About AIA Labs
AIA Labs is an in-house venture at Bridgewater Associates focused on using the state-of-the-art in Artificial Intelligence (AI) to generate returns in markets. We manage a dedicated investment strategy on behalf of the world's most sophisticated pools of capital and employ the latest tools in AI, machine learning, statistics, and optimization. Our work is organized around a single mission: to create a general-purpose intelligence (an "artificial investor") capable of performing systematic investment research.
We maintain extensive ties to broader research community and cultivate a collegial, intellectually vibrant work environment. Our team is led by Managing Chief Investment Officer Greg Jensen and Chief Scientist Dr. Jasjeet Sekhon.
Role Overview
We are seeking a Technical Project Manager to partner closely with machine learning researchers and engineers to drive execution across a portfolio of research and applied ML initiatives. This role sits at the intersection of research strategy, technical execution, and delivery rigor, enabling scientists and ML engineers to focus on innovation while ensuring work progresses efficiently toward meaningful outcomes. You will work side-by-side with research scientists to shape and run a research agenda, translate exploratory work into structured plans, dynamically prioritize initiatives, and manage capacity across highly specialized teams. Many initiatives in this space are highly confidential and strategically sensitive, requiring discretion, sound judgment, and the ability to operate effectively with limited information sharing.
Success in this role requires comfort operating in ambiguous, experimental environments, strong technical fluency, and the ability to impose just enough structure without constraining research velocity.
Research Program & Agenda Management
- Partner closely with research scientists to define, maintain, and execute a machine learning research agenda, spanning exploratory research, experimentation, and applied development.
- Translate research goals and hypotheses into clear project plans, milestones, and success criteria while respecting the iterative nature of scientific work.
- Coordinate across multiple concurrent research efforts, balancing short-term experimentation with longer-term strategic initiatives.
- Support planning and execution of confidential research initiatives, ensuring appropriate handling of sensitive information and controlled communication.
Delivery, Prioritization & Capacity Management
- Own dynamic prioritization across research and ML engineering efforts, adjusting plans based on experimental results, shifting priorities, and resource constraints.
- Manage team capacity across scientists and ML engineers, ensuring realistic commitments, sustainable pace, and effective allocation of specialized skills.
- Identify and resolve dependencies across research, engineering, data, and infrastructure teams.
- Proactively unblock ML engineers and researchers by removing operational, process, or coordination barriers—often within constrained or confidential contexts.
Agile & Execution Enablement (Applied to Research)
- Facilitate planning, check-ins, reviews, and retrospectives tailored to research-driven workflows, not traditional product-only delivery.
- Apply Agile principles pragmatically to research environments, adapting processes to support experimentation, learning, and iteration.
- Establish lightweight execution rhythms that provide visibility while respecting confidentiality boundaries.
Machine Learning & Technical Collaboration
- Work closely with machine learning engineers on model development, experimentation pipelines, evaluation cycles, and production handoffs.
- Understand ML development workflows well enough to anticipate bottlenecks related to data availability, experimentation cycles, compute constraints, or evaluation timelines.
- Support transitions from research prototypes to more production-oriented implementations in partnership with engineering teams.
- Leverage AI-enabled and automation tooling to improve experiment tracking, documentation, and delivery reporting while maintaining strict confidentiality standards.
Stakeholder Communication & Documentation
- Serve as a central coordination point between research, ML engineering, product, and leadership stakeholders.
- Communicate progress, learnings, risks, and trade-offs with clarity, including when outcomes are uncertain or exploratory.
- Produce structured documentation (e.g., research roadmaps, execution plans, dependency maps) with appropriate controls for confidential content.
- Exercise sound judgment regarding information sharing, ensuring sensitive details are disclosed only to appropriate audiences.
Minimum Requirements
- 3+ years of experience managing technical projects or programs in highly technical or research-driven environments.
- Demonstrated ability to drive execution in ambiguous problem spaces where requirements evolve based on experimentation and learning.
- Proven experience owning work end-to-end, from early exploration through delivery of tangible outcomes or insights.
- Strong understanding of software development processes and sufficient familiarity with machine learning workflows to effectively partner with scientists and ML engineers.
- Experience dynamically prioritizing work and managing capacity across teams with competing demands.
- Strong, pragmatic knowledge of Agile principles and experience adapting them beyond traditional product teams.
- Excellent verbal and written communication skills, with exceptional discretion and judgment when handling sensitive information.
Preferred Qualifications
- Experience working on confidential, proprietary, or strategically sensitive initiatives.
- Experience supporting machine learning research or applied research teams.
- Familiarity with ML experimentation cycles, evaluation methodologies, and research-to-production transitions.
- Comfort operating in environments where progress is measured by learning, signal, and insight rather than shipