Staff Machine Learning Scientist

Staff Machine Learning Scientist

Zendesk

Multiple Locations

We are delighted to be hiring a Staff Machine Learning Scientist for the AI Agent product at Zendesk. This is an exciting opportunity for a senior-level leader to not only bridge the gap between cutting-edge ML research and real-world impact but to actively define that bridge. You will play a pivotal role in shaping our product vision through bottom-up innovation, rapid prototyping, and the establishment of best practices. You'll have the freedom to discover and champion new product ideas, mentor junior scientists, and work alongside a talented cross-functional team to build the world’s most advanced AI agents in CX.

Key Responsibilities

• Pioneer Product Vision and Innovation: You will partner closely with product managers and other stakeholders to discover and define the product roadmap. You'll champion bottom-up innovation, bringing forward novel ideas and research to influence the team's strategic direction.
• Lead Rapid Prototyping: You will own the full research and development lifecycle from end-to-end, translating business requirements into technical designs. This includes the rapid implementation and testing of hypotheses using suitable tools and technologies to quickly validate solutions for future production.
• Standardize Research and Evaluation: You will establish and evangelize research standards and best practices for the evaluation of our AI agents, ensuring consistency and rigor in how we measure success.
• Mentor and Enable the Team: You will act as a force multiplier, mentoring other scientists and engineers, and enabling them to tackle complex challenges. You will also help non-technical stakeholders, like Product Managers, better understand the technical landscape.
• Drive End-to-End Projects: You will own complex, designated product areas, guiding the team through the full research project lifecycle, from initial ideation to implementation, experimentation, and reporting.
• Stay at the Forefront of Research: You will keep up with the latest advancements in the NLP and ML fields, introducing new concepts and literature as potential solutions to our business problems.
• Collaborate and Peer Review: You will regularly discuss and peer-review research ideas with your colleagues, contributing to a culture of shared learning and continuous improvement.

Who You Are

• A Proven Innovator: You have a strong track record of Machine Learning or NLP research, ideally in a senior industry role. You are known for not just solving problems, but for identifying and framing them.
• Expert in Deep Learning: You have deep knowledge of building deep learning neural network architectures, with a focus on NLP. You can implement complex ideas from research literature into robust proof-of-concept models.
• Experienced with Generative AI: You have significant experience with LLMs, Prompt Engineering, and AI Agent Context Management. Experience with conversational AI and voice technologies, including text-to-speech (TTS) and speech-to-speech (STS), is a huge bonus.
• A Collaborative Leader: You have a positive, proactive, and team-oriented mindset. You are passionate about your craft and are keen to take part in product discussions. You care deeply about helping others succeed and have great communication skills.
• The Right Qualifications: You hold a degree in Computer Science, Machine Learning, Statistics, Engineering, Mathematics, or a related field.

How We Measure Success

• Strategic Impact: Your work directly influences and aligns with the overall product roadmap and strategic goals.
• Mentorship and Team Growth: Your ability to mentor and enable others demonstrably improves the team's capabilities and output.
• Bottom-Up Innovation: Your ability to identify new opportunities and champion these ideas leads to tangible improvements in our products.
• Research Standardization: The research evaluation frameworks you establish are widely adopted and improve the quality and rigor of our work.
• Collaboration: Your effectiveness in collaborating with product managers, engineers, designers, and other stakeholders helps bridge technical and non-technical gaps.