About the job
Our team bridges the gap between machine learning capabilities and user-facing products. We transform advanced ML technologies into valuable features that tackle real customer problems. We're looking for a skilled Machine Learning Solutions Engineer who can work at the intersection of ML research, engineering, and product development to build production-ready AI systems that deliver measurable business value. If you're passionate about making ML models work in real-world applications and can collaborate optimally across technical and non-technical teams, we'd love to talk with you!
Responsibilities
Build proof-of-concepts that demonstrate ML capabilities in practical contexts
develop strategies for measuring product value
design effective evaluation frameworks
help build flawless moves between different ML models as technologies evolve
Qualifications
Minimum
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
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