Senior Applied AI Solutions Engineer

The role sits at the intersection of deep ML engineering and product impact. You'll spend roughly half your time in the field — helping new customers move from POC to production, running technical onboarding, and working hands-on through their ML stack. The other half you'll spend building — prototyping applied AI use cases that show what's possible on the platform, going deep on emerging techniques before they're mainstream, and turning that expertise into concrete product direction. This is not a presales role. You get your hands dirty every day. Responsibilities include: Build prototypes and demos across the product portfolio — serverless inference, databases, MLflow, MLOps, and vertical use cases in Physical AI and HCLS — that become assets for sales, product, and engineering teams; Support new customers hands-on through POC design, technical onboarding, and validation; Go deep on emerging applied AI — new training techniques, inference optimizations, agentic architectures, new frameworks — and turn findings into working prototypes, writeups, and product recommendations; Feed the product roadmap with specific, grounded feedback; be the voice of "here's what broke in three customer POCs last month and here's what needs to change"; Develop reusable technical assets — notebooks, reference architectures, benchmark results — that reduce onboarding friction at scale. Requirements include: You have fine-tuned large models, debugged distributed training jobs, built production RAG or agentic pipelines, and optimized inference on GPU infrastructure; You are fluent in PyTorch, HuggingFace, CUDA fundamentals, Kubernetes for ML, MLflow or equivalent, vector databases; You have worked with enterprise ML teams; You read papers and implement them; You can explain activation checkpointing tradeoffs to an ML engineer and the cost implications to a CTO. Nice-to-have: vertical domain experience in Physical AI / robotics / simulation, HCLS, or enterprise AI application development; familiarity with MLOps at scale; prior cloud or AI infrastructure work; public notebooks or talks. What we offer: Competitive salary and comprehensive benefits; Flexible working arrangements; Opportunities for professional growth; An equal opportunity employer focused on diversity and inclusion.