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About This Role
About Nscale
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Nscale is taking on the hyperscalers by building a vertically integrated GenAI cloud platform. We own the data centres, 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
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Nscale is looking for a Senior AI Product Engineer to join our product engineering team. You'll lead technical design and delivery of major features and subsystems — acting as the technical anchor for your team while raising the quality bar across everything the team ships.
At this level, you own substantial portions of Nscale's AI services platform: the API gateway, core AI service capabilities, billing and usage infrastructure, and the developer\-facing SDKs and tooling that customers use to build on Nscale. You work on complex, multi\-sprint projects and drive them to completion while mentoring the engineers around you.
This role expands beyond delivery into something broader: you become a multiplier for the people around you. That means growing the engineers you work with, shaping how the team approaches hard problems, and taking greater ownership of the technical direction of what you build.
Responsibilities
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- Lead technical design and end\-to\-end delivery for major product subsystems and complex features
- Own architectural decisions within your team's domain — from API contracts to deployment strategy
- Set the quality standard: drive test coverage, observability, reliability, and incident response
- Mentor more junior engineers through design reviews, code reviews, and regular pairing
- Collaborate cross\-functionally with AI platform, infrastructure, and product teams
- Identify and proactively address technical debt and scalability constraints before they become blockers
- Contribute to hiring by reviewing candidate submissions and participating in interviews
- Produce clear design documentation that enables asynchronous decision\-making across the team
Requirements
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- 5–8 years of software engineering experience
- Deep expertise in backend or full\-stack development at scale (Python, TypeScript, Go, or similar)
- Proven experience designing and operating API platforms or developer\-facing cloud services
- Strong track record of owning and delivering complex, multi\-sprint projects end\-to\-end
- Experience with cloud\-native architecture: Kubernetes, distributed systems, observability stacks
- Experience building services with clear control plane / data plane separation; familiarity with cell\-based or ring\-based architecture patterns for cloud service scalability and fault isolation
- Experience with declarative, reconciliation\-based provisioning: desired\-state controllers or workflows that are idempotent, converge after partial failure, and detect drift in customer\-facing resources
- Treats customer\-facing configuration surfaces — input schemas, defaults, and deployment values — as versioned API contracts, with validation, documentation, and compatibility handled deliberately
- Experience making long\-running provision and teardown flows supportable: readiness signals, structured failure reporting at the boundary that owns the decision, and clear remediation paths for operators
- Hands\-on production ownership: on\-call rotation, incident response, and debugging live systems under real traffic
- Experience monitoring and tracking service stability over time — SLOs and error budgets, alerting tied to customer impact, and detecting reliability regressions before customers report them
- Experience instrumenting usage and cost: metering consumption, attributing spend to tenants or workloads, and keeping unit costs visible to the team
- Ability to communicate technical decisions clearly to product managers and engineering stakeholders
- Sound engineering judgment: knows when to simplify, when to invest, and when to defer
Preferred
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- Experience building or consuming SDKs, Terraform providers, or extensibility layers for cloud platforms
- Familiarity with infrastructure\-as\-code tooling (Terraform, Pulumi) and how platform teams expose it to customers
- Experience building AI\-integrated product features (LLM inference APIs, fine\-tuning UX, agentic workflows)
- Experience modelling a deployable unit as a contract over its packaging artifacts — typed inputs, defaults, readiness checks, and published outputs that other services consume
- Working knowledge of AI/ML infrastructure concepts: serving, evaluation, and model lifecycle management
*For information on how Nscale handles candidate personal data, please see our Employee \& Candidate Privacy Notice:* *Here.*
Salary Context
This $180K-$260K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. Disclosed range: $180K to $260K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
nSCALE AI Hiring
nSCALE has 4 open AI roles right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $260K - $650K.
Location Context
AI roles in New York pay a median of $220,000 across 1,650 tracked positions.
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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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