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About This Role
New York City Area Network Eng In office Full-time About Groq Inference is the engine that powers AI, and Groq was built from the silicon up to deliver the world's fastest inference at scale. We pioneered the LPU—the first processor designed specifically for AI inference—and are transforming that innovation into a global cloud platform powering production AI workloads.
With the capital, infrastructure, and team to execute, we're uniquely positioned to define the next era of AI infrastructure. The opportunity is massive, and it's still wide open. Now let's go build it!
Mission:
Groq is building high-performance AI infrastructure designed to make inference fast, predictable, and scalable. Our infrastructure teams design and operate the systems that connect compute, data, and services at significant scale. We are looking for a Sr./Staff Network Engineer, AI/HPC to help architect, design, validate, and evolve the network infrastructure supporting Groq's rapidly growing AI compute environments.
As a Sr./Staff Network Design Engineer, you will own network architecture and design across high-performance data center and AI infrastructure environments. You will translate capacity, performance, reliability, and business requirements into scalable network designs that can be deployed and operated consistently.
This role requires deep expertise in data center networking, routing protocols, network architecture, automation, and large-scale infrastructure. You will work closely with network operations, data center engineering, compute, platform, security, and infrastructure teams to take designs from concept through validation and production deployment. You will also help establish the engineering standards, design patterns, tooling, and technical direction that allow Groq's network infrastructure to scale.
In this role, success means building network architectures that allow Groq's infrastructure to grow without sacrificing performance, reliability, or operational simplicity. You will create repeatable designs rather than one-off solutions, automate wherever practical, identify scaling limits before they become production problems, and make the network an enabling layer for Groq's compute infrastructure.
You'll combine deep networking expertise with a systems engineering mindset—considering not only whether a network works, but how it behaves at scale, how it fails, how it is validated, and how efficiently it can be operated.
Location: 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.
Responsibilities & opportunities in this role:
- Own the end-to-end network design process, translating customer compute, workload, scale, and tenancy requirements into production-ready” architectures for AI training and inference.
- Work closely with software teams building inference and training stacks to align network topology with stack architecture.
- Develop network architectures, high-level designs (HLDs), low-level designs (LLDs), review bill of materials (BOMs), and implementation standards leveraging industry and vendor reference architectures.
- Design spine-leaf/Clos fabrics across front-end, back-end, and storage networks, with a focus on performance, resiliency, scalability, and operational simplicity.
- Evaluate and implement technologies such as BGP, EVPN, VXLAN, ECMP, IPv4/IPv6, QoS.
- Develop strategies for data center interconnect, WAN connectivity, internet edge, cloud connectivity, and external peering.
- Perform network capacity planning and traffic engineering for high-bandwidth AI and distributed compute workloads.
- Partner with compute and infrastructure engineering teams to understand workload communication patterns and translate them into network requirements.
- Establish standards for network hardware platforms, topology, addressing, routing policy, configuration, and lifecycle management.
- Evaluate switches, optics, cabling architectures, network operating systems, and emerging networking technologies.
- Build lab environments, proofs of concept, and design-validation frameworks before introducing new architectures into production.
- Define failure scenarios and validate network behavior during link, device, control-plane, and site failures.
- Drive network automation and infrastructure-as-code practices using technologies such as Python, Ansible, Git, APIs, and CI/CD pipelines.
- Develop automated design validation, configuration generation, compliance, and testing capabilities.
- Establish network observability requirements, including telemetry, performance metrics, alerting, and capacity visibility.
- Lead technical reviews and provide guidance on complex network architecture and design decisions.
- Troubleshoot complex performance and reliability issues that span network, compute, hardware, and distributed systems.
- Partner with operations teams to ensure designs are supportable, observable, and safe to operate at scale.
- Mentor engineers and raise the technical bar for network architecture, automation, documentation, and engineering practices.
Ideal candidates have/are:
- 8+ years of experience in network engineering, architecture, or infrastructure engineering, with significant experience designing large-scale data center networks.
- Deep understanding of TCP/IP, BGP and modern data center routing architectures.
- Strong experience designing leaf-spine / Clos network fabrics.
- Experience with EVPN/VXLAN and modern network virtualization architectures.
- Strong understanding of ECMP, routing convergence, failure domains, traffic engineering, and network resiliency.
- Experience designing networks with high-bandwidth Ethernet platforms, including 100G, 400G, or higher-speed interfaces.
- Strong knowledge of optical networking fundamentals, transceivers, fiber infrastructure, and data center cabling.
- Experience with network platforms and operating systems from vendors such as Arista, Cisco, Juniper, NVIDIA, or equivalent.
- Experience with network automation and software development using Python and APIs.
- Familiarity with infrastructure-as-code, source control, automated testing, and CI/CD engineering practices.
- Experience with network telemetry and observability technologies such as streaming telemetry, SNMP, flow data, and time-series monitoring.
- Strong understanding of Linux networking and TCP/IP fundamentals.
- Demonstrated ability to develop architecture documents, design standards, implementation plans, and technical specifications.
