At Netflix, our mission is to entertain the world. Together, we are writing the next episode - pushing the boundaries of storytelling, global fandom and making the unimaginable a reality. We are a dream team obsessed with the uncomfortable excitement of discovering what happens when you merge creativity, intuition and cutting-edge technology. Come be a part of what’s next. We launched a new ad-supported tier in November 2022 and are building an in-house world-class ad tech ecosystem to offer our members more choices in consuming their content. Our new tier allows us to attract new members at a lower price point while also creating a compelling path for advertisers to reach deeply engaged audiences. Our Team: The Ad Ranking team within the Ads Data Science and Engineering organization is the central intelligence driving ad personalization at Netflix. The team is responsible for enhancing ad quality and performance through advanced machine learning and optimization algorithms, utilizing both proprietary and external data signals. Key areas of focus include Identity Science, User Understanding, Audience & Targeting, Relevance & Engagement Prediction, and Bidding & Pacing. Our goal is to create innovative, data-driven solutions that deliver highly relevant ad experiences for our members and achieve impactful results for advertisers, all while upholding the exceptional quality and personalization characteristic of the Netflix experience. Responsibilities: - Design and implement machine learning and optimization algorithms to improve ad quality and performance. - Build, train, and evaluate models on large-scale production data. - Develop online and offline evaluation frameworks to rigorously measure the impact of model and algorithm improvements. - Partner closely with the product team to define optimization objectives, constraints, and trade-offs that align with product and business goals. - Communicate technical decisions, trade-offs, and experiment results to both technical and non-technical stakeholders, driving understanding and adoption of ML-driven solutions. Qualifications: - Advanced degree (PhD or Master’s) in Computer Science, Statistics, Mathematics, or related quantitative field. - Proficiency in Python, Scala or Java. - Deep knowledge of machine learning, optimization, and data analysis techniques. - Experience with prototyping and deploying algorithms using large-scale production data. - Strong business acumen and ability to translate technical results into business impact. - Experience in ad optimization stack, e.g. targeting, ranking, bidding.. - Excellent communication and collaboration skills. Generally, our compensation structure consists solely of an annual salary; we do not have bonuses. You choose each year how much of your compensation you want in salary versus stock options. To determine your personal top of market compensation, we rely on market indicators and consider your specific job family, background, skills, and experience to determine your compensation in the market range. The range for this role is $466,000.00 - $750,000.00. This compensation range will vary based on location. Netflix provides comprehensive benefits including Health Plans, Mental Health support, a 401(k) Retirement Plan with employer match, Stock Option Program, Disability Programs, Health Savings and Flexible Spending Accounts, Family-forming benefits, and Life and Serious Injury Benefits. We also offer paid leave of absence programs. Full-time hourly employees accrue 35 days annually for paid time off to be used for vacation, holidays, and sick paid time off. Full-time salaried employees are immediately entitled to flexible time off. See more details about our Benefits here. Netflix is a unique culture and environment. Learn more here. Inclusion is a Netflix value and we strive to host a meaningful interview experience for all candidates. If you want an accommodation/adjustment for a disability or any other reason during the hiring process, please send a request to your recruiting partner. We are an equal-opportunity employer and celebrate diversity, recognizing that diversity builds stronger teams. We approach diversity and inclusion seriously and thoughtfully. We do not discriminate on the basis of race, religion, color, ancestry, national origin, caste, sex, sexual orientation, gender, gender identity or expression, age, disability, medical condition, pregnancy, genetic makeup, marital status, or military service. Job is open for no less than 7 days and will be removed when the position is filled.

• Design, develop, fine-tune, and evaluate machine learning, deep learning, and Generative AI models. • Optimize model performance across accuracy, latency, scalability, and cost dimensions. • Design and implement scalable, production-ready AI solutions. • Build, maintain, and improve MLOps pipelines for model training, deployment, monitoring, and lifecycle management. • Partner with Product Managers, Product Owners, Software Engineers, Data Scientists, and Research teams to align AI solutions with business and product objectives. • Work with structured and unstructured datasets, including healthcare, claims, and life sciences data, to build high-performance AI systems.

