Sr./Staff Forward Deployed Engineer

Groq•New York, NY

21d•$270,400 - $401,600

About The Position

As a Sr./Staff Forward Deployed Engineer focused on AI Infrastructure at Groq, you will take complex customer infrastructure programs from requirements to working production environments. The work spans next-generation NVIDIA GPU systems and Groq’s purpose-built inference platform, including GroqCloud, GroqMetal, GPU and LPX infrastructure, and the networking, storage, orchestration, observability, and workload layers around them. This is a hands-on individual-contributor role involving direct work in systems, coding and automation, troubleshooting across the stack, and translating ambiguous customer requirements into deployed and validated solutions. You will work directly with customers and partner with Commercial, Field Engineering, Platform and Cloud Engineering, Networking, Data Center Operations, Security, and Support teams. Field experience will inform reusable tooling, deployment patterns, reference architectures, and improvements to the Groq platform. The role is based in the Dallas, San Francisco, or New York City area. Remote work is available while Groq establishes its local office, with an expectation that the role will transition to onsite once the office opens.

Requirements

At least 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.
Technical depth in at least one area such as GPU or accelerator systems, networking, storage, orchestration or platform engineering, or infrastructure reliability, and sufficient breadth to troubleshoot 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 or programming skills for diagnostics, automation, deployment tooling, testing, or integrations.
A track record of personally debugging and delivering systems rather than working 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 gathering requirements from customer engineers, explaining technical tradeoffs clearly, and documenting work so others can reproduce it.
Comfort operating in a fast-moving environment where customer requirements, platform capabilities, and implementation details can evolve in parallel.
Candidates must qualify as U.S. Persons for export control purposes or otherwise be eligible for an applicable export license.

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 distributed AI workload data-access patterns.
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 proofs of concept, 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 proofs of concept, demonstrations, 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 sufficiently 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, explaining design choices, tradeoffs, performance results, and risks to 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 across organizational or technical boundaries, drive them to resolution, and coordinate internal experts while maintaining end-to-end ownership.
Embed with Platform, Cloud, Infrastructure, or Operations teams when priority customer deployments require concentrated engineering execution, automation, or integration work.
Build reusable tools, automation, reference architectures, test suites, deployment patterns, and documentation, and capture lessons learned so customer-specific engineering improves future deployments.
Bring structured customer feedback and field evidence to Product and Engineering, identify recurring gaps, and help turn one-off solutions into repeatable platform capabilities.