Senior Machine Learning Engineer

🕒 July 23

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Zendesk

WebsiteLinkedInAll Job Openings

5001 - 10000 employees

Founded 2007

💼 Consulting

📣 Marketing

📦 Logistics

💰 Grant on 2019-09

Consulting • Marketing • Logistics

Zendesk is a company that provides a comprehensive suite of customer service and sales solutions. Their offerings include messaging, live chat, AI-driven automation, data privacy and protection, and an agent workspace, all designed to enhance customer experience and support operations. Their platform is designed to be flexible and scalable, catering to businesses of all sizes, including small enterprises, startups, and large corporations. Zendesk emphasizes the importance of integrated workflows and personalized customer interactions, leveraging customer data and AI to improve service efficiency. Additionally, they provide professional services, resources for training, and a strong emphasis on diversity, inclusion, and sustainability.

📋 Description

• Own and deliver ML-powered product features end-to-end — from data pipelines and model integration through serving, monitoring, and iteration in production. • Work closely with Scientists to productionise research outputs into reliable, user-facing features. • Build and maintain ML infrastructure: model serving, inference pipelines, LLM integrations, and evaluation frameworks. • Contribute to technical design discussions and architecture decisions within your team, with growing influence across teams. • Collaborate with product software engineers to ensure ML capabilities are well-integrated into the broader product experience. • Improve the reliability, performance, and cost-efficiency of the ML systems you work on — proactively identifying and addressing issues. • Mentor more junior engineers through code review, pairing, and knowledge sharing.

🎯 Requirements

• 5+ years of experience in software engineering, with a meaningful focus on ML engineering, MLOps, or building ML-powered products. • Fluent in Python; working proficiency in Ruby is a plus. • Solid experience building and operating ML systems in production: model serving, inference pipelines, and monitoring. • Experience integrating LLMs into production systems — prompt engineering, evaluation, or multi-provider setups. • Comfortable with SQL and data infrastructure — you can work with data pipelines, transformations, and data quality. • Experience with containerised deployments (Docker, Kubernetes) and cloud infrastructure (AWS). • A track record of owning features end-to-end and delivering them to production with high quality. • Ability to work with uncertainty and the flexibility to pivot with changing priorities. • Strong collaboration skills — you work effectively with scientists, product engineers, and product managers. • Preferred Experience with Snowflake and dbt for data transformations and analytics. • Hands-on experience with ML pipeline tooling (e.g., Metaflow) and experiment tracking (e.g., MLflow). • Experience with model serving frameworks (e.g., BentoML) on Kubernetes. • Familiarity with ML frameworks such as PyTorch or TensorFlow. • Experience with event-driven architectures (e.g., Kafka). • Experience with iterative, metrics-driven product development (A/B testing, feature flags, incremental rollouts).

🏖️ Benefits

• Flexible working hours • Professional development opportunities