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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Machine Learning Engineer - **Company:** Zendesk - **Location:** United States (Remote available) - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** A/B Testing, Amazon Web Services, Amazon S3, Cloud Computing, Code Review, Data Infrastructure, Data Transformation, Python (Programming Language), Machine Learning, MySQL, Software Product Management, Tensorflow, Ruby, Software Engineering, SQL Databases, Pytorch, Delivery Pipeline, Large Language Models, Snowflake, Prompt Engineering, Event Driven Architecture, Kubernetes, Apache Kafka, Machine Learning Operations, Data Pipelines, Docker - **Published:** August 19, 2026 - **Apply:** https://arc.dev/remote-jobs/j/redirect/pdl46mmy1d ## About the Role * 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). Tech Stack * Our code is written in Ruby and Python * Our servers live in AWS * Our ML pipelines use Metaflow * Our experiment tracking uses MLflow * Our models are served via BentoML on Kubernetes * Our data is stored in S3, RDS MySQL, and Snowflake (with dbt for transformations) * Our services and models are deployed to Kubernetes using Docker * Heavy usage of LLM technology from multiple providers via our LLM Proxy ## 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. ## Related Videos - [Effective Machine Learning - Managing Complexity with MLOps](https://www.wearedevelopers.com/videos/185-effective-machine-learning-managing-complexity-with-mlops) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [MySQL Protocol Features You Should Be Aware Of](https://www.wearedevelopers.com/videos/100267-mysql-protocol-features-you-should-be-aware-of) - [Coffee with Developers: David Heinemeier Hansson](https://www.wearedevelopers.com/videos/875-coffee-with-developers-david-heinemeier-hansson) - [DevOps for Machine Learning](https://www.wearedevelopers.com/videos/179-devops-for-machine-learning) - [Docker build without Docker](https://www.wearedevelopers.com/videos/100114-docker-build-without-docker) ## Related Articles - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [Dev Digest 121 - AI goes offline](https://www.wearedevelopers.com/magazine/456-dev-digest-121-ai-goes-offline) - [How We Built a Worry-Free System That Runs for 10+ Years – And What We’d Do Again](https://www.wearedevelopers.com/magazine/751-how-we-built-a-worry-free-system-that-runs-for-10-years-and-what-we-d-do-again) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [Dev Digest 120 - Apple and peers](https://www.wearedevelopers.com/magazine/455-dev-digest-120-apple-and-peers)