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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Staff Machine Learning Platform Engineer - **Company:** Hybrid Faire - **Location:** San Francisco, CA, United States (Remote available) - **Experience:** Expert - **Salary:** $295,000.0 - **Contract:** Permanent contract - **Skills:** Airflow, Amazon Web Services, Amazon S3, Big Data, Cloud Computing, Cloud Engineering, Software Quality, Continuous Integration, Data Architecture, Distributed Systems, Github, Python (Programming Language), Machine Learning, MySQL, Open Source Technology, Tensorflow, Azure Machine Learning, SQL Databases, Datadog, Pytorch, Large Language Models, Snowflake, Apache Spark, Generative AI, Kotlin, Data Lakes, Pyspark, Kubernetes, Information Technology, Apache Kafka, Data Management, Machine Learning Operations, Terraform, Docker, Databricks - **Published:** July 17, 2026 - **Apply:** https://www.dice.com/job-detail/5402f0ca-8719-40b0-b715-78b8ec5f4816 ## About the Role We're looking for smart, resourceful and passionate people to join us as we power the shop local movement. If you believe in community, come join ours., * 10-12 years of experience building and improving large-scale ML or data platforms. * A degree (preferably graduate level) in Computer Science, Engineering, Statistics, or a related technical field. * Deep expertise in Databricks lakehouse architecture, including governance via Unity catalog, orchestration via Workflows, and cost optimization * Proven ability to design systems that support multiple data science teams and production workloads * Strong background in distributed systems, ML infrastructure, and cloud architecture. * Demonstrated technical leadership across teams and orgs; ability to influence without authority * Experience integrating LLM workflows into enterprise platforms is a plus * Previous contributions to open source ML Infrastructure projects or research publications is a very strong plus Tech Stack Faire uses a modern cloud based tech stack. For this role, you'll want to be proficient with the following: Category Technologies Languages Python, SQL, Kotlin ML Frameworks PyTorch, PySpark, MLFlow Big Data & Processing Spark, Kafka, Databricks, Snowflake, Fivetran, Iceberg, Unity Catalog, Datadog, Airflow, Cockroach DB, MySQL Cloud & Infrastructure AWS, S3, SageMaker, Kubernetes, Docker, GitHub Actions, Terraform Generative AI ## Description As the Senior Staff Machine Learning Platform Engineer, you will own the technical vision and evolution of Faire's ML platform. You will set standards, influence org-wide architecture, and lead complex, cross-functional initiatives that unlock data science velocity at scale. This role will also be key to adapting ML workflows to take advantage of modern AI productivity tools. You won't just build models, you will architect the systems that allow those models to help tens of thousands of small retailers compete and grow their local businesses. What You Will Do * Define and drive the long-term architecture of Faire's ML platform including training, inference, feature management, governance * Establish company-wide standards for code quality, testing, MLOps (CI/CD), experimentation, model lifecycle management, and observability * Lead adoption and advanced use of Unity Catalog, multi-workspace strategies, and data/ML mesh patterns * Architect highly scalable ML workflows using Spark, Delta Lake, and MLflow * Optimize performance, reliability, and cost of the ML platform * Evaluate and integrate emerging Databricks features * Stay ahead of the curve by engaging with the latest developments in machine learning and AI * Serve as senior ML technical advisor to Faire's data science and production engineering teams * Represent Faire at ML conferences and meetups * Mentor ML engineers and raise the overall bar for Machine Learning at Faire ## Related Videos - [MySQL Protocol Features You Should Be Aware Of](https://www.wearedevelopers.com/videos/100267-mysql-protocol-features-you-should-be-aware-of) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [Kotlin Multiplatform - True power of native code reuse](https://www.wearedevelopers.com/videos/4-kotlin-multiplatform-true-power-of-native-code-reuse) - [Shipping Faster with Less: Render on Cloud Hosting, AI Workloads, and the Future of DevOps](https://www.wearedevelopers.com/videos/1894-shipping-faster-with-less-render-on-cloud-hosting-ai-workloads-and-the-future-of-devops) - [Coding for Good: Achieving social change with an app](https://www.wearedevelopers.com/videos/1645-coding-for-good-achieving-social-change-with-an-app) - [Empowering Retail Through Applied Machine Learning](https://www.wearedevelopers.com/videos/976-empowering-retail-through-applied-machine-learning) ## Related Articles - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [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) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [Dev Digest 121 - AI goes offline](https://www.wearedevelopers.com/magazine/456-dev-digest-121-ai-goes-offline) - [Dev Digest 120 - Apple and peers](https://www.wearedevelopers.com/magazine/455-dev-digest-120-apple-and-peers)