Senior Machine Learning Engineer

Zendesk•Huntersville, NC

46d•PLN 302,000 - PLN 454,000•Hybrid

About The Position

At Zendesk, our focus is helping our customers build great relationships with their customers. Founded by three Danish entrepreneurs, Zendesk has experienced remarkable success and growth while maintaining a fun, positive, and down-to-earth culture. We are looking for a Senior ML Engineer to join our AI Copilot organisation. AI Copilot is a multi-million ARR product that puts AI directly into the hands of customer service agents and administrators. You will own the delivery of ML-powered product features at Zendesk scale, taking capabilities from prototype through to production. We ship to learn: our philosophy is to deliver early, deliver often, and iterate based on real-world customer feedback.

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.

Nice To Haves

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).

Responsibilities

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.