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Forward Deployed Architect

Job in San Francisco, San Francisco County, California, 94199, USA
Listing for: Distyl
Full Time position
Listed on 2026-06-18
Job specializations:
  • Software Development
    AI Engineer (Applied/Software), Software Architect, Backend Developer, DevOps
Salary/Wage Range or Industry Benchmark: 200000 - 250000 USD Yearly USD 200000.00 250000.00 YEAR
Job Description & How to Apply Below

About Distyl AI

Distyl is an applied AI technology company partnering with the world’s most ambitious institutions to rearchitect critical operations for the frontier of AI. Our customers include the largest companies in telecom, healthcare, insurance, manufacturing, consumer goods, and global social organizations. We research and deploy technologies that power AI-native operations—both for our partners and for Distyl itself. Our work spans research into self-constructing systems, the development of the most reliable execution of AI systems, and products that transform mission‑critical workflows.

As a result, Distyl's technologies affect some of the world's largest operations—from hundreds of millions of consumer interactions to tens of millions of supply chain transactions and millions of patient journeys. Distyl is backed by leading investors including Lightspeed Venture Partners, Khosla Ventures, Coatue, DST Global, and the board‑members of 20+ F500s. The results reflect this approach: a 100% production deployment success rate for our customers and one of the few enterprise AI companies to run a profitable business.

What We Are Looking For

At Distyl, we build AI systems that operate inside the most complex enterprise environments in the world. Doing this well requires AI Architects who can take full ownership of how intelligent systems are structured, integrated, secured, and operated in production—while remaining deeply hands‑on in the work.

Forward Deployed AI Architects

Experienced builders with the technical judgment to design and evolve end‑to‑end AI systems across real enterprise stacks. They operate as trusted technical counterparts to customer engineering leadership, security teams, and operators, and are expected to make and defend architectural decisions that balance capability, risk, and operational reality. Their authority comes from execution, not title.

Key Responsibilities
  • Own the technical shape of AI systems deployed in customer environments
  • Define how models, agents, data pipelines, APIs, and orchestration layers fit together, how those systems integrate with existing enterprise infrastructure, and how they are operated safely and reliably ir focus is on cross‑cutting architectural patterns that span multiple customer systems, ensuring consistency, safety, and long‑term evolvability across deployments
  • Hands‑on throughout the lifecycle—building reference implementations, reviewing and evolving production systems, and stepping in directly on high‑risk or high‑impact technical problems
  • Accountable for architectural coherence across teams and over time, ensuring that systems remain composable, secure, observable, and adaptable as requirements evolve
  • Investigate how AI systems, legacy systems, and human operators interact in practice
  • Identify failure modes, risk surfaces, and integration bottlenecks, and establish guardrails and patterns that allow teams to move quickly without sacrificing safety, auditability, or correctness
  • Act as peers to senior technical leaders in customer engagements—whiteboarding architectures, pressure‑testing assumptions, and translating business objectives into concrete, executable system designs. They influence direction through clarity of reasoning and demonstrated delivery, not escalation
Qualifications
  • 5+ years of software engineering experience
  • Proven ownership of complex system architecture: you have designed, built, and operated complex distributed systems in production, and have taken responsibility for how those systems evolve over time. You are comfortable making irreversible or high‑impact architectural decisions and owning their consequences
  • Depth in AI systems and integration: you can reason fluently about how AI components—models, agents, retrieval, evaluation, orchestration—interact with data platforms, APIs, and existing services. You understand how design choices affect reliability, latency, security, and debuggability in real deployments
  • Strong security and risk intuition: you bring a security‑first mindset to system design, with practical experience around data access, identity, auditability, and compliance. You can articulate trade‑offs…
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