• Design, build, and deploy AI/ML solutions with a focus on large language models (LLMs) using Python and AWS infrastructure. • Architect scalable APIs and microservices utilizing FastAPI, ensuring robust, high-performance systems. • Integrate LangChain and advanced AI frameworks to deliver innovative model interaction capabilities. • Collaborate with cross-functional teams to translate complex mathematical, statistical, or physics-based problem statements into actionable engineering solutions. • Apply core math, statistics, or physics knowledge to improve model performance, interpretability, and reliability. • Maintain high standards of code quality, documentation, and peer reviews within a fast-paced core team. • Communicate technical concepts and project progress clearly through both written reports and verbal discussions.

Role Description 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' needs, build data products that work 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 - 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 real-time 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 - 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 - 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 - 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 For this role, you may be required to travel occasionally to participate in project or team in-person meetings. Benefits - Competitive pay - Generous time off - Ample parental and wellness leave - Healthcare - Retirement savings program - And much more (offerings vary by location)

• Colaborar en un proyecto en modalidad full-remote. • Diseñar y desplegar múltiples entornos (desarrollo - pruebas - producción). • Implementar Infraestructura como Código (IaC) con Terraform o Crossplane. • Evaluar y monitorizar modelos o agentes de IA.

Role Description Penguin Random House is the largest trade publishing company in the world. The Data Science team is seeking a Staff Machine Learning Scientist to lead and advance the development of personalization products, including recommender systems for our websites, email programs, and online marketing. Personalization is a core growth lever for book discovery and customer engagement, directly improving how readers find the right books across every digital touchpoint. Improving recommendation quality and relevance has a direct downstream impact on customer experience and business outcomes. We are investing in expanding our portfolio of business-critical personalization products and further improving our existing models. This role will own personalization and recommender system work end-to-end, from model development to deployment to output monitoring, in close partnership with business stakeholders, platform engineers, and the rest of the personalization group. We have a mature machine learning practice and strong infrastructure, supported by strong data warehouse and DevOps partners. We are transitioning to AI-accelerated development and use modern agentic coding tools like Claude Code to speed up how we build and maintain personalization systems, with rigorous quality gates including tests, reproducible workflows, and measurable improvements in model performance and reliability. Experience with Claude Code or agentic workflows is a plus, but we prioritize strong fundamentals and the ability and willingness to learn new workflows effectively. - Define and drive the technical roadmap for personalization and recommender systems, prioritizing roadmap items to meet business goals and defining short-term vision for the team. - Propose and deliver R&D that directly shapes roadmaps, multiple projects, and long-term deliverables. Models are used over the long term by multiple products and teams. - Design and lead the development of software used by multiple teams, ensuring long-term maintainability, scalability, and adaptability. - Ensure complex, multi-service personalization products meet SLAs and provide correct results over time. Adapt systems to changing business needs and resolve multi-product, multi-team service incidents. - Establish and enforce experimentation best practices, including A/B testing frameworks, offline evaluation methodology, and metrics design across personalization surfaces. - Lead team meetings, ensure the team's progress on the roadmap, and make technical decisions that unblock projects. - Manage stakeholders' expectations with data-driven narratives and communicate effectively with senior leadership to align on strategy and track progress. - Drive organizational efficiency and business impact by implementing new technologies and processes. Foster a collaborative and high-performance team culture. - Mentor senior and mid-level scientists, setting high code quality standards and best practices for the team. - Stay current with advances in recommender systems, LLMs for personalization, and representation learning, bringing relevant advances into production when they deliver measurable improvement. Qualifications - PhD in Computer Science, Machine Learning, Engineering, Operations Research, Statistics, or a related quantitative field, OR Master's with 8+ years of applied ML experience. - Deep expertise in recommender systems, personalization, ranking/retrieval, or computational advertising, with a track record of shipping systems that operate at scale. - Expert-level Python and deep proficiency with modern ML frameworks (PyTorch or TensorFlow) and recommendation-specific tooling (e.g., NVTabular, Merlin, Triton). - Strong experience with cloud-based ML infrastructure (AWS, Kubernetes, Databricks), containerization (Docker), and model serving at low latency. - Advanced SQL skills and experience architecting large-scale data pipelines and feature stores. - Demonstrated ability to define technical roadmaps, influence direction across teams, and make architectural decisions that hold up over time. - Excellent communication skills with the ability to present complex technical work to executive and non-technical audiences. - Be cutting edge. Use the latest AI tools to develop well-designed and robust software. Requirements - Experience building and scaling real-time recommendation services handling millions of requests. - Expertise in A/B testing methodology, causal inference, or experimentation platforms. - Familiarity with LLM-based approaches to recommendation and content understanding. - Experience with MLOps practices: model monitoring, feature stores, CI/CD for ML, and automated retraining pipelines. - Prior experience technically leading a team of ML practitioners and setting standards adopted by others. Benefits - Medical/Prescription drug insurance - Dental - Vision - Health Care/Dependent Care Flexible Spending Account - Health Savings Account - Pre-Tax and Roth 401(k) - Short and Long-Term Disability Insurance - Life/AD&D Insurance - Commuter Benefits - Student Loan Repayment Program - Educational Assistance - Generous paid time off