
  • vector search / ANNintermediate
  • streamingintermediate

Required Qualifications

  • 8+ years of industry experience, with 4+ years as tech lead experience (preferred) (experience, 8 years)
  • 8+ years of industry experience, with 4+ years as tech lead experience (preferred) (experience, 8 years)
  • Deep expertise in search and/or ranking algorithms(e.g., retrieval, LTR, semantic search, query understanding, entity recognition) (experience)
  • Deep expertise in search and/or ranking algorithms(e.g., retrieval, LTR, semantic search, query understanding, entity recognition) (experience)
  • Strong experience building large-scale ML systems in production (experience)
  • Strong experience building large-scale ML systems in production (experience)
  • Proven ability to lead technical direction and influence across teams (experience)
  • Proven ability to lead technical direction and influence across teams (experience)
  • Experience with online experimentation and metrics-driven development (experience)
  • Experience with online experimentation and metrics-driven development (experience)
  • Strong programming skills in Python (Java/Go a plus) (experience)

Preferred Qualifications

  • Experience with distributed systems and cloud platforms (AWS/GCP/Azure) (experience)
  • Experience with distributed systems and cloud platforms (AWS/GCP/Azure) (experience)
  • Solid understanding of data fundamentals (SQL, pipelines, feature stores) (experience)
  • Solid understanding of data fundamentals (SQL, pipelines, feature stores) (experience)
  • Excellent communication skills and ability to work cross-functionally (experience)
  • Excellent communication skills and ability to work cross-functionally (experience)
  • Experience with recommender systems or personalization (experience)
  • Experience with recommender systems or personalization (experience)
  • Experience with NLP techniques (experience)
  • Experience with NLP techniques (experience)
  • Familiarity with vector search / ANN (Faiss, ScaNN, etc.) (experience)
  • Familiarity with vector search / ANN (Faiss, ScaNN, etc.) (experience)
  • Experience in streaming, marketplace, or content discovery platforms (experience)
  • Experience in streaming, marketplace, or content discovery platforms (experience)

Responsibilities

  • Lead the design and development of large-scale model driven search algorithms including retrieval, ranking, and query understanding
  • Lead the design and development of large-scale model driven search algorithms including retrieval, ranking, and query understanding
  • Define and evolve the technical strategy for search, balancing relevance, latency, and scalability
  • Define and evolve the technical strategy for search, balancing relevance, latency, and scalability
  • Drive end-to-end ML systems: data pipelines, feature engineering, model training, experimentation, and serving
  • Drive end-to-end ML systems: data pipelines, feature engineering, model training, experimentation, and serving
  • Partner closely with product, data, and infrastructure teams to shape and execute on the search roadmap and user experience
  • Partner closely with product, data, and infrastructure teams to shape and execute on the search roadmap and user experience
  • Mentor engineers and data scientists and raise the bar on algorithm development, system design, ML and engineering practices
  • Mentor engineers and data scientists and raise the bar on algorithm development, system design, ML and engineering practices
  • Identify and drive high-impact opportunities across search and personalization
  • Identify and drive high-impact opportunities across search and personalization
  • Motivate, inspire and create a culture of experimentation and data-driven innovation while constantly striving to be an advocate for doing what is right for our customers
  • Motivate, inspire and create a culture of experimentation and data-driven innovation while constantly striving to be an advocate for doing what is right for our customers

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