Interested in this AI/ML Engineer role at Neuberger Berman?
Apply Now →Skills & Technologies
About This Role
Business Overview:
Neuberger is building a high\-performance Secondary Data Strategy capability to strengthen data quality, analytical insight, and scalable reporting across its private markets secondaries platform. The team operates at the intersection of investment data, technology, and business process design, partnering across investment, product, and operational stakeholders to improve how data is captured, structured, and activated.
Position Overview:
The Analytics, Data Products \& AI Lead is a senior onshore hire for the Secondary Data Strategy team. This role owns the analytics, AI, automation, and client\-data\-product layer of the function, with a focus on judgment\- and design\-heavy work. The individual will lead the intelligence layer supporting the team’s secondary data strategy, including AI\-based data extraction, RFP and client datasets, and advanced reporting and analytics, while driving cross\-functional coordination across business and technology partners.
Primary Responsibilities:
- Lead the analytics and data product agenda for the Secondary Data Strategy workstream, translating business needs into scalable reporting, insight, and workflow solutions.
- Design and enhance the intelligence layer that supports secondary investment data, including standardized reporting outputs, analytical views, and downstream client deliverables.
- Drive AI\- and automation enabled data extraction initiatives to improve efficiency, consistency, and scalability in how investment and market data are captured and structured.
- Oversee the development and maintenance of client and RFP datasets, ensuring data is decision useful, presentation ready, and aligned with stakeholder needs.
- Partner cross\-functionally with investment teams, product specialists, technology, data management, and operations to prioritize and execute strategic initiatives.
- Help define data standards, governance expectations, and quality controls for critical secondaries related datasets and reporting outputs.
- Identify opportunities to improve reporting frameworks, automate recurring analytical processes, and build higher value data products for internal and external use.
- Contribute subject matter expertise on secondary market data structures, reporting needs, and process design in support of broader team development.
Experience \& Skills Qualifications:
- 6–9\+ years of relevant experience in analytics, data products, business intelligence, financial data strategy, or a related function.
- Strong background in analytics, data product development, or data strategy, with the ability to convert complex business requirements into practical solutions.
- Hands\-on experience applying AI/ML to unstructured document and data extraction (e.g., LLMs, NLP, OCR, intelligent document processing) to convert deal, fund, and market documents into structured data.
- Practical understanding of large language models and generative AI, including prompt engineering and evaluating model outputs for accuracy, reliability, and hallucination risk.
- Ability to design and oversee AI\-enabled data pipelines and automation workflows, including human\-in\-the\-loop validation and quality controls.
- Awareness of responsible/governed AI practices: data privacy, model governance, auditability, and appropriate use of third\-party vs. internal AI tooling.
- Strong communication and stakeholder management skills, with the ability to lead cross\-functional initiatives.
- High degree of ownership, structured problem\-solving ability, and comfort operating in a build\-and\-improve environment.
- Private markets or secondaries domain knowledge is highly preferred.
Preferred Qualifications:
- Familiarity with CapIQ, PitchBook, Preqin, and Bloomberg.
- Working knowledge of VBA, SQL and/or Python.
- Experience with data modeling, reporting architecture, or financial data structures.
- Private equity secondaries experience or closely related private markets exposure.
- Experience with AI/ML frameworks and tooling (e.g., Python ML libraries, LLM APIs/frameworks such as OpenAI, Claude, or similar).
Applicants must be authorized and have the right to work in the country where the role is located without the need for current or future sponsorship.
Compensation Details
The salary range for this role is $175,000\-$190,000\. This is the lowest to highest salary we in good faith believe we would pay for this role at the time of this posting. We may ultimately pay more or less than the posted range, and the range may be modified in the future. This range is only applicable for jobs to be performed in the job posting location. An employee’s pay position within the salary range will be based on several factors including, but limited to, relevant education, qualifications, certifications, experience, skills, seniority, geographic location, business sector, performance, shift, travel requirements, sales or revenue\-based metrics, market benchmarking data, any collective bargaining agreements, and business or organizational needs. This job is also eligible for a discretionary bonus, which, along with base salary and retirement contributions, is part of our total comprehensive package. We offer a comprehensive package of benefits including paid time off, medical/dental/vision insurance, retirement, life insurance and other benefits to eligible employees.### Note: No amount of pay is considered to be wages or compensation until such amount is earned, vested, and determinable. The amount and availability of any bonus, commission, production, or any other form of compensation that are allocable to a particular employee remains in the Company's sole discretion unless and until paid and may be modified at the Company’s sole discretion, consistent with the law.
*Neuberger is an equal opportunity employer. The Firm and its affiliates do not discriminate in employment because of race, creed, national origin, religion, age, color, sex, marital status, sexual orientation, gender identity, disability, citizenship status or protected veteran status, or any other characteristic protected by local, state, or federal laws, rules, or regulations. If you would like to contact us regarding the accessibility of our website or need assistance completing the application process, please contact* *[email protected]**.*
*Learn about the* *Applicant Privacy Notice**.*
Salary Context
This $175K-$190K range is above the median 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 Neuberger Berman, 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. This role's midpoint ($182K) sits 15% below the category median. Disclosed range: $175K to $190K.
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.
Neuberger Berman AI Hiring
Neuberger Berman has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $190K - $190K.
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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.