Machine Learning Engineer

About the company Our client is a fast-growing software company. The role Raydar is recruiting for this role on behalf of our client. Build and ship machine learning systems that move from prototype to production, covering model work, data pipelines and the services that run them. The role suits an engineer who can take an idea through to a working, reliable feature with limited direction. What you'll do - Build, integrate and deploy AI-powered services into production environments for business customers. - Fine-tune, evaluate and apply machine learning models to practical problems. - Design scalable pipelines for model training, inference and data processing. - Improve the speed, capacity, cost and reliability of production AI systems. - Work with large datasets and connect systems to internal tools and APIs. - Work with product, research and design colleagues to deliver features end to end. - Set up evaluation methods, monitoring and feedback loops for production AI systems. Requirements What we're looking for - A Bachelor's or Master's degree in Computer Science, Engineering or a related field. - Strong proficiency in Python and common engineering or machine learning frameworks. - A track record of shipping software that runs live for real users. - Working knowledge of data movement, service interfaces and hosted compute in a public cloud. - Past hands-on work applying learned models or analytics-heavy software in practice. - Two to six years of relevant experience. - Prior work in a fast-paced startup setting, ideally as an early team member. Bonus points - Experience deploying or scaling machine learning systems at meaningful scale. - Familiarity with large language models, agents or workflow automation. - Exposure to multi-machine computing or high-volume backend platforms. - Prior founding experience or end-to-end ownership of a product. Benefits Compensation and benefits - Base salary: USD 210,000 to 325,000 per year - Health insurance - Retirement plan Location and work model - San Francisco, CA, United States - On-site - Full-time