• Lead and scale a high-performing engineering organization responsible for the Personalization Platform that powers real-time, personalized product experiences and multi-channel targeted user messaging across Capital One products and services. • Define the technical strategy, delivery roadmap, and operating model for a portfolio spanning recommendation systems, ranking, decisioning, GenAI infrastructure, MLOps, and low-latency application-serving systems. • Build, develop, and manage engineers and engineering leaders; set a high bar for hiring, performance, talent density, coaching, and succession planning across the organization. • Partner cross-functionally with Product, Data Science, Cloud Infrastructure, and Machine Learning Platform teams to align strategy, prioritize investments, and co-develop advanced recommendation systems and algorithms serving Capital One users. • Drive the design, buildout, and operation of robust ML infrastructure and pipelines supporting feature extraction, model training, testing, guardrails, evaluation, deployment, and both real-time and batch inference with strong reliability, scalability, and operational rigor. • Architect low-latency, event-driven systems for real-time personalization and decisioning based on streaming data, user behavior, and contextual signals. • Drive the evolution of MLOps practices through automated, metrics-backed deployment workflows, validation and testing systems, model lifecycle governance, and scalable observability. • Guide the adoption of state-of-the-art AI and LLM optimization techniques to improve scalability, cost, latency, throughput, and reliability of large-scale production AI systems. • Provide organizational technical and people leadership by influencing architecture, engineering standards, delivery excellence, incident management, and cross-team strategy while mentoring managers, tech leads, and senior engineers. • Make high judgment build-vs-buy decisions across a broad stack of Open Source and SaaS AI technologies such as AWS Ultraclusters, Huggingface, VectorDBs, Nemo Guardrails, PyTorch, and more. • Attract and retain top talent in the AI industry and nurture personal and professional development for your team. • Foster a culture of learning and staying abreast of the state-of-the-art in AI.

Overview Come build community, explore your passions, and do your best work at Microsoft while helping a team deliver machine learning (ML) capabilities that are reliable, measurable, and ready to integrate into real products and services. This opportunity will allow you to bring your aspirations, talent, potential and excitement for the journey ahead. As a Machine Learning Engineer (MLE), you will contribute—under guidance and with support from teammates—to understand ML requirements for a feature, prepare data, train baseline models, and evaluate results using standard metrics. This opportunity will allow you to grow your skills in ML workflows, model integration, and engineering practices that support security, privacy, accessibility, and responsible AI. Microsoft’s mission is to empower every person and every organization on the planet to achieve more. As employees, we come together with a growth mindset, innovate to empower others and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond. Responsibilities - Implements data preprocessing steps, baseline models, and evaluation approaches under supervision. - Contributes small, well-defined components of ML workflows (e.g., dataset preparation, training utilities, evaluation scripts) with guidance. - Supports integration of ML models into existing systems and services, coordinating with engineering partners as needed. - Follows established practices for reproducibility, logging, monitoring, and secure development throughout the ML lifecycle. - Collaborates with teammates and partner teams to validate end-to-end functionality prior to release. - Learns and applies team processes related to security, privacy, accessibility, and responsible AI in day-to-day work. Qualifications Required Qualifications - Master's Degree in Computer Science, or related technical discipline with proven experience coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience. - Proficiency to speak, write and read in English language. Preferred Qualifications - Proficiency in one programming language (Python preferred) and common ML libraries (e.g., scikit-learn, PyTorch, TensorFlow). - Understanding of ML and GenAI fundamentals (e.g., supervised learning, evaluation metrics, overfitting/underfitting, regularization, embeddings, tokenization, transformers). - Ability to manipulate structured and unstructured data (e.g., pandas, SQL). - Familiarity with Git, code reviews, testing, and debugging in a collaborative environment. - Interest in learning deployment patterns, monitoring/observability, and responsible AI practices. This position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled. Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, citizenship, color, family or medical care leave, gender identity or expression, genetic information, immigration status, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran or military status, race, ethnicity, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable local laws, regulations and ordinances. If you need assistance with religious accommodations and/or a reasonable accommodation due to a disability during the application process, read more about requesting accommodations.