- Ability to analyze complex systems and make sound architectural decisions involving performance, reliability, cost, and operational tradeoffs.
- Strong communication skills and the ability to influence technical decisions across engineering organizations.
- Experience designing networks for AI/ML clusters, HPC environments, distributed computing systems, or large GPU/accelerator deployments.
- Experience with very large-scale Ethernet fabrics and high-throughput east-west traffic patterns.
- Knowledge of RDMA, Priority Flow Control (PFC), ECN, congestion management, and lossless or near-lossless Ethernet design.
- Familiarity with backend technologies like Infiniband/RoCE/ Spectrum-X experience strongly preferred
- Experience designing networks supporting distributed training or large-scale AI inference.
- Familiarity with SONiC or other disaggregated/open networking platforms.
- Experience with multi-site data center architecture and data center interconnect.
- Experience working with cloud networking environments such as AWS, GCP, or Azure.
- Familiarity with network source-of-truth and automation platforms such as NetBox, Nautobot, or equivalent systems.
- Experience designing and operating infrastructure at hyperscale or in rapidly growing technology environments.
Why Join Us:
- Purposeful Hiring: You’re not here by accident, and neither is anyone else. Every teammate is handpicked with intention because who we build with matters.
- Builders Wanted: You’re not just riding the rocket ship, you’re building it. Your work directly shapes the trajectory of our company.
- Mission-Driven Work: We’re here to make a real impact. Our mission fuels everything we do.
- Tackling Hard Problems: If easy isn’t your thing, you’re in the right place. We solve some of the most complex and exciting challenges in our space.
- Excellence Is The Standard: High performance isn’t just encouraged, it’s the baseline. And it’s contagious.
Compensation Groq is committed to providing competitive compensation through our Total Cash philosophy, which incorporates potential bonus value directly into base pay. The total cash salary range for this position, which is inclusive of the potential bonus value, is $270,400-$401,600, with individual placement determined by your geographic location, experience, skills, and alignment with internal compensation standards. This range is specific to candidates located in the United States. Compensation for international candidates will vary based on local market dynamics. Beyond cash compensation, Groq also offers a Long-Term Incentive (LTI) Program and a robust suite of employee benefits. US Job Posting This position may require access to technology and/or information subject to U.S. export control laws and regulations, including the Export Administration Regulations (EAR). To comply with these requirements, candidates for this role must meet certain citizenship or residency criteria. 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.
Non-US Job Postings This position may require access to technology and/or information subject to U.S. export control laws and regulations, as well as applicable local laws and regulations, including the Export Administration Regulations (EAR). To comply with these requirements, candidates for this role must meet all relevant export control eligibility criteria.
Groq is an Equal Opportunity Employer. We are committed to creating an inclusive environment for all employees and applicants. All qualified applicants will receive consideration for employment without regard to race, color, religion, national origin, sex (including gender identity, sexual orientation, and pregnancy), age, disability, genetic information, protected veteran status, or any other characteristic protected by applicable law.
Groq complies with all applicable federal, state, and local laws governing nondiscrimination in employment. We do not tolerate discrimination or harassment based on any protected characteristic.
Groq is committed to working with and providing reasonable accommodations to qualified individuals with physical or mental disabilities. If you require a reasonable accommodation to complete an application or to participate in the hiring process, please contact us at [email protected]. This contact is for accommodation requests only, which will be considered on a case-by-case basis.
All offers of employment are contingent upon verification of the applicant’s identity and employment authorization in accordance with federal law.
Groq encourages people with criminal record histories to apply for employment, and values diverse experiences, including prior contact with the criminal legal system. To that end, Groq welcomes such applicants in accordance with the California Fair Chance Act, Los Angeles City Fair Chance Act Ordinance, Los Angeles County Fair Chance Act Ordinance, and San Francisco Fair Chance Act Ordinance. Philadelphia applicants can review information pertaining to Philadelphia’s Fair Criminal Record Screening Standards Ordinance here: https://www.phila.gov/documents/fair-chance-hiring-law-poster.
As part of our hiring process, Groq may use artificial intelligence (“AI”) tools or automated systems to assist with activities such as reviewing applications, evaluating qualifications, scheduling interviews, analyzing assessment responses, or supporting recruiting operations. These tools are designed to assist—not replace—human decision-making, and hiring decisions are subject to human review. We may process information you provide during the application process, including resumes, application materials, interview responses, assessments, and, where applicable, audio, video, or transcript data. If legally required, we will request consent before using technologies that analyze biometric or video interview data. Candidates may request reasonable accommodations, an alternative evaluation process, additional information regarding the use of AI in the hiring process, or review of certain automated decisions by contacting [email protected].
Salary Context
This $270K-$401K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $178K across 1623 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Groq, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($336K) sits 59% above the category median. Disclosed range: $270K to $401K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Groq AI Hiring
Groq has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $401K - $401K.
Location Context
AI roles in New York pay a median of $221,000 across 3,078 tracked positions. That's 4% above the national median.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (4,384) and senior (3,343) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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