Product Manager, Enterprise Technology and AI Productivity

Bridgewater Associates•New York, NY

5d•$225,000 - $275,000•Hybrid

About The Position

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.

Requirements

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 Haves

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.

Responsibilities

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.
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.
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.
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.
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.