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About Nscale
Nscale is taking on the hyperscalers by building a vertically integrated GenAI cloud platform. We own the data centers, software, and applications that power today's AI stack using sustainable technology solutions. 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 a Nscaler, you'll build trust through openness and transparency, where everyone is inspired to do their best work. Collaboration is key, and we work together swiftly and respectfully, embracing adaptability and resilience in all we do.
About the Role
Nscale is looking for a Principal AI Engineer (Specialised) to lead the inference and post-training pillar of our AI systems engineering organization. You'll define the multi-year technical roadmap for how models are served, evaluated, and post-trained on Nscale's GPU cloud, across dedicated and serverless inference and bring-your-own-model deployments. You'll lead the most consequential architectural programmes in that space and set the engineering standards that 20–50+ engineers build to.
As a Principal engineer, you are one of the deepest technical authorities in the company on AI systems. Your decisions set the cost, latency, and reliability at which Nscale serves tokens and runs post-training workloads, and those numbers have to compete with the world's leading AI infrastructure providers. The problems span the full stack: kernel efficiency on state-of-the-art GPU systems, fleet-level KV cache and serving architecture, the evals that prove model quality, and RL loops where inference and training share hardware. You frame the solutions the organization executes against, including the API contracts customers see.
How We Work
- Dog years. We move quickly and compress a lot of learning into a short time.
- Don't let perfect be the enemy of good. Ship, measure, iterate.
- Be relentless. Own the problem end to end and see it through.
- One team, one mission. Outcomes over process, and no "not my job".
Responsibilities
- Define and own the multi-year technical roadmap for Nscale's inference, evals, and post-training platform, and translate it into architecture that multiple teams can execute against
- Lead company-scale architectural initiatives in the pillar, such as next-generation serving (disaggregated prefill/decode, KV cache orchestration across GPU, host, and storage tiers, speculative decoding, multi-tenant scheduling), GPU kernel and model efficiency work (custom kernels, FP8/NVFP4/INT8/4 quantization, sparsity, distillation, MoE serving), evals and benchmarking frameworks, and post-training and RL infrastructure
- Establish engineering standards adopted across all AI teams: API design and compatibility guarantees, benchmarking and evals methodology, training stability norms, and performance testing practices
- Own the framework by which cost, latency, throughput, and model quality trade-offs are made and measured across the pillar
- Identify long-horizon systemic risks early (serving engine and framework bets, accelerator support, capability gaps) and resolve them before they block the organization
- Align AI engineering, research, product, and infrastructure leadership on multi-team technical strategy; frame technical trade-offs in product and commercial terms
- Mentor and develop Staff and Senior AI Engineers, and grow the next generation of inference technical leaders at Nscale
- Represent Nscale's technical approach externally: open-source leadership in the frameworks we depend on, publications, conference talks, and partnerships with GPU vendors and AI labs
Requirements
- 10–15 years of engineering experience, with a clear track record of pillar-level impact on production AI systems
- 4+ years of hands-on work with LLMs in inference, GPU performance, evals, or post-training and RL, in production or research
- Demonstrated ability to define multi-year technical strategy for complex, multi-team AI systems organizations
- World-class depth in production LLM inference, GPU performance, evals, and/or post-training and RL infrastructure, with strong working knowledge across the rest
- Demonstrated ownership of the architecture of a large-scale production inference or training platform
- Proven ability to create architectural frameworks and engineering standards adopted across large engineering organizations
- Deep understanding of the hardware/software boundary for AI accelerators: CUDA or ROCm, memory bandwidth and interconnect constraints, and distributed compute paradigms
- Strong history of growing technical leaders (Staff and above) and multiplying technical capability across teams
- External recognition in the AI systems community through research, open source, or industry contribution
Preferred
- Prior experience at a top-tier AI lab or major hyperscaler AI infrastructure team
- Maintainer or core contributor to a foundational inference, kernel, or RL framework (vLLM, SGLang, TensorRT-LLM, LMCache, FlashInfer, Triton, verl, OpenRLHF, TRL, DeepSpeed, Megatron-LM, etc.)
- Hands-on depth in RL for LLMs (DPO/GRPO-style methods, reward modelling, multi-turn and tool-use RL) and the interaction between inference and training infrastructure
- Experience defining developer API platforms adopted at scale by external developers
- Deep experience with control plane / data plane architecture and cell-based deployment patterns in large-scale inference infrastructure
- Published work in AI systems: MLSys, NeurIPS Systems Track, OSDI, EuroSys, SC, or equivalent
- Experience with hardware-software co-design: custom accelerator kernels (CUDA, Triton), compiler-level optimization, AI hardware roadmap engagement
- Experience defining pricing, SLO, and capacity models for a commercial inference product
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Salary Context
This $290K-$520K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 in Demand for This Role
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 $212,000 based on 11,857 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($405K) sits 91% above the category median. Disclosed range: $290K to $520K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
nSCALE AI Hiring
nSCALE has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $293K - $520K.
Location Context
AI roles in New York pay a median of $221,000 across 2,878 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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