Sr./Staff Forward Deployed Engineer

Groq•New York, NY

1d•$270,400 - $401,600•Remote

About The Position

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 GroqCloud, GroqMetal (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 is a deeply hands-on individual-contributor role. You will work directly in systems, write code and automation, troubleshoot across layers of the stack, and turn ambiguous customer requirements into deployed and validated solutions. The work you do in the field will also shape what comes next: turning hard-won lessons into reusable tooling, deployment patterns, reference architectures, and improvements to the Groq platform for the customers that follow.

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.

Nice To Haves

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.

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.