Our team is responsible for helping Customer Experience teams to achieve their best, by intelligently solving repetitive work, so they can shift their focus to solving more sophisticated problems. We use the latest trends in Machine Learning and AI algorithms to help us on that mission, and we're passionate about empowering our customers.
As a Junior Machine Learning Scientist, you will drive development, evaluation, and deployment of novel ML/AI models to power intelligent automation and customer service solutions at scale. You will collaborate closely with engineers, product managers, and cross-functional teams to translate research into solutions directly impacting millions of support interactions.
What you get to do every day
Research, prototype, and develop state-of-the-art NLP/ML models for use cases such as intent detection, auto-assist, chatbots and intelligent agent routing.
Design and execute rigorous experiments and evaluations (offline/online, A/B) to improve model accuracy and robustness.
Work closely with ML Engineers to productionize ML solutions—including data pipelines, scalable model serving, and monitoring.
Analyze large, multi-lingual customer interaction datasets to uncover insights and power new solutions.
Participate in technical reviews and share knowledge of underlying ML methodologies and best practices.
Present your work to a multi-disciplinary, global audience.
Stay up to date with recent literature in Machine Learning and Natural Language Processing (NLP) and share knowledge internally.
Key challenges / use cases
How do we enrich customer service conversations with accurate language detection, intent recognition, and real-time sentiment analysis, to enable proactive customer engagement and optimal routing?
How can we automate all customer service interactions as much as possible, from process automation to agent assistance and chatbots with a knowledge base?
How do we optimize routing at scale—matching tickets or chats to the most appropriate agent/team in real-time across multiple languages and regions?
How do we automate large-scale A/B testing and model evaluation (online and offline) to continually iterate and improve ML-driven triage and agent-assist tools?
What novel approaches or architectures (e.g., retrieval-augmented generation, few-shot/fine-tuning strategies) can extend our conversational AI platforms to unlock new customer support use cases and modalities?
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?
How do we combine signals from conversation context, customer history, and external data to improve prediction and decision accuracy across our ML services?
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?
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?
And many more!
What you bring to the role
MSc (or PhD) degree in computer science, electrical engineering, math, or related areas.
A good foundation in statistics and machine learning techniques.
Solid coding skills in Python; experience with ML frameworks (preferably PyTorch).
Experience with deep learning and/or NLP is a bonus.
Great written and verbal communication skills.
A collaborative and can-do attitude.
A desire to learn and to grow.
What our tech stack looks like
Our code is written in Python and Ruby.
Our servers live in AWS.
Our machine learning models rely on PyTorch.
Our ML pipelines use AWS Batch and MetaFlow.
Our data is stored in S3, RDS MySQL, Redis, ElasticSearch, Snowflake and Aurora.
Our services are deployed to Kubernetes using Docker, and use Kafka for stream-processing.
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The intelligent heart of customer experience
Zendesk software was built to bring a sense of calm to the chaotic world of customer service. Today we power billions of conversations with brands you know and love.
Zendesk believes in offering our people a fulfilling and inclusive experience. Our hybrid way of working, enables us to purposefully come together in person, at one of our many Zendesk offices around the world, to connect, collaborate and learn whilst also giving our people the flexibility to work remotely for part of the week.
As part of our commitment to fairness and transparency, we inform all applicants that artificial intelligence (AI) or automated decision systems may be used to screen or evaluate applications for this position, in accordance with Company guidelines and applicable law.
Zendesk is an equal opportunity employer, and we’re proud of our ongoing efforts to foster global diversity, equity, & inclusion in the workplace. Individuals seeking employment and employees at Zendesk are considered without regard to race, color, religion, national origin, age, sex, gender, gender identity, gender expression, sexual orientation, marital status, medical condition, ancestry, disability, military or veteran status, or any other characteristic protected by applicable law. We are an AA/EEO/Veterans/Disabled employer. If you are based in the United States and would like more information about your EEO rights under the law, please click here.
Zendesk endeavors to make reasonable accommodations for applicants with disabilities and disabled veterans pursuant to applicable federal and state law. If you are an individual with a disability and require a reasonable accommodation to submit this application, complete any pre-employment testing, or otherwise participate in the employee selection process, please send an e-mail to peopleandplaces@zendesk.com with your specific accommodation request.
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