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Job Description
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#### Requisition ID
94306
#### Department
Office of the General Counsel
#### Job Function
Office of the General Counsel
#### Location
New York,New York,United States
#### Role Location Designation
Hybrid \- 3 days per week
Location Designation: Hybrid \- 3 days per week
Join the Office of the General Counsel where we collaborate with diverse business units, providing effective and creative legal guidance that aligns with New York Life's core values. Your work directly impacts how the company makes decisions, fostering sound business practices, safeguarding its reputation and optimizing efficiency. Become part of a team dedicated to serving as a trusted partner, helping the business navigate the legal landscape and achieve its goals.
New York Life seeks a highly accomplished and sophisticated attorney to join its Technology, Intellectual Property \& Strategic Sourcing (TIPSS) Law Group. The successful candidate is a seasoned attorney capable of successfully engaging as a trusted legal advisor to business and technology leaders on the acquisition, implementation, and commercialization of emerging technologies, including Generative AI, Agentic AI, machine learning, intelligent automation, and next\-generation digital platforms. The selected attorney will lead the negotiation of our most complex technology, AI and strategic commercial transactions that advance business objectives while effectively managing legal, regulatory, cybersecurity, intellectual property, operational, and third\-party risks.
As part of a Fortune 100 regulated financial services and insurance company, you will engage directly with senior executives (including C\-level executives), structuring and negotiating agreements that are critical to New York Life’s long\-term success.
What You'll Do:
- Lead complex transactions across a broad spectrum of strategic areas, including:
+ Advanced technology transactions (SaaS, cloud, data, AI/GenAI/Agentic AI solutions, cybersecurity, digital platforms).
+ Strategic partnerships, joint ventures, and collaborations with emerging tech providers.
+ High\-value services, marketing, and other enterprise agreements.
- Serve as primary negotiator and strategist, driving deal outcomes that advance business goals while mitigating risk; this includes leading sophisticated negotiations on GenAI and Agentic AI agreements—covering model licensing, AI\-as\-a\-service arrangements, autonomous agent deployments, and multi\-party AI development deals—and navigating novel risk allocation, IP ownership, indemnification structures, audit rights, and evolving regulatory requirements..
- Advise senior leadership on AI adoption and governance, emerging technologies, privacy, cybersecurity, and third\-party risk management in the context of third party transactional work; counsel must have a working command of GenAI model architecture risk, Agentic AI autonomy risk (including multi\-agent systems and automated decision\-making pipelines), and the ability to translate those risks into enforceable contractual protections.
- Provide practical, business\-savvy counsel to internal stakeholders, balancing innovation, regulatory requirements, and enterprise risk while bringing creativity and a forward\-thinking mindset to develop legal strategies that advance NYL’s priorities.
- Collaborate across the Law Department and with cross\-functional teams (Technology, AI \& Data, Risk, Compliance, Procurement, Marketing and critical business units) to ensure seamless support for business initiatives.
- Drive innovation in Law Department by being open to leveraging new technologies, including AI\-enabled tools, to improve efficiency, accuracy, and business outcomes.
- Champion process excellence that balances risk management with business enablement.
Desired Skills \& Expertise
- Proven ability to handle sophisticated technology and commercial transactions from inception through execution in a regulated environment.
- Deep expertise in AI (including GenAI and Agentic AI), machine learning, big data, cloud computing, and other emerging technologies; specifically, demonstrated ability to negotiate GenAI and Agentic AI agreements, including the ability to assess and mitigate risks unique to large language models (LLMs), autonomous agents, model training on proprietary data, output indemnification, and AI system auditability and explainability requirements.
- Strong background in partnership agreements, joint ventures, strategic alliances, and other complex multi\-party arrangements.
- Strong working knowledge of privacy, cybersecurity, and data governance, with the ability to provide actionable risk mitigation strategies.
- Exceptional communication skills: able to translate complex legal and technical issues into clear, practical advice for senior executives, including the ability to articulate GenAI and Agentic AI contractual risk in business terms that resonate at the executive level.
- Strong leadership presence with proven ability to influence and guide senior executives (including C\-level executives).
What You'll Bring:
- 10\+ years of progressive experience leading sophisticated technology and commercial transactions, including AI, digital transformation, and strategic partnerships, with a demonstrated ability to manage multiple high\-profile matters simultaneously.
- Experience specifically negotiating GenAI and/or Agentic AI agreements is required and will be a material differentiator.
- Bachelors degree preferred, JD required; admission to the New York State Bar required.
- Experience in a regulated financial services or insurance environment strongly preferred.
- Demonstrated success leading high\-stakes negotiations and managing multiple complex matters simultaneously.
Job Level: LEVELPF6
Pay Transparency
Salary Range: $185,000\-$264,500
Overtime eligible: Exempt
Discretionary bonus eligible: Yes
Sales bonus eligible: No
Actual base salary will be determined based on several factors but not limited to individual’s experience, skills, qualifications, and job location. Additionally, employees are eligible for an annual discretionary bonus. In addition to base salary, employees may also be eligible to participate in an incentive program.
Our Benefits
We provide a full package of benefits for employees – and have unique offerings for a modern workforce, including leave programs, adoption assistance, and student loan repayment programs. Based on feedback from our employees, we continue to refine and add benefits to our offering, so that you can flourish both inside and outside of work.Click hereto discover more about our comprehensive benefit options or visit our NYL Benefits Site.
Our Commitment to Inclusion
At New York Life, fostering an inclusive workplace is fundamental to who we are and how we serve our communities. We have a longstanding commitment to creating an environment where individuals can contribute their best and succeed together. This foundation is rooted in our core values of humanity and integrity, ensuring that every employee feels valued and supported. By embracing a broad range of perspectives and experiences, we achieve greater success and fulfill our promise of providing financial security and peace of mind to families across all communities. Click here to learn more about New York Life’s leadership in this space.
Recognized as one of *Fortune’s* World’s Most Admired Companies, New York Life is committed to improving local communities through a culture of employee giving and volunteerism, supported by the Foundation. We're proud that due to our mutuality, we operate in the best interests of our policy owners. To learn more about career opportunities at New York Life, please visit the Careers page of www.NewYorkLife.com.
Job Requisition ID: 94306
Salary Context
This $185K-$264K range is above the 75th percentile 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 New York Life, 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 $218,750 based on 3,817 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $120,000. Disclosed range: $185K to $264K.
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.
New York Life AI Hiring
New York Life has 3 open AI roles right now. They're hiring across AI Software Engineer, AI/ML Engineer, Data Scientist. Based in New York, NY, US. Compensation range: $143K - $264K.
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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