Utah Staffing
Senior Machine Learning Engineer
Where data does more. Join the Snowflake team. There is only one data cloud. Snowflake's founders started from scratch and designed a data platform built for the cloud that is effective, affordable, and accessible to all data users. But it didn't stop there. They engineered Snowflake to power the data cloud, where thousands of organizations unlock the value of their data with near-unlimited scale, concurrency, and performance. This is our vision: a world with endless insights to tackle the challenges and opportunities of today and reveal the possibilities of tomorrow. We're at the forefront of the data revolution, committed to building the world's greatest data and applications platform. Our 'get it done' culture allows everyone at Snowflake to have an equal opportunity to innovate on new ideas, create work with a lasting impact, and excel in a culture of collaboration. We're looking for a Senior Machine Learning Engineer to join Snowflake's Corporate Machine Learning team. In this role, you will design, build, and optimize scalable systems that power AI and ML driven solutions across Snowflake's business data. This is a high-impact engineering role focused on taking AI and ML applications from prototype to production, partnering closely with ML and Data Scientists and cross-functional teams to ensure robust and performant deployment of machine learning solutions. On the Corporate ML Team at Snowflake, you will: Provide technical and thought leadership; designing and implementing advanced machine learning techniques, focusing on robust and scalable solutions. Lead development for ML systems: Design, build, and maintain production-grade ML systems, with a focus on performance, scalability, and maintainability. Operationalize ML models: Partner with ML Scientists to translate models into efficient, reliable pipelines and services, enabling seamless deployment and monitoring in production environments. Architect end-to-end ML infrastructure: Own the full lifecycle of ML solutions - from feature engineering and data pipelines to model serving, CI/CD, observability, and retraining. Develop hands-on; analyze large amounts of data, manage data quality, design and develop complex ML models (and the ensuing ML solutions) including ML pipelines, deploy and manage production-grade applications end-to-end, and tell the story in a compelling manner. Collaborate across teams: Work closely with data scientists, data engineers, platform teams, and business stakeholders to deliver solutions that align with product and business needs. Champion MLOps best practices: Establish and maintain infrastructure/tooling for versioning, experimentation, testing, deployment, and monitoring of ML models. Enable reproducibility and scale: Develop reusable components, templates, and automation to scale ML development across use cases and teams. Mentor and guide: Provide technical mentorship to junior engineers and scientists on engineering practices and production workflows. Our ideal candidate will have: High levels of curiosity, eager enthusiasm and demonstrable experience working on open-ended problems. Strong software engineering foundations, with expertise in Python and experience developing production-quality systems using best practices in testing, modularity, and documentation. Deep experience in MLOps and ML infrastructure, including... Snowflake is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, color, gender identity or expression, marital status, national origin, disability, protected veteran status, race, religion, pregnancy, sexual orientation, or any other characteristic protected by applicable laws, regulations, and ordinances.
Where data does more. Join the Snowflake team. There is only one data cloud. Snowflake's founders started from scratch and designed a data platform built for the cloud that is effective, affordable, and accessible to all data users. But it didn't stop there. They engineered Snowflake to power the data cloud, where thousands of organizations unlock the value of their data with near-unlimited scale, concurrency, and performance. This is our vision: a world with endless insights to tackle the challenges and opportunities of today and reveal the possibilities of tomorrow. We're at the forefront of the data revolution, committed to building the world's greatest data and applications platform. Our 'get it done' culture allows everyone at Snowflake to have an equal opportunity to innovate on new ideas, create work with a lasting impact, and excel in a culture of collaboration. We're looking for a Senior Machine Learning Engineer to join Snowflake's Corporate Machine Learning team. In this role, you will design, build, and optimize scalable systems that power AI and ML driven solutions across Snowflake's business data. This is a high-impact engineering role focused on taking AI and ML applications from prototype to production, partnering closely with ML and Data Scientists and cross-functional teams to ensure robust and performant deployment of machine learning solutions. On the Corporate ML Team at Snowflake, you will: Provide technical and thought leadership; designing and implementing advanced machine learning techniques, focusing on robust and scalable solutions. Lead development for ML systems: Design, build, and maintain production-grade ML systems, with a focus on performance, scalability, and maintainability. Operationalize ML models: Partner with ML Scientists to translate models into efficient, reliable pipelines and services, enabling seamless deployment and monitoring in production environments. Architect end-to-end ML infrastructure: Own the full lifecycle of ML solutions - from feature engineering and data pipelines to model serving, CI/CD, observability, and retraining. Develop hands-on; analyze large amounts of data, manage data quality, design and develop complex ML models (and the ensuing ML solutions) including ML pipelines, deploy and manage production-grade applications end-to-end, and tell the story in a compelling manner. Collaborate across teams: Work closely with data scientists, data engineers, platform teams, and business stakeholders to deliver solutions that align with product and business needs. Champion MLOps best practices: Establish and maintain infrastructure/tooling for versioning, experimentation, testing, deployment, and monitoring of ML models. Enable reproducibility and scale: Develop reusable components, templates, and automation to scale ML development across use cases and teams. Mentor and guide: Provide technical mentorship to junior engineers and scientists on engineering practices and production workflows. Our ideal candidate will have: High levels of curiosity, eager enthusiasm and demonstrable experience working on open-ended problems. Strong software engineering foundations, with expertise in Python and experience developing production-quality systems using best practices in testing, modularity, and documentation. Deep experience in MLOps and ML infrastructure, including... Snowflake is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, color, gender identity or expression, marital status, national origin, disability, protected veteran status, race, religion, pregnancy, sexual orientation, or any other characteristic protected by applicable laws, regulations, and ordinances.