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About This Role
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### About Nscale
Nscale is the GPU cloud engineered for AI. We provide cost\-effective, high\-performance infrastructure for AI start\-ups and large enterprise customers. Nscale enables AI\-focused companies to achieve superior results by reducing the complexity of AI development. Our GPU cloud bolsters technical capabilities and directly supports strategic business outcomes, including cost management, rapid innovation, and environmental responsibility.
We thrive on a culture of relentless innovation, ownership, and accountability, where every team member takes pride in their work and drives it with excellence and urgency. As an Nscaler, you’ll build trust through openness and transparency, where everyone is inspired to do their best work. If you join our team, you’ll be contributing to building the technology that powers the future.
### About The Role
The Network Operations and Engineering teams at Nscale operate some of the most demanding networking environments in the industry, supporting large\-scale AI GPU clusters where network performance directly impacts customer outcomes.
We’re looking for a Senior Principal Front End Network Engineer – AI Infrastructure to provide technical leadership across Nscale’s front\-end networking domain.
This role is focused on owning the reliability, scalability, and long\-term evolution of our high\-performance Ethernet front\-end networks. These networks support inference traffic, cluster management, data ingress/egress, shared storage connectivity, long\-haul circuits, and carrier handoffs. You will operate as a senior technical authority, influencing architecture, standards, and operational practices across teams while tackling the most complex front\-end network challenges in the platform.
### What You'll Be Doing
- Owning the technical direction and operational strategy for Nscale’s front\-end AI infrastructure networks at the highest level
- Designing, reviewing, and evolving large\-scale Ethernet leaf\-spine / Clos fabric architectures (including Arista and Nokia platforms) to support future growth, inference workloads, and storage requirements
- Acting as the senior\-most escalation point for the most complex front\-end network incidents, guiding deep technical investigations and systemic fixes
- Driving cross\-team and cross\-functional initiatives to improve fabric reliability, performance predictability, observability, and operational maturity at scale
- Defining standards for hardware configuration, routing, congestion management, firmware lifecycle management, automation, and change safety across Arista EOS and Nokia platforms
- Partnering with SRE, Compute Platform, Storage, and Network Architecture teams to influence end\-to\-end system design, including long\-haul/DCI circuits and storage network integration
- Mentoring senior and principal\-level network engineers, raising the bar for operational rigor and technical excellence across the organization
- Driving measurable improvements in uptime, latency consistency, capacity efficiency, and incident reduction for front\-end services
### About You (Skills / Qualifications)
- 12\+ years of experience in network engineering, with deep focus on hyperscale data centre, cloud, or AI infrastructure networking
- Expert\-level operational and architectural experience with large\-scale Ethernet data centre fabrics (leaf\-spine / Clos topologies)
- Strong hands\-on expertise with Arista (EOS / Etherlink) and/or Nokia (7220 IXR, 7250 IXR, 7750 SR series) platforms in production environments at scale
- Deep understanding of modern data centre routing and control planes (BGP, OSPF, ECMP, EVPN\-VXLAN)
- Proven experience with long\-haul circuits, DCI, and optical transport (dark fiber, carrier Ethernet, coherent optics, ZR/ZR\+)
- Strong background in storage networking over Ethernet and shared storage connectivity at hyperscale
- Demonstrated ability to debug and resolve complex cross\-layer issues spanning hardware, optics, routing, and application layers
- Proven ability to lead complex technical initiatives across teams and influence strategy without direct authority
- A systems\-level mindset, balancing performance, reliability, scalability, and operational cost at the highest level
### Nice to Have
- Extensive experience with Arista or Nokia platforms at hyperscale or large AI infrastructure scale
- Deep familiarity with front\-end network design patterns for massive AI clusters (inference, management, and storage tiers)
- Experience designing or operating large\-scale DCI / long\-haul optical or carrier networks
- Strong automation and tooling experience (Python, Ansible, validation frameworks, telemetry pipelines)
- Prior experience influencing platform or infrastructure strategy at significant scale
### What We Can Offer You
At Nscale, you'll find a collaborative, supportive, and innovative environment where your contributions spark real impact. We're building something extraordinary, and we want you at the core.
- Highly competitive package (base \+ equity) with reviews every 12 months.
- Join the fastest\-growing tech startup, your chance to push boundaries, collaborate with brilliant minds, and make your mark on cutting\-edge AI.
- Expect a dynamic progression plan tailored to your ambitions. Grow by trying new things, leading, challenging the status quo, and owning your impact, always with our full support.
- Human\-First Flexibility: We treat you as humans first. Our flexible workplace trusts Nscalers to deliver, giving you the autonomy to shape your day around life's moments.
Join our thriving remote\-first team. Geography is no barrier to impact or connection. We build seamless virtual collaboration, empowering you, wherever you work.
### Equal Opportunities Statement
We strongly encourage applications from people of colour, the LGBTQ\+ community, people with disabilities, neurodivergent people, parents, carers, and people from lower socio\-economic backgrounds.
If there’s anything we can do to accommodate your specific situation, please let us know.
The responsibilities outlined in this job description are not exhaustive and are intended to provide a general overview of the position. The employee may be required to perform additional duties, tasks, and responsibilities as assigned by management, consistent with the skills and qualifications required for the role.
The range below reflects the base salary for the position. Actual compensation may vary based on job\-related factors such as skill set, experience, education, and location. In addition to base salary, this role may be eligible for bonus, equity, and/or commission programs. Nscale may offer a competitive benefits package including medical, dental, vision, flexible paid time off, parental leave, and retirement plan participation.
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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At nSCALE, 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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
nSCALE AI Hiring
nSCALE has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span US, Seattle, WA, US. Compensation range: $185K - $185K.
Location Context
AI roles in Austin pay a median of $215,300 across 523 tracked positions. That's 8% 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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