• Design, develop, fine-tune, and evaluate machine learning, deep learning, and Generative AI models, including Large Language Models (LLMs). • Apply appropriate modeling techniques (supervised, unsupervised, NLP, deep learning) based on problem context and data constraints. • Optimize model performance across accuracy, latency, scalability, and cost dimensions. • Conduct rigorous model evaluation, validation, and benchmarking using large-scale datasets. • Apply data preprocessing, feature engineering, augmentation, and synthetic data generation techniques to improve model robustness. • Design and implement scalable, production-ready AI solutions integrated into existing platforms and workflows. • Build, maintain, and improve MLOps pipelines for model training, deployment, monitoring, and lifecycle management. • Deploy and manage AI applications in cloud environments (Azure, AWS, or GCP), including containerization and orchestration where applicable. • Monitor model performance in production; identify drift, degradation, or failures and implement remediation strategies. • Troubleshoot and resolve AI/ML engineering issues across development and production environments. • Partner with Product Managers, Product Owners, Software Engineers, Data Scientists, and Research teams to align AI solutions with business and product objectives. • Translate product requirements and use cases into technical architectures and model designs. • Support integration of AI capabilities into customer-facing products and internal platforms. • Communicate technical concepts, tradeoffs, and limitations clearly to non-technical stakeholders. • Work with structured and unstructured datasets, including healthcare, claims, and life sciences data, to build high-performance AI systems. • Ensure responsible handling, transformation, and validation of data used for model training and inference. • Collaborate with data engineering and QA teams to ensure data pipelines and AI workflows are production-ready and auditable. • Stay current with advances in Generative AI, LLM architectures, model fine-tuning techniques, and applied machine learning. • Contribute to internal best practices, standards, and reusable components for AI/ML development. • Document AI/ML workflows, architectures, methodologies, and lessons learned for internal knowledge sharing. • Proactively identify opportunities to improve scalability, reliability, and efficiency of existing AI systems.

Who we are At Twilio, we’re shaping the future of communications, all from the comfort of our homes. We deliver innovative solutions to hundreds of thousands of businesses and empower millions of developers worldwide to craft personalized customer experiences. Our dedication to remote-first work, and strong culture of connection and global inclusion means that no matter your location, you’re part of a vibrant team with diverse experiences making a global impact each day. As we continue to revolutionize how the world interacts, we’re acquiring new skills and experiences that make work feel truly rewarding. Your career at Twilio is in your hands. We use Artificial Intelligence (AI) to help make our hiring process efficient. That said, every hiring decision is made by real Twilions! . See yourself at Twilio Join the team as Twilio’s next Staff Machine Learning Engineer. About the job This position is needed to scope, design, and deploy machine learning systems into the real world, the individual will closely partner with Product & Engineering teams to execute the roadmap for Twilio’s AI/ML products and services. You will understand customers need, build data products that works at a global scale and own end-to-end execution of large scale ML solutions. To thrive in this role, you must have a deep background in ML engineering, and a consistent track record of solving data & machine-learning problems at scale. You are a self-starter, embody a growth attitude, and collaborate effectively across organizations. Responsibilities In this role, you’ll: - Build and maintain scalable machine learning solutions in production - Train and validate both deep learning-based and statistical-based models considering use-case, complexity, performance, and robustness - Demonstrate end-to-end understanding of applications and develop a deep understanding of the “why” behind our models & systems - Partner with product managers, tech leads, and stakeholders to analyze business problems, clarify requirements and define the scope of the systems needed - Work closely with data platform teams to build robust scalable batch and realtime data pipelines - Collaborate with software engineers, build tools to enhance productivity and to ship and maintain ML models - Drive high engineering standards on the team through mentoring and knowledge sharing - Uphold engineering best practices around code reviews, automated testing and monitoring Qualifications Twilio values diverse experiences from all kinds of industries, and we encourage everyone who meets the required qualifications to apply. If your career is just starting or hasn't followed a traditional path, don't let that stop you from considering Twilio. We are always looking for people who will bring something new to the table! Required: - 7+ years of applied ML experience with proficiency in Python - Strong background in the foundations of Machine Learning and building blocks of modern Deep Learning - Track record of building, shipping and maintaining Machine Learning models in production in an ambiguous and fast paced environment. - Track record of designing and architecting large scale experiments and analysis to inform product roadmap. - You have a clear understanding of frameworks like - PyTorch, TensorFlow, or Keras, why and how these frameworks do what they do - Familiarity with ML Ops concepts related to testing and maintaining models in production such as testing, retraining, and monitoring. - Demonstrated ability to ramp up, understand, and operate effectively in new application / business domains. - You’ve explored modern data storage, messaging, and processing tools (Kafka, Apache Spark, Hadoop, Presto, DynamoDB etc.) and demonstrated experience designing and coding in big-data components such as DynamoDB or similar - Experience working in an agile team environment with changing priorities - Experience of working on AWS Desired: - Experience with Large Language Models Location This role will be remote, and based in India Travel We prioritize connection and opportunities to build relationships with our customers and each other. For this role, you may be required to travel occasionally to participate in project or team in-person meetings. What We Offer Working at Twilio offers many benefits, including competitive pay, generous time off, ample parental and wellness leave, healthcare, a retirement savings program, and much more. Offerings vary by location. Twilio thinks big. Do you? We like to solve problems, take initiative, pitch in when needed, and are always up for trying new things. That's why we seek out colleagues who embody our values — something we call Twilio Magic. Additionally, we empower employees to build positive change in their communities by supporting their volunteering and donation efforts. So, if you're ready to unleash your full potential, do your best work, and be the best version of yourself, apply now! If this role isn't what you're looking for, please consider other open positions. Twilio is proud to be an equal opportunity employer. We do not discriminate based upon race, religion, color, national origin, sex (including pregnancy, childbirth, reproductive health decisions, or related medical conditions), sexual orientation, gender identity, gender expression, age, status as a protected veteran, status as an individual with a disability, genetic information, political views or activity, or other applicable legally protected characteristics. We also consider qualified applicants with criminal histories, consistent with applicable federal, state and local law. Qualified applicants with arrest or conviction records will be considered for employment in accordance with the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act. Additionally, Twilio participates in the E-Verify program in certain locations, as required by law.

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.