ML Features Solutions Engineer

About the Role

We are seeking an ML Features Solutions Engineer to join our Product and Solution Engineering team, driving the development and optimization of core ML features for enterprise deployment. This role combines deep ML expertise with hands-on engineering, working at the intersection of ML research and product development to deliver production-grade capabilities to our customers.

This role is critical for accelerating ML feature development and bridging the gap between ML research and product engineering and will be driving the following:

• Core ML Feature Development: Drive improvements to ML features including model optimization, inference performance, and feature enhancements.
• Production-Ready Solutions: Build and deploy production-ready ML solutions for enterprise customers with focus on reliability and scale.
• Research to Product Bridge: Translate ML research innovations into practical product features and customer-facing capabilities.
• Cross-Team Collaboration: Work closely with SDK, testing, and customer teams to ensure ML features meet enterprise requirements.
• Impact: Accelerates ML feature development and optimization, enabling faster time-to-market for new capabilities while ensuring enterprise-grade quality and performance.

Responsibilities

• Design and implement core ML features including model optimization, quantization, and inference enhancements
• Optimize model performance for latency, throughput, and memory efficiency on SambaNova hardware
• Develop and improve features such as Function Calling, Structured Output, and JSON mode conformance
• Create end-to-end ML solutions that showcase platform capabilities and accelerate customer adoption
• Convert cutting-edge ML research into practical, deployable product features
• Establish benchmarks and quality standards for ML features in production environments
• Work with SDK team to ensure ML features are properly exposed and documented for developers
• Support enterprise customers implementing advanced ML features in their workflows
• Partner with ML research, platform engineering, and customer teams

Required Qualifications

• Master’s degree or higher in Computer Science, Machine Learning, Electrical Engineering, or related field
• 5+ years of industry experience in ML engineering or applied ML research
• 3+ years of hands-on experience with large language models and transformer architectures
• Expert proficiency in Python and deep learning frameworks: PyTorch (required), TensorFlow, or JAX
• Experience with model optimization techniques: quantization, pruning, distillation, efficient inference
• Strong understanding of LLM inference optimization: KV cache, batching strategies, memory management
• Experience deploying ML models to production at scale
• Track record of translating research concepts into production features

Preferred Qualifications

• PhD in Machine Learning, NLP, or related field
• Experience with custom hardware acceleration (TPUs, custom ASICs)
• Hands-on experience with inference frameworks: vLLM, TensorRT-LLM, or similar
• Experience with function calling and tool use in LLMs
• Knowledge of structured generation and constrained decoding
• Experience with ML feature development in enterprise contexts
• Contributions to open-source ML projects

What We Offer

• Work on cutting-edge ML features powering the fastest AI inference platform
• Direct impact on product capabilities used by enterprise customers globally
• Collaborate with world-class ML researchers and engineers
• Bay Area location enabling close collaboration with core ML teams
• Competitive compensation and benefits
• Opportunity to shape the future of enterprise AI