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About This Role
About Nebius:
Nebius is leading a new era in cloud infrastructure for the global AI economy. We are building a full\-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment, without the cost and complexity of building large in\-house AI/ML infrastructure.
Built by engineers, for engineers. From large\-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI.
Listed on Nasdaq (NBIS) and headquartered in Amsterdam, we have a global footprint with R\&D hubs across Europe, the UK, North America and Israel. Our team of 1,500\+ includes hundreds of engineers with deep expertise across hardware, software and AI R\&D.
### About Tavily
We're building the infrastructure layer for agentic web interaction at scale. Our API is designed from the ground up to power Retrieval\-Augmented Generation (RAG) and real\-time reasoning in AI systems. By connecting LLMs to high\-quality, trustworthy web content, we help developers build agents that are not only intelligent — but also informed.
We work with some of the most innovative teams in AI — from small startups shaping the ecosystem to the largest enterprises deploying AI at scale. Whether it's powering sales assistants, research copilots, or internal knowledge tools, we're the missing link between LLMs and the real world.
The Role
We are looking for a Senior Software Engineer to design and build the billing platform behind a novel search engine tailored for agentic AI consumption. This role is based in our New York office, hybrid with the NYC team.
Our APIs are used by millions of developers and the AI agents acting on their behalf, all paying based on what they consume. You will own the systems that turn product usage into accurate, trustworthy revenue, supporting flexible billing models—usage\-based pricing, subscriptions, prepaid balances with auto top\-up, and enterprise contracts. Correctness is non\-negotiable: every event must be measured precisely and every invoice must be right to the cent.
In this position, your responsibility will be to:
- Design and operate the billing platform end to end, from usage events to invoices
- Build metering and usage\-aggregation pipelines that turn high\-volume events into billable amounts
- Design and operate prepaid credit/wallet systems with auto top\-up and balance management
- Support enterprise/contract billing with custom pricing, negotiated terms, and scheduled invoicing
- Build reconciliation, idempotency, and invoice\-accuracy safeguards to ensure revenue data is always correct
- Lead the migration to the next generation of our billing system with no revenue loss or downtime
- Define observability and quality metrics for billing correctness, freshness, and throughput
- Ensure billing systems meet audit and compliance requirements (SOX, financial audit)
What we're looking for:
- 6\+ years building production backend systems, of which 3\+ years actively building and operating billing or metering systems in production
- Hands\-on experience with usage\-based / metered billing: rating, aggregation, and proration logic
- Hands\-on experience with subscription / recurring billing alongside usage\-based models
- Hands\-on experience with prepaid credits / wallet systems and auto top\-up mechanics
- Hands\-on experience with enterprise / contract billing: custom pricing and negotiated terms
- End\-to\-end integration of a third\-party billing platform (Stripe Billing, Metronome, Orb, or Zuora)
- Strong expertise in Python and TypeScript/Node (Go a plus)
- Production experience with transactional databases (Postgres or equivalent) for financial\-grade data
- Built high\-volume event / usage pipelines into a data warehouse (Kafka or Kinesis Snowflake or BigQuery)
- Owned billing correctness in production: reconciliation, idempotency, invoice accuracy
- Product\-Led Growth (PLG) experience: worked on billing for high\-volume, self\-serve users
- Hands\-on with Auth0, Okta, or similar identity systems in production
- Shipped revenue systems under SOX or financial audit constraints
Nice to have:
- Led a billing system migration or re\-platform with zero revenue loss or downtime
- Built hybrid billing combining usage, subscriptions, and prepaid in one system
- Worked across both a payments layer (Stripe) and a metering / rating layer (Metronome / Orb)
- Billed a developer\-facing / API product with pay\-as\-you\-go and API\-key metering
- Used Snowflake for usage aggregation and revenue analytics
Why Tavily?
- Full ownership — small team, you own the entire infrastructure, not a slice of it
- Real scaling challenges — bursty scraping workloads, cache invalidation, multi\-region, millions of daily requests
- AI\-native company — your infra directly powers AI agents used by leading companies in the space.
Key employee benefits in the US:
- Health insurance: 100% company\-paid medical, dental, and vision coverage for employees and families.
- 401(k) plan: Up to 4% company match with immediate vesting.
- Parental leave: 20 weeks paid for primary caregivers, 12 weeks for secondary caregivers.
- Remote work reimbursement: Up to $85/month for mobile and internet.
- Disability \& life insurance: Company\-paid short\-term, long\-term and life insurance coverage.
Benefits \& Perks:
- Competitive compensation
- Career growth and learning opportunities
- Flexibility and ownership
- Collaborative and innovative culture
- Opportunity to work on impactful AI projects
- International environment and talented teams
What's it like to work at Nebius:
Fast moving \- Bold thinking \- Constant growth \- Meaningful impact \- Trust and real ownership \- Opportunity to shape the future of AI
Equal Opportunity Statement:
Nebius is an equal opportunity employer. We are committed to fostering an inclusive and diverse workplace and to providing equal employment opportunities in all aspects of employment. We do not discriminate on the basis of race, color, religion, sex (including pregnancy), national origin, ancestry, age, disability, genetic information, marital status, veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by applicable law.
Applicants must be authorized to work in the country in which they apply and will be required to provide proof of employment eligibility as a condition of hire.
If you need accommodations during the application process, please let us know.
Salary Context
This $147K-$224K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Nebius, 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($185K) sits 15% below the category median. Disclosed range: $147K to $224K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Nebius AI Hiring
Nebius has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $224K - $224K.
Location Context
AI roles in New York pay a median of $220,000 across 1,045 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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