Sr./Staff Forward Deployed Engineer

Groq is hiring a Sr./Staff Forward Deployed Engineer focused on AI infrastructure. The role leads hands-on customer engagements from requirements and architecture through deployment, validation, and production readiness, while building reusable tools and bringing field feedback to engineering teams.

Responsibilities:

• 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 repeatable platform capabilities

Requirements:

• 4+ years of hands-on experience building, deploying, operating, or troubleshooting cloud infrastructure, AI infrastructure, HPC systems, large-scale platforms, or similarly demanding production environments
• Strong Linux and distributed-systems fundamentals, with practical experience in Kubernetes, Slurm, bare-metal environments, or comparable infrastructure platforms
• Meaningful technical depth in at least one area such as GPU or accelerator systems, networking, storage, orchestration/platform engineering, or infrastructure reliability, with enough breadth to troubleshoot across adjacent layers
• Working knowledge of AI training and inference workloads and how workload characteristics affect compute, networking, storage, scheduling, latency, and throughput
• Strong Python, Go, Bash, or equivalent scripting/programming skills for diagnostics, automation, deployment tooling, testing, or integrations
• A track record of personally debugging and delivering systems rather than operating only at the architecture, project-management, or escalation level
• Ability to break ambiguous problems into concrete technical actions and drive issues to resolution when responsibility spans multiple teams
• Strong written and verbal communication skills, including the ability to gather requirements from customer engineers, explain technical tradeoffs clearly, and document work so that others can reproduce it
• Comfortable operating in a fast-moving environment where customer requirements, platform capabilities, and implementation details can evolve in parallel
• Specifically, they must qualify as U.S. Persons for export control purposes (i.e., U.S. citizen, U.S. lawful permanent resident (Green Card holder), or a protected individual under 8 U.S.C. § 1324b(a)(3) such as a refugee or asylee), or otherwise be eligible for an applicable export license
• Experience at a neocloud, hyperscaler, AI infrastructure provider, HPC environment, frontier AI company, or other organization operating large-scale accelerator infrastructure
• Hands-on experience with NVIDIA GPU infrastructure and technologies such as CUDA, NCCL, NVLink/NVSwitch, DCGM, GPU Operator, InfiniBand, RoCE, Kubernetes, or Slurm
• Experience bringing up, qualifying, or operating multi-node GPU clusters, including health checks, burn-in or stress testing, collective-communication testing, performance benchmarking, and acceptance criteria
• Familiarity with high-performance storage systems such as VAST, Weka, Lustre, Ceph, or similar technologies and the data-access patterns of distributed AI workloads
• Experience with infrastructure automation and lifecycle tooling such as Terraform, Ansible, CI/CD, BMC/Redfish, PXE/iPXE, or related systems
• Prior solutions engineering, sales engineering, solutions architecture, or technical pre-sales experience in cloud, networking, security, AI infrastructure, or data center systems
• Customer-facing networking experience including private interconnects and peering, BGP and routing, load balancing, Kubernetes/Cilium network policy, and north-south and east-west traffic design
• Customer-facing security experience including access-control architecture, network isolation, enterprise security reviews, and SOC 2 / ISO 27001-style diligence or questionnaires
• Experience defining or executing technical PoCs, reference architectures, cluster acceptance tests, performance benchmarks, migration plans, or production-readiness criteria