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About This Role
### *Over the last 20 years, Ares’ success has been driven by our people and our culture. Today, our team is guided by our core values – Collaborative, Responsible, Entrepreneurial, Self\-Aware, Trustworthy – and our purpose to be a catalyst for shared prosperity and a better future. Through our recruitment, career development and employee\-focused programming, we are committed to fostering a welcoming and inclusive work environment where high\-performance talent of diverse backgrounds, experiences, and perspectives can build careers within this exciting and growing industry.*
Job Description
Role Summary:
The Senior Associate, AI PMO is a core member of the Data \& AI Program Management Office, responsible for driving the execution rigor behind Ares' Data \& AI Strategy. This role owns end\-to\-end program management for a portfolio of AI and data initiatives — translating strategic priorities into structured delivery plans, running agile execution in JIRA, and producing polished executive and steering committee materials that communicate progress, risk, and value to senior firm leadership. The Senior Associate operates with a high degree of autonomy, partners directly with product, engineering, and platform leads, and is expected to independently own the narrative and cadence of the PMO function.
Key Responsibilities:
Program \& Project Management
- Own end\-to\-end program management for a portfolio of AI/data initiatives, managing multiple workstreams, spanning the hub\-and\-spoke Data \& AI organization, from intake and prioritization through delivery and adoption.
- Establish and run governance cadences (steering committees, sprint reviews, milestone checkpoints) across the Data \& AI organization and vertical stakeholders.
- Manage cross\-workstream dependencies, resourcing conflicts, and critical\-path risks; escalate proactively with recommended mitigations.
- Define and maintain program\-level RAID logs, roadmaps, and capacity/resourcing views across engineering, product, and data science pods.
Agile \& Technical Execution Support
- Build and maintain JIRA structures (epics, initiatives, sprints, workflows) to translate strategic and executive priorities into actionable backlogs for engineering and AI product teams.
- Partner with engineering leads and product managers to groom backlogs, size effort, and sequence delivery against firm priorities.
- Own JIRA/Confluence hygiene and reporting standards (burndown, velocity, release tracking) across the Data \& AI portfolio.
Executive Communication \& Storyboarding
- Independently develop executive\-ready PowerPoint materials — strategy narratives, board and CEO talking points, quarterly business reviews — that translate technical progress into business impact.
- Own the design and content of senior Steering Committee and governance forum decks, working directly with the vertical heads in Data \& AI on messaging.
- Develop clear, compelling storylines for complex, technical AI/data programs (e.g., agentic platform build\-out, Databricks\-on\-Azure migration) tailored to executive level audiences.
- Build and maintain a reusable library of PMO templates, status frameworks, and visual standards for the Data \& AI organization.
Cross\-Functional Partnership
- Serve as the connective tissue across Data \& AI Product, Engineering, Platform, Governance, and the vertically facing technology teams to ensure alignment on priorities and delivery.
- Partner with Governance/Risk/Compliance stakeholders to track model and agent sign\-offs, ensuring PMO reporting reflects governance status accurately.
- Mentor and provide day\-to\-day direction to the Associate, AI PMO.
Required Qualifications
- 5–8 years of program/project management experience, ideally within a technology, data/AI, or financial services organization; PE or alternative asset management experience a strong plus.
- Demonstrated ownership of complex, multi\-workstream technical programs from strategy through delivery.
- Advanced proficiency in PowerPoint with a strong track record of building executive and board\-level narratives and visual storytelling.
- Hands\-on experience configuring and administering JIRA (epics, workflows, sprints) and Confluence for technical program execution; Agile/Scrum experience required.
- Working domain knowledge of data and AI concepts (e.g., RAG, agentic architectures, model governance, data platforms) sufficient to translate technical work into business language.
- Exceptional written and verbal communication skills; comfort presenting directly to senior leadership.
- Bachelor's degree required; PMP, CSM, or equivalent certification preferred.
Preferred / Nice to Have
- Prior experience supporting a CDAO, Chief Data Officer, or enterprise AI transformation function.
- Familiarity with Databricks, Azure, or similar cloud/data platforms.
- Experience building PMO functions or governance processes from scratch (0\-to\-1 environments).
- Exposure to private equity or credit investment workflows (CIM review, IC memos, deal diligence).
Tools \& Systems
JIRA, Confluence, Microsoft PowerPoint, Excel, and SharePoint form the core toolkit; familiarity with Databricks, LangFuse, or similar AI/data platform tooling is beneficial but not required.
Reporting Relationships
Head of Data and AnalyticsCompensation
The anticipated base salary range for this position is listed below. Total compensation may also include a discretionary performance\-based bonus. Note, the range takes into account a broad spectrum of qualifications, including, but not limited to, years of relevant work experience, education, and other relevant qualifications specific to the role.
$150,000 \- $170,000
The firm also offers robust Benefits offerings. Ares U.S. Core Benefits include Comprehensive Medical/Rx, Dental and Vision plans; 401(k) program with company match; Flexible Savings Accounts (FSA); Healthcare Savings Accounts (HSA) with company contribution; Basic and Voluntary Life Insurance; Long\-Term Disability (LTD) and Short\-Term Disability (STD) insurance; Employee Assistance Program (EAP), and Commuter Benefits plan for parking and transit.
Ares offers a number of additional benefits including access to a world\-class medical advisory team, a mental health app that includes coaching, therapy and psychiatry, a mindfulness and wellbeing app, financial wellness benefit that includes access to a financial advisor, new parent leave, reproductive and adoption assistance, emergency backup care, matching gift program, education sponsorship program, and much more.
*There is no set deadline to apply for this job opportunity. Applications will be accepted on an ongoing basis until the search is no longer active.*
Salary Context
This $150K-$170K range is below 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 Ares Management, 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. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($160K) sits 27% below the category median. Disclosed range: $150K to $170K.
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.
Ares Management AI Hiring
Ares Management has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Los Angeles, CA, US. Compensation range: $170K - $170K.
Location Context
AI roles in Los Angeles pay a median of $215,000 across 397 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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