Sr​/Forward Deployed Engineer

As a Sr./Staff Forward Deployed Engineer focused on AI Infrastructure at Groq, you will work at the frontier of large-scale AI systems, taking complex customer infrastructure programs from requirements to working production environments. You’ll help bring some of the newest accelerator and infrastructure technologies into production, spanning next-generation NVIDIA GPU systems alongside Groq’s purpose-built inference platform.

You will operate across Groq Cloud, Groq Metal (Groq’s infrastructure platform), GPU and LPX infrastructure, and the networking, storage, orchestration, observability, and workload layers around them. This is an opportunity to work on infrastructure where the playbooks are still being written: bringing up new systems, solving problems that emerge only at scale, and helping customers deploy demanding AI workloads on platforms at the leading edge of the market.

You will work directly with customers while partnering closely with Commercial, Field Engineering, Platform and Cloud Engineering, Networking, Data Center Operations, Security, and Support teams.

This role will be based in one of our three hiring hubs: the Dallas, San Francisco, or New York City area. The person hired for this role must be based in one of these three areas. You’ll have the flexibility to work remotely while we establish our local Groq office, with the expectation that this role will transition to onsite once the office opens.

• Own technical execution across complex customer engagements, from discovery and architecture through PoCs, demos, deployment, cluster bring-up, validation, acceptance, production readiness, and operational handoff.
• Translate incomplete or ambiguous customer requirements into practical architectures, implementation plans, test criteria, runbooks, and concrete engineering actions.
• Work hands-on across Linux, bare-metal infrastructure, Kubernetes and Slurm, networking, storage, observability, automation, and Groq platform integrations to bring customer environments online and resolve issues.
• Support large-scale GPU and LPX deployments, including infrastructure bring-up, cluster health and performance validation, workload testing, benchmarking, failure isolation, and production-readiness evidence.
• Understand customer AI workloads well enough to reason about training and inference behavior, concurrency, throughput, latency, data movement, caching, scheduling, and infrastructure bottlenecks.
• Lead technical portions of customer discovery, architecture reviews, demonstrations, and proofs of concept, clearly explaining design choices, tradeoffs, performance results, and risks to both engineering and business stakeholders.
• Partner with Networking and Security teams on customer requirements such as private connectivity and peering, routing, ingress and egress, load balancing, network policy, access controls, security architecture reviews, and enterprise security diligence.
• Troubleshoot production and pre-production issues that cross organizational or technical boundaries, drive them to resolution, and coordinate the right internal experts without losing end-to-end ownership.
• Embed with Platform, Cloud, Infrastructure, or Operations teams when priority customer deployments expose gaps that require concentrated engineering execution, automation, or integration work.
• Build reusable tools, automation, reference architectures, test suites, deployment patterns, documentation, and lessons learned so that customer-specific engineering makes the platform better for the next deployment.
• Bring structured customer feedback and field evidence back to Product and Engineering, identifying recurring gaps and helping turn one-off solutions into…