Staff Machine Learning Scientist

Staff Machine Learning Scientist

Lisbon, Portugal

time type

Full time

 

As a Staff Machine Learning Scientist you are a recognized leader and domain expert, responsible for advancing the state-of-the-art in ML/AI for customer service at a global scale. You steer research vision, mentor scientists across teams, and drive adoption of foundational models powering Zendesk’s most impactful features.

 

What you get to do every day

• Lead end-to-end design, development, and deployment of novel ML/LLM models and algorithms—defining research agendas that shape Zendesk’s AI-powered roadmap.

• Pioneer large, complex initiatives across product lines, such as building multilingual, real-time language/intent/sentiment analysis frameworks, conversational AI agents, and next-generation agent-assist solutions.

• Define and drive experimentation standards, statistically robust offline/online evaluations, and model governance for compliance, fairness, and explainability.

• Bridge cutting-edge research and production, collaborating with Engineering to build systems that scale globally and meet real-world performance constraints.

• Mentor, guide, and develop Senior Scientists and Engineers, fostering a culture of scientific rigor, creativity, and technical excellence across the organization.

• Represent Zendesk externally—publishing papers, speaking at conferences, and engaging with the wider ML/AI community.

• Advise leadership on ML/AI technology strategy and assess emerging industry trends for integration into Zendesk solutions.

• Mentor junior scientists and help grow the ML research culture.

 

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 degree (PhD preferred) in computer science, electrical engineering, math, or related areas. 

• Substantial track record of impactful research and deployment of ML/AI solutions at scale—preferably in NLP, LLMs or information retrieval.

• Proven technical and research leadership across projects/teams; ability to define research vision and influence organizational direction.

• Deep expertise in experimental design, statistical analysis, and ML science best practices.

• Strong coding skills in Python; experience with ML frameworks (preferably PyTorch).

• Experience with large-scale experimentation (e.g., A/B testing), data analysis, and performance tracking.

• Outstanding mentorship and communication skills—able to both advance scientific discourse and influence engineering/product execution.

• Be pragmatic and results oriented.

• Recognized contributions to the scientific community (publications, open source, talks) a strong plus.

 

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. 

#LI-AO1

Hybrid: In this role, our hybrid experience is designed at the team level to give you a rich onsite experience packed with connection, collaboration, learning, and celebration - while also giving you flexibility to work remotely for part of the week. This role must attend our local office for part of the week. The specific in-office schedule is to be determined by the hiring manager.

 

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.