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About This Role
About Bridgewater
Bridgewater Associates is a premier asset management firm, focused on delivering unique insight and partnership for the most sophisticated global institutional investors.
Our investment process is driven by a tireless pursuit to understand how the world’s markets and economies work — using cutting\-edge technology to validate and execute on timeless and universal investment principles.
Founded in 1975, we are a community of independent thinkers who share a commitment to excellence. By fostering a culture of openness, transparency, and inclusion, we strive to unlock the most complex questions in investment strategy, management, and corporate culture.
Our Culture
Our culture is anchored in excellence, meaning constant improvement, and it is deeply tied to our mission. Because markets are objective, competitive, and getting smarter everyday, we need to keep rapidly improving to have any chance of beating them. Truth is our most essential tool for engaging with the markets and constantly improving because once you know what's true about your problems and opportunities, you can determine how to get better. Valuing truth means being transparent about your decision\-making and mistakes, giving and receiving feedback with humility, and fighting for the best answers over hierarchy, ego, or self\-interest. Operating this way is hard – it's only possible because we build meaning in our work and relationships. This meaning comes from the audacity of the mission, and the joy of working alongside people who make you a better version of yourself. The culture, like Bridgewater itself, is always evolving. In 1997 our founder Ray Dalio wrote down his lessons, starting with a Philosophy Statement which remains our foundation. This later evolved into a set of 300\+ Principles. In 2022, when Ray transitioned the company, we re\-underwrote several of those principles and evolved others, with a specific focus on Meritocracy. Today the culture sits, alongside our people, as our most important edge. When we get it right, it’s the engine that powers everything else.
The Opportunity
Enterprise Technology sits at the intersection of Bridgewater’s technology platform and its front office investment organization — the Alpha Engine. Our team is responsible for enabling the firm’s most senior investors, researchers, and client service professionals with the tools, platforms, and AI\-powered workflows they need to generate returns and serve clients.
We are hiring a Product Manager to own and drive execution across a portfolio of high\-impact initiatives that directly enable investor and client service workflows. This is not a backlog\-grooming, ticket\-management role. We need someone who can deeply understand how investors and client service professionals work, translate that understanding into product strategy, and drive delivery of meaningful outcomes — often in ambiguous, fast\-moving spaces where AI is reshaping workflows in real time. We value candidates who combine structured problem\-solving foundations (e.g., management consulting, strategy, or advisory roles) with hands\-on product delivery in technically complex environments — and who are actively building with AI tools themselves, not just evaluating them.
What You’ll Own
You will be the primary product owner for several of the team’s major workstreams, with direct accountability to senior business stakeholders across the Alpha Engine and Client Service departments. Your portfolio will span:
Investor Workflow Enablement
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- Own the product strategy for enabling investor workflows within the existing enterprise toolset — including secure app deployment on the firm’s internal platform, AI\-assisted meeting synthesis and writing agents, and management system design for the investment organization.
- Partner with senior investors to understand their daily demands, observe workflows directly (physically and virtually), and translate that into a living roadmap of hypotheses — not just feature requests.
New Product Evaluation \& AI Tooling
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- Evaluate, pilot, and make build\-vs\-buy recommendations for emerging AI capabilities: computer use agents, voice dictation, co\-work/agentic tools from vendors (i.e., Anthropic, OpenAI, Microsoft, Google, etc.), and first\-party model integrations.
- Operationalize visibility into LLM usage, model costs, and annualized run rates across teams to ensure the firm is making smart, informed investment decisions in AI infrastructure.
- Navigate the firm’s security, compliance, and data classification frameworks (to enable AI capabilities within appropriate risk boundaries. You will regularly engage with risk, compliance, and security stakeholders to drive policy evolution.
Hardware \& Mobile Experience
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- Drive the strategy and delivery of a mobile experience for front office professionals — from responsive web apps to native\-feeling mobile deployment of internal tools. This requires building alignment across product, engineering, security, and senior business leadership on what “good” looks like before solutioning.
- Own the compute endpoint strategy for investors, including hardware refresh pilots (Mac vs. Windows), performance optimization, and managing the change management that comes with it.
