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Description
As a Machine Learning Solutions Engineer, you'll play a crucial role in our ML product development lifecycle. You'll collaborate with ML researchers, software engineers, product managers, and designers. You'll be responsible for prototyping ML-powered features, evaluating their technical feasibility and business impact, and guiding the implementation process. In this role, you will build proof-of-concepts that demonstrate ML capabilities in practical contexts, develop strategies for measuring product value, design effective evaluation frameworks, and help build flawless moves between different ML models as technologies evolve. You'll need to think holistically about how ML systems fit into larger product ecosystems and user workflows. You will be successful in our team if you enjoy solving complex technical problems with a product approach, can communicate optimally with diverse partners, and thrive at finding the right balance between ML performance and product requirements. This role requires both technical depth and the ability to see the big picture of how ML brings value for users.
Minimum Qualifications
Bachelor's degree in Computer Science, Machine Learning, or a related technical field 2+ years of experience integrating ML capabilities into software products Strong programming skills in Python and experience with ML frameworks Experience with prototyping, measuring, and iterating on ML-powered features Understanding of ML evaluation metrics and how they translate to business metrics Knowledge of modern software development practices and tools Excellent communication skills with the ability to explain technical concepts to non-technical stakeholders
Preferred Qualifications
Experience with Large Language Models (LLMs) and understanding how to optimally integrate them into products Practical knowledge of Retrieval Augmented Generation (RAG) systems and their applications Experience crafting and implementing ML evaluation frameworks that connect to product success metrics Familiarity with A/B testing and experimental design for ML features Background in developing successful POC-to-production rollout strategies for ML features Experience collaborating with cross-functional teams including product management, design, and engineering Demonstrated ability to balance technical trade-offs with product requirements
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