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. What success looks like in 12 months The product and sales teams have a library of working, polished demos they reach for on calls Enterprise customers you’ve touched have meaningfully faster time-to-value than those you haven’t At least 2–3 product changes were shipped because of feedback you originated The team understands where applied AI is heading 6–12 months from now, partly because you told them Your responsibilities will include: Build prototypes and demos across the product portfolio—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 We expect you to have: You’ve fine-tuned large models, debugged distributed training jobs, built production RAG or agentic pipelines, and optimized inference on GPU infrastructure You’re fluent in the modern ML stack: PyTorch, HuggingFace, CUDA fundamentals, Kubernetes for ML, MLflow or equivalent, vector databases You’ve worked with enterprise ML teams—whether as a solutions engineer, customer engineer, or an ML engineer who collaborated closely with customers You read papers and implement them—not for credit, but because it’s how you stay sharp You communicate with calibration: you can explain activation checkpointing tradeoffs to an ML engineer in the morning and the cost implication to a CTO in the afternoon It will be an added bonus if you have: Experience in any of our vertical domains: Physical AI / robotics / simulation, HCLS (drug discovery, medical imaging, clinical NLP), or enterprise AI application development Familiarity with MLOps at scale (Kubeflow, Metaflow, Argo, Ray) Prior work at a cloud provider or AI infrastructure company You’ve shared technical work publicly—notebooks, talks, blog posts that people actually use Who thrives here You’ll thrive here if you’re energized by variety—one day deep in a customer’s MLOps stack, the next building a demo from scratch. You want your technical depth to influence product decisions, not just close deals. What we offer Competitive salary and comprehensive benefits package, Flexible working arrangements, International environment, Opportunity to work on impactful AI projects, and an equal opportunity employer statement.

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