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About This Role
Who We Are
Addepar is a global data and AI platform empowering investment professionals to turn complex financial information into actionable intelligence. Addepar unifies portfolio, market and client data in a total portfolio view and delivers AI\-powered insights within investment and client workflows. More than 1,400 firms in nearly 60 countries use Addepar to manage and advise on nearly $9 trillion in assets. Its open platform integrates with nearly 650 software, data and consulting partners to power end\-to\-end investment operations across firms of all sizes and complexity. Addepar supports clients worldwide with offices in New York City, Salt Lake City, London, Edinburgh, Pune, Dubai, Geneva, Singapore and São Paulo.
#### The Role
We are seeking a well rounded Staff AI Security Engineer to join our growing Information Security \& Risk team. In this role, you will be a member of our newly formed Security Solutions team. This team is at the forefront of driving organization\-wide security enhancements through high\-impact, project\-driven initiatives with a heavy focus on AI.
If you are looking for an opportunity to build from the ground up, influence security architecture, and meaningfully elevate Addepar's overall security posture, this is the role for you.
Addepar takes a market\-based approach to pay. A successful candidate's starting pay will be determined based on the role, job\-related skills, experience, qualifications, work location, and market conditions.
The range displayed on each job posting reflects the minimum and maximum target base salary for roles in Colorado, California, and New York. The current range for this role is $199,000 \- $249,000 (base salary) \+ bonus \+ equity \+ benefits.
Your recruiter can share more about the specific salary range for your preferred location during the hiring process. Additionally, these ranges reflect the base salary only, and do not include bonus, equity, or benefits.
Applicants must be legally authorized to work in the United States for any employer without requiring current or future visa sponsorship (for example, employment\-based visas such as H\-1B, F\-1/OPT, or similar), and must be authorized to begin work in the U.S. on their first day of employment.
#### What You'll Do
Focus on AI Security in the following areas:
- + Innovation \& Strategy \- Keep up with the latest trends, models and innovations in all facets of AI Security and advise on proper architectures
+ Platform AI Security \- Working with our Engineering and Application Security teams to ensure our products are leveraging AI in a secure manner across our platform.
+ AI Enablement \-Partnering with our Architecture team to continue to evolve Addepar's Internal AI Productivity initiatives by securely enabling our business to leverage AI to streamline processes and enhance productivity.
AI Security Defense in Depth \- Focusing on how to protect Addepar against AI based attacks and threats such as Supply Chain compromises by doing the following:
- + AI Guardrails \- Advising or implementing guardrails and controls to minimize data loss risks and use of insecure or unapproved methods of AI use.
+ AI for Security \- Guiding and Mentoring fellow Security teams on how to leverage AI for security benefits.
+ AI Governance \- Ensure documentation and guidance for all employees is kept up to date and assist with any questions or concerns that arise.
While the focus is on AI Security, as part of the Security Solutions team; you will have the opportunity to:
- + Drive Security Initiatives: Lead and execute complex, project\-driven security enhancements across the organization's infrastructure, applications, and corporate environment.
+ Security Architecture \& Design: Collaborate with engineering and product teams to architect, build, and deploy scalable, resilient security solutions.
+ Enablement \& Automation: Build tools, guardrails, and automated workflows that empower development teams to ship secure code quickly and safely.
+ Risk Mitigation: Identify gaps in the current security posture, design remediation plans, and systematically execute them to maturity.
+ Cross\-Functional Collaboration: Act as a trusted security advisor across engineering, product, and R\&D teams, fostering a strong culture of security enablement rather than restriction.
+ Mentorship: Mentor junior and mid\-level engineers within the Information Security \& Risk team, helping to elevate collective technical capabilities.
#### Who You Are
- Experience: 9\+ years of dedicated experience in security engineering, application security, infrastructure security, data security or cloud security roles.
- Project Leadership: Proven track record of taking vague security problems, turning them into structured project plans, and executing them to completion.
- Attention to details and analytical skills.
- Curious, always learning and deeply interested in Information Security.
- Ability to build strong relationships and work collaboratively with internal and external partners.
- Excellent verbal and written communication skills with the ability to build strong relationships with internal stakeholders and external partners.
#### Desired Technical Skills
- Deep expertise in all aspects of AI including AWS Services, LLM's, MCP, Agentic Agents, Frameworks, Governance \& Regulations, Attacks, Guardrails and solid understanding of costs.
- A Software Engineering background is a strong plus.
- Amazon Web Services across its most widely used services, especially Security services.
- Strong experience and understanding with Authentication \& Authorization protocols. and how non\-human identities can be secured appropriately.
#### Nice to Have
- AI based certification such as AAISM, AAIA or CAISP.
- Knowledge and Experience with AI based frameworks such as NIST AI Framework or EU Artificial Intelligence Act
- Knowledge and Experience with MITRE ATLAS or OWASSP Top 10 for LLM's.
- Bachelors or Masters Degree with a focus in Information Systems/Computer Science/Security
Our Values
- Act Like an Owner \- Think and operate with intention, purpose and care. Own outcomes.
- Build Together \- Collaborate to unlock the best solutions. Deliver lasting value.
- Champion Our Clients \- Exceed client expectations. Our clients' success is our success.
- Drive Innovation \- Be bold and unconstrained in problem solving. Transform the industry.
- Embrace Learning \- Engage our community to broaden our perspective. Bring a growth mindset.
In addition to our core values, Addepar is proud to be an equal opportunity employer. We seek to bring together diverse ideas, experiences, skill sets, perspectives, backgrounds and identities to drive innovative solutions. We commit to promoting a welcoming environment where inclusion and belonging are held as a shared responsibility.
We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us to request accommodation.
PHISHING SCAM WARNING: Addepar is among several companies recently made aware of a phishing scam involving con artists posing as hiring managers recruiting via email, text and social media. The imposters are creating misleading email accounts, conducting remote "interviews," and making fake job offers in order to collect personal and financial information from unsuspecting individuals. Please be aware that no job offers will be made from Addepar without a formal interview process. Additionally, Addepar will not ask you to purchase equipment or supplies as part of your onboarding process. If you have any questions, please reach out to ta\[email protected].
Salary Context
This $199K-$249K 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 Addepar, 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: $199K to $249K.
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.
Addepar AI Hiring
Addepar has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $249K - $249K.
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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