- Champion improvements to the internal developer and technologist experience — identifying and reducing friction in how technical staff interact with security policies, development infrastructure, and tooling so they can focus on high\-value work.
Media Strategy \& Client Service Workflows
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- Drive near\-term execution on the firm’s media strategy: reliable transcripts, API access to meeting recordings, real\-time post\-processing, and enabling the distribution of meeting artifacts within the firm’s compliance framework.
- Support Client Service transformation initiatives including Salesforce workflow optimization, AI\-powered content generation (audio/video for clients), scalable email enablement, and the evaluation and rollout of new CS\-facing tools.
How You’ll Work
- Build deep, trusted relationships with senior business stakeholders across the Investment and Client Service departments. Understand their workflows by sitting with them — not just by reading requirements documents.
- Maintain a living synthesis of the external landscape: vendor capabilities, emerging tools, competitor approaches, and a perspective on what’s coming next. The AI landscape is moving weekly; you need to keep pace.
- Articulate the “Job to be Done” for every item on your roadmap. You don’t build features because they’ve been asked for — you validate whether they solve the core workflow problem, and you kill low\-value requests when the evidence says to.
- Maintain an evolving view of the entire Bridgewater Technology product suite and map customer demand back to the right approach for solving problems — whether that’s a new build, a configuration change, a vendor tool, or a process change.
- Handle the ambiguity of new technology integration with minimal escalation. Bring solutions, not just problems — but know when to escalate early to keep projects on track.
- Track not just deployment but actual usage patterns. Prove what is working and what is not. Deliver measurable adoption metrics and be willing to iterate or kill initiatives based on evidence.
- Actively integrate AI into your own day\-to\-day workflows and demonstrate specific examples of how it improves your output. You should be a practitioner, not just an evaluator.
- Drive continuous improvement in team processes, delivery practices, and organizational workflows. Lead meaningful process improvements that the team adopts.
- Shift your time away from purely tactical execution (sprint grooming, ticket management) toward strategic discovery, synthesis, and stakeholder engagement.
What We’re Looking For
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- 3–7 years of experience in product management, strategy consulting, or a problem\-solving role in a technically complex environment, with a demonstrated track record of owning initiatives end\-to\-end and driving them to realized business value.
- You are actively building with AI tools outside of your day job — whether that’s automating personal workflows, setting up agents, prototyping solutions, or contributing to open\-source projects. We want to see evidence of curiosity in action, not just familiarity with concepts.
- Deep comfort working in AI/ML\-adjacent spaces. You don’t need to train models, but you need to understand LLMs, embeddings, RAG architectures, AI agents, model selection trade\-offs, and cost dynamics well enough to make informed product decisions and partner effectively with engineers and data scientists.
- Comfort operating in environments with significant governance, compliance, or security constraints. You don’t need to come from a regulated industry, but you must be able to engage constructively with risk, legal, and security stakeholders and navigate policy discussions without getting over your skis.
- Strong communication skills and executive presence. You will engage with senior business stakeholders — including C\-suite and senior investment professionals — with increasing independence over time. You must be able to translate complex technical concepts into clear business narratives and navigate ambiguity with composure.
- A bias toward action and delivery, combined with strategic judgment about when to push and when to wait. The technology landscape shifts weekly; you must be comfortable making decisions with incomplete information and iterating rapidly.
- Familiarity with software development lifecycles and Agile delivery practices. We can teach the specifics quickly — what matters more is that you understand how technology gets built and can partner effectively with engineers.
Nice\-to\-Have
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- Experience in financial services, asset management, or working with investment professionals.
- Hands\-on experience with AI\-assisted development tools (i.e., Claude Code, GitHub Copilot, Cursor), building agents or automated workflows, or prototyping internal tools using low\-code/AI platforms. Show us your GitHub repo or side projects.
- Experience with vendor management and enterprise software evaluation — including navigating procurement, security reviews, and build\-vs\-buy decisions.
- Experience with data platform tooling, search infrastructure (vector search, knowledge graphs), or enterprise content management systems.
- Comfort operating in ambiguous problem spaces where requirements evolve alongside experimentation and learning.
