Staff Solutions Architect, AI Sim (AI Engineering)

TL;DR

Staff Solutions Architect, AI Sim (AI Engineering): Accelerating customer adoption of AI Simulation (AISim) division's Large Quantitative Models (LQMs) by leading high-touch onboarding, establishing reference architectures, enabling secure integrations across cloud/LLM/partner environments, and driving product improvements. Focus on enterprise-grade deployment best practices across security, reliability, and cost/performance optimization.

Location: Remote (United States)

Salary: $116K – $222K (Tier 1: San Francisco, Los Angeles, Seattle, New York, Boston, and Washington, DC; Tier 2: all other Locations in the US)

Company

hirify.global is delivering AI solutions that address some of the world's greatest challenges.

What you will do

• Lead customer onboarding and implementation for AISim Physics and ML-based solutions.
• Serve as a trusted technical advisor to Product and Engineering teams.
• Build and maintain reference architectures, integration patterns, and implementation runbooks.
• Drive enterprise-grade deployment best practices across security, identity/access, networking, observability, reliability, and cost/performance optimization.
• Provide technical escalation support and root-cause analysis in partnership with Engineering.
• Contribute to field motions as needed (solution scoping, demos, proof-of-value plans, RFP/security responses).

Requirements

• 7+ years in Solutions Architecture, Customer Engineering, or Technical Consulting for enterprise SaaS, ML platforms, or data-intensive systems.
• Strong cloud architecture experience (AWS/Azure/GCP): IAM, networking, compute, storage, containers/Kubernetes, and observability.
• Working knowledge of ML and data pipelines (deployment patterns, evaluation/monitoring, governance basics).
• Proven ability to translate customer requirements into clear architectures, implementation plans, and technical deliverables.
• Excellent communication skills with both technical and executive stakeholders.

Nice to have

• Familiarity with LLM ecosystems (model APIs, RAG/agents, evaluation/monitoring) and partner integration workflows.
• Experience with product-led growth enablement (instrumentation, onboarding optimization, self-serve patterns).
• Background supporting regulated or security-sensitive enterprise deployments.
• Exposure to scientific computing, life sciences and/or materials sciences is a plus.

Culture & Benefits

• Career Defining Work: Accelerate the discovery of better drugs and materials.
• Comprehensive medical, dental, and vision coverage.
• Retirement savings with company matching.
• Fully remote.
• Flexible paid time off, company-wide seasonal breaks.
• Opportunities for continuous learning and growth.