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About This Role
AI Governance Engineer
Dalio Family Office Overview:
The Dalio Family Office (DFO) supports Barbara and Ray Dalio and their family in their ventures, investments, and philanthropic efforts under Dalio Philanthropies, which includes OceanX, Dalio Education, Endless Network, and the Beijing Dalio Foundation. The core of the DFO's culture is built around meaningful work and meaningful relationships and the family's commitment to giving back. The office is headquartered in Westport, CT with regional offices in New York City, Singapore, and Abu Dhabi.
Position Summary:
This AI Governance Analyst / Engineer will build and operate the program that lets the organization adopt agentic AI aggressively, and safely. You will author the AI\-SSDLC, gate agents before production, own the organization's AI threat model, and hunt for risky or misaligned behavior before it becomes an incident. The role works across IT, Compliance, Legal, and every business unit building or consuming AI tools, and serves as the primary SME on Agentic AI security for senior leadership and the board.
Day\-to\-day responsibilities would include a combination of the following:
- Develop and manage the AI Secure Software Development Lifecycle (AI\-SSDLC), including governance controls and an enterprise agent registry.
- Lead AI threat modeling, risk assessments, red team exercises, and security reviews for AI agents and platforms.
- Monitor AI systems for security, compliance, and behavioral risks; enforce governance standards and executive reporting.
- Partner with Legal, Privacy, Compliance, and Security teams to align AI programs with regulatory and industry frameworks.
- Drive automation, process scalability, and reporting dashboards to improve governance efficiency and risk management.
The ideal candidate will possess the following knowledge, skills, attributes, and values:
- Integrity and sound judgment in balancing AI innovation with risk management.
- Expertise in AI governance, AI\-SSDLC, and enterprise risk frameworks.
- Strong knowledge of AI security, threat modeling, red teaming, and agentic AI architectures.
- Ability to influence and collaborate across IT, Legal, Compliance, Privacy, and business teams.
- Proven capability to design scalable governance processes, controls, and automation.
- Executive\-level communication skills with the ability to present complex AI risks and strategies to senior leadership and the board.
Illustrative Benefits:
- 100% company paid medical premiums
- 17 company paid holidays
- Friday summer hours
- Monthly community happy hours
- Hybrid work environment
- Free catered food services for in\-office days
- Generous PTO offering
- Casual dress code
- 150% 401(k) match up to $7,500 and 100% match above $7,500 ($15k match limit)
- Gym reimbursement, back up childcare services, insurance, financial, and legal services, and much more!
Qualifications:
- Bachelor's Degree or Diploma in Information Security, Risk Management, Computer Science, or a related field.
- 5\+ years of experience in information security, IT governance, risk management, compliance, or software engineering, including 2\+ years of hands\-on enterprise AI/automation experience.
- Knowledge of AI in Power Automate, including AI Builder, agentic workflows, connector permissions, and audit logging.
- Strong understanding of Model Context Protocol (MCP), trust boundaries, tool scoping, and AI agent security.
- Expertise with Microsoft Purview, including DSPM for AI, DLP, sensitivity labels, audit logging, insider risk.
- Experience with OWASP, STRIDE, PASTA, NIST, AI RMF, ISO/IEC
- Ability to develop scripts or low code solutions; Python, Powershell, or Power Platform and analyze security logs
- Approximately 10% travel required both domestically and internationally
Compensation:
Compensation for the role includes a competitive salary in the range from $175,000 \-$260,000 (inclusive of a merit\-based bonus, dependent on years of experience, level of education obtained, as well as applicable skillset) and an excellent benefits package, including paid time off ranging from 15 to 25 days based on years of service, paid sick and safe leave, dental, vision, life and disability insurance, paid parental time off, birth mother recovery pay, sick family member pay, parental ramp back up program, gym reimbursement and generous employer match for 401k.
Please note we are unable to provide immigration sponsorship for this position.
*At the DFO, we believe our biggest asset is our people. We are proud to be an equal opportunity employer, hiring and developing individuals from diverse backgrounds and experiences to add to our collaborative culture. The DFO treats all candidates and employees with respect and does not discriminate in our recruiting, hiring, and promoting processes and general treatment during employment, including on the basis of actual or perceived race, creed, color, religion, sex, age, sexual orientation, gender identity and/or expression, alienage or national origin, ancestry, citizenship status, marital status, veteran status, or disability**.*
Salary Context
This $175K-$260K 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 Dalio Family Office, 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. Mid-level AI roles across all categories have a median of $194,400. Disclosed range: $175K to $260K.
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.
Dalio Family Office AI Hiring
Dalio Family Office has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Westport, CT, US. Compensation range: $260K - $260K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
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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