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About This Role
About Nscale
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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
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We’re hiring a Finance Manager, AI Infrastructure to help scale the financial backbone of our global AI compute platform.
Reporting to the VP of Finance, this role sits within Finance Business Partnership and partners closely with engineering, infrastructure, operations, and procurement leadership. You’ll own key areas of financial planning, cost governance, and performance analysis across high\-growth AI infrastructure investments, building the models and reporting needed to support a complex, capital\-intensive environment.
This role is critical to how Nscale makes smart infrastructure decisions as we grow. You’ll help drive investment decisions, improve visibility into core cost drivers such as utilization, power, and capacity efficiency, and turn technical and operational complexity into clear financial insight that informs long\-term capacity planning, vendor choices, and infrastructure strategy.
What you'll be doing
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Financial Planning \& Analysis
- Support budgeting, forecasting, and monthly reporting for AI infrastructure, including GPUs, cloud, and related platforms
- Build and maintain financial models covering capacity, utilization, and cost trends
- Analyze variances across compute, cloud, and network spend and highlight key drivers and risks
- Track and report on core performance metrics such as cost per GPU hour and utilization
Cost Analysis \& Optimization
- Develop unit economics for AI workloads across training and inference
- Identify cost\-efficiency opportunities across cloud usage, hardware, and vendor spend
- Partner with engineering teams to understand cost drivers and usage patterns
- Contribute analysis that supports pricing and margin improvement initiatives
Investment \& Capacity Decision Support
- Build financial models to support infrastructure investment decisions
- Evaluate trade\-offs across capacity expansion and deployment strategies
- Track actual performance against investment assumptions and surface variances
- Support vendor analysis and contract evaluations through financial modeling
Cross\-Functional Partnership
- Partner with engineering, infrastructure, and procurement teams to support financial decision\-making
- Translate technical concepts into clear, actionable financial insights
- Engage technical stakeholders to align financial analysis with operational realities
Reporting, Controls \& Governance
- Produce regular reporting on infrastructure spend and performance
- Help establish cost\-tracking and governance processes
- Ensure accuracy and consistency of financial data across systems and reports
About You
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- 5–8 years of experience in FP\&A, strategic finance, or infrastructure / cloud finance roles at a high\-growth or top\-tier technology company
- Advanced financial modeling skills, including scenario analysis, sensitivity modeling, and long\-range planning
- Experience owning or materially contributing to forecasting and planning in a complex, usage\-based or infrastructure\-heavy environment
- Strong understanding of cost drivers in cloud, compute, or infrastructure systems, including utilization, capacity, and unit cost dynamics
- Demonstrated ability to partner effectively with engineering or technical teams and influence decision\-making
- Track record of driving insights from large, ambiguous datasets and turning them into clear recommendations
- High proficiency in Excel / Google Sheets, with experience using SQL and data tools such as Looker or Tableau
- Experience in AI, GPU cloud, or distributed infrastructure environments is a plus
- Familiarity with unit economics, margin modeling, capital allocation, investment analysis, or capacity planning is advantageous
- High ownership, strong analytical rigor, comfort with ambiguity, and the ability to challenge assumptions with data
What we can offer you
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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 US compensation package (base \+ bonus \+ equity), with performance reviews every 12 months.
- Join one of the fastest\-growing AI infrastructure companies — your chance to directly shape how global AI capacity is planned and deployed. ✨
- Expect a dynamic progression plan tailored to your ambitions. Grow by leading critical cross\-functional initiatives and shaping capital strategy — 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.
Equal Opportunities Statement
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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.
Salary Range
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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.
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.
Salary Range
$110,000 \- $185,000 USD
Salary Context
This $110K-$185K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 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. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($147K) sits 19% below the category median. Disclosed range: $110K to $185K.
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 Seattle pay a median of $227,400 across 1,084 tracked positions. That's 14% 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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