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ML Features Solutions Engineer
Multiple locations on-site full time senior Mar 4, 2026
About this role
The era of pervasive AI has arrived. In this era, organizations will use generative AI to unlock hidden value in their data, accelerate processes, reduce costs, drive efficiency and innovation to fundamentally transform their businesses and operations at scale.
SambaNova Suite™ is the first full-stack, generative AI platform, from chip to model, optimized for enterprise and government organizations. Powered by the intelligent SN40L chip, the SambaNova Suite is a fully integrated platform, delivered on-premises or in the cloud, combined with state-of-the-art open-source models that can be easily and securely fine-tuned using customer data for greater accuracy. Once adapted with customer data, customers retain model ownership in perpetuity, so they can turn generative AI into one of their most valuable assets.
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
Submission Guidelines
Please note that in order to be considered an applicant for any position at SambaNova Systems, you must submit an application form for each position for which you believe you are qualified.
EEO Policy
SambaNova Systems is an Equal Opportunity/Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard basis of age (40 and over), color, disability, gender identity, genetic information, marital status, military or veteran status, national origin/ancestry, race, religion, creed, sex (including pregnancy, childbirth, breastfeeding), sexual orientation, and any other applicable status protected by federal, state, or local laws.
Benefits Summary for US-Based, Full-Time Employment Positions
SambaNova offers a competitive total rewards package, including the base salary, plus equity and benefits. We cover 95% premium coverage for employee medical insurance, and 77% premium coverage for dependents and offer a Health Savings Account (HSA) with employer contribution. We also offer Dental, Vision, Short/Long term Disability, Basic Life, Voluntary Life, and AD&D insurance plans in addition to Flexible Spending Account (FSA) options like Health Care, Limited Purpose, and Dependent Care. Our library of well-being benefits available to you and your dependents includes a full subscription to Headspace, Gympass+ membership with access to physical gyms, One Medical membership, counseling services with an Employee Assistance Program, and much more.
Offices: Austin, Texas, United States (Austin, TX); Palo Alto, California, United States (Palo Alto, CA);