Senior Machine Learning Scientist

<h2>Job Description</h2><p></p><p><span> </span></p><p><span>Zendesk’s people have one goal in mind: to make Customer Experience better. Our products help more than 125,000 global brands (AirBnb, Uber, JetBrains, Slack, among others) make their billions of customers happy, every day.</span></p><p><span>Our team is dedicated to providing a state-of-the-art retrieval-augmented generation (RAG) platform across multiple channels; including customer service bots, email and search. In collaboration with Software and ML Engineers, we deliver high-quality AI products leveraging the latest tools and techniques, and serve them at a scale that most companies can only dream of. We’re passionate about empowering end-users to quickly find answers to their questions, and helping our customers make the most of their knowledge base.</span></p><p><span>We’re looking for a Senior ML Scientist to join our team. You will collaborate closely with engineers, product managers, and cross-functional teams to translate research into solutions directly impacting millions of support interactions. You will play a key role in levelling up the RAG platform powering Zendesk!</span></p><h2></h2><p></p><h2><span>What you’ll be doing</span></h2><ul><li><p><span>Research, prototype, and develop state-of-the-art NLP/ML models for use cases to drive automated resolutions for end-user issues.</span></p></li><li><p><span>Design and execute rigorous experiments and evaluations (offline/online, A/B) to improve model accuracy and robustness.</span></p></li><li><p><span>Improve prompts and hyperparameters to optimize our state-of-the-art retrieval and generative capabilities</span></p></li><li><p><span>Work closely with ML Engineers to productionize ML solutions—including data pipelines, scalable model serving, and monitoring.</span></p></li><li><p><span>Analyze large, multi-lingual customer interaction datasets to uncover insights and power new solutions.</span></p></li><li><p><span>Participate in technical reviews and share knowledge of underlying ML methodologies and best practices.</span></p></li><li><p><span>Present your work to a multi-disciplinary, global audience.</span></p></li><li><p><span>Stay up to date with recent literature in Machine Learning and Natural Language Processing (NLP) and share knowledge internally.</span></p></li><li><p><span>Champion initiatives to improve the quality and robustness of Zendesk AI capabilities.</span></p></li><li><p><span>Mentor junior scientists and help grow the ML research culture.</span></p></li></ul><h2></h2><p></p><h2><span>Key challenges / use cases</span></h2><ul><li><p><span>How do we enrich customer service conversations with accurate language detection and task classification, efficient retrieval and real-time conversation generation, to enable proactive customer engagement and optimal resolution?</span></p></li><li><p><span>How can we automate all customer service interactions as much as possible with omni-channel bots with a knowledge base?</span></p></li><li><p><span>How do we automate large-scale A/B testing and model evaluation (online and offline) to continually iterate and improve our RAG tools?</span></p></li><li><p><span>What novel approaches or architectures (e.g., retrieval-augmented generation, agentic, few-shot/fine-tuning strategies) can extend our conversational AI platforms to unlock new customer support use cases and modalities?</span></p></li><li><p><span>How do we efficiently operationalize, monitor, and update large-scale (LLM/ML) models in dynamic, high-throughput production settings, ensuring model health, drift detection, and continuous learning?</span></p></li><li><p><span>How do we combine signals from conversation context, customer history, and external data to improve prediction and decision accuracy across our ML services?</span></p></li><li><p><span>What are the emerging advancements in ML/AI research (e.g., large language models, efficient adaptation, re-ranking, retrieval, or explainable AI) that should be incorporated into Zendesk’s customer experience ecosystem?</span></p></li><li><p><span>How can we bridge the gap between cutting-edge research and impactful product features, rapidly validating ideas in production and quantifying their real-world business value?</span></p></li><li><p><span>And many more!</span></p></li></ul><p></p><h2><span>What you bring to the role</span></h2><ul><li><p><span>MSc degree (PhD preferred) in computer science, electrical engineering, math, or related areas. </span></p></li><li><p><span>Deep knowledge of ML theory, algorithms, and modern NLP/LLM techniques.</span></p></li><li><p><span>Demonstrated ability to conduct independent research and deliver production-grade ML solutions.</span></p></li><li><p><span>Strong coding skills in Python; experience with ML frameworks (preferably PyTorch).</span></p></li><li><p><span>Experience with large-scale experimentation (e.g., A/B testing), data analysis, and performance tracking.</span></p></l