Senior Applied AI Solutions Engineer

Role

Senior Applied AI Solutions Engineer responsible for closing the gap between documentation and real-world ML workloads by prototyping capabilities, accelerating customers through their first 90 days, and translating field insights into product direction. The role combines hands-on customer-facing work (POC design, technical onboarding, validation) with internal prototyping and research on applied AI techniques.

Key Responsibilities

• Build prototypes and polished demos across the product portfolio (serverless inference, databases, MLflow, MLOps, Physical AI, HCLS) for use by sales, product, and engineering teams
• Support customers hands-on through POC design, technical onboarding, and validation; act as the bridge between customer ML teams and the platform
• Research and implement emerging applied AI techniques (training methods, inference optimizations, agentic architectures) and convert findings into working prototypes, writeups, and product recommendations
• Provide specific, grounded feedback to the product roadmap based on recurring issues observed in customer POCs
• Develop reusable technical assets such as notebooks, reference architectures, and benchmark results to reduce onboarding friction at scale

Requirements

• Proven experience fine-tuning large models, debugging distributed training jobs, building production RAG or agentic pipelines, and optimizing inference on GPU infrastructure
• Fluency in the modern ML stack (explicitly mentions PyTorch, HuggingFace, CUDA fundamentals, Kubernetes for ML, MLflow or equivalent, vector databases)
• Experience working with enterprise ML teams in solution/customer engineering or ML engineering roles that collaborate closely with customers
• Active practice of reading and implementing research papers
• Strong ability to communicate technical tradeoffs to both engineers and senior stakeholders

Preferred / Nice-to-have

• Domain experience in Physical AI / robotics / simulation, HCLS (drug discovery, medical imaging, clinical NLP), or enterprise AI application development
• Familiarity with MLOps frameworks at scale (Kubeflow, Metaflow, Argo, Ray)
• Prior experience at a cloud provider or AI infrastructure company
• Publicly shared technical work (notebooks, talks, blog posts)

Success Metrics (12 months)

• A library of working, polished demos adopted by product and sales
• Enterprise customers supported have meaningfully faster time-to-value
• At least 2–3 product changes shipped based on feedback originated by this role
• Team has clearer visibility on where applied AI is heading 6–12 months out

Compensation & Benefits (summary)

• Base compensation range listed: $200—$350,000 USD
• Competitive salary and comprehensive benefits package
• Flexible working arrangements and opportunities for professional growth
• Collaborative, international environment