Physical Requirements
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The anticipated onsite requirement for this role is 4 days per week at our New York City office location, with occasional travel to our Westport, CT office location
Compensation
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The wage range for this role is $225,000 – $275,000 inclusive of base salary and discretionary target bonus. The expected base salary for this role is between 75% – 85% of this wage range.
One of our core priorities at Bridgewater is to enable our employees to build a great life and career, and we believe our benefits are an important extension of that philosophy. As such, currently Bridgewater offers a competitive suite of benefits.
*Bridgewater reserves the right to change its current benefits program at any time, in a manner that is consistent with applicable federal and state regulations.*
*This job description is not a contract and confers no contractual rights, privileges, or benefits on any applicant or potential applicant. Bridgewater has the right to change any and all terms of this job description, including, but not limited to, job responsibilities, qualifications and benefits. Nothing in this job description constitutes an offer or guarantee of employment. Please note that we* *do not* *provide immigration sponsorship for this position.*
*Bridgewater Associates, LP is an Equal Opportunity Employer*
Salary Context
This $225K-$275K range is above the 75th percentile for AI Product Manager roles in our dataset (median: $187K across 164 roles with salary data).
View full AI Product Manager salary data →Role Details
About This Role
AI Product Managers define what AI features get built and why. They translate business problems into ML-solvable tasks, work with engineering to scope model requirements, and own the metrics that determine if an AI feature is working. The role requires a rare combination of technical fluency and product instinct.
Unlike traditional product management, AI PM work involves managing uncertainty at a fundamental level. Your model might work 90% of the time. What happens the other 10%? What's the user experience when the AI is wrong? How do you measure 'good enough' for a probabilistic system? These questions don't have easy answers, and the AI PM is the person responsible for finding them.
Across the 4,133 AI roles we're tracking, AI Product Manager positions make up 5% of the market. At Bridgewater Associates, this role fits into their broader AI and engineering organization.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
What the Work Looks Like
A typical week includes: reviewing model evaluation results with the ML team, defining success metrics for a new AI feature, conducting user research on how customers respond to AI-generated outputs, writing product requirements that include accuracy thresholds and fallback behaviors, and presenting the AI roadmap to leadership. You're the translator between technical capability and business value.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
Skills Required
Technical fluency with ML concepts is essential, though you won't be writing models. Expect to understand training data, evaluation metrics, model limitations, and responsible AI practices. SQL and basic Python are increasingly expected. Experience with A/B testing, data analysis, and product analytics is baseline. Understanding LLM capabilities and limitations is now a core requirement.
The differentiator is AI-specific product thinking: knowing when to use ML vs. heuristics, understanding the cost of training data collection, designing graceful degradation for model failures, and building products that improve with usage data. Experience with AI safety, bias mitigation, and responsible AI deployment is increasingly important.
Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
Compensation Benchmarks
AI Product Manager roles pay a median of $213,800 based on 610 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,778. This role's midpoint ($250K) sits 17% above the category median. Disclosed range: $225K to $275K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
Bridgewater Associates AI Hiring
Bridgewater Associates has 2 open AI roles right now. They're hiring across AI Product Manager, AI/ML Engineer. Positions span New York, NY, US, Westport, CT, US. Compensation range: $275K - $800K.
Location Context
AI roles in New York pay a median of $211,000 across 2,760 tracked positions. That's 5% above the national median.
Career Path
Common paths into AI Product Manager roles include Product Manager, Data Analyst, Technical Program Manager.
From here, career progression typically leads toward Director of AI Product, VP Product, Head of AI.
The most effective path is PM experience plus self-directed AI education. Take Andrew Ng's courses, build a small ML project, and learn enough Python to read model evaluation code. The goal isn't to become an ML engineer. It's to have credibility in technical conversations and to understand what's possible, what's hard, and what's a bad idea.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
AI Hiring Overview
The AI job market has 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 roles).
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
The AI Job Market Today
The AI job market spans 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). 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 (106) are outnumbered by mid-level (1,901) and senior (1,663) 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 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 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 $200,700. Top-quartile roles start at $254,000, and the 90th percentile reaches $307,500. 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 $274,200 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 (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 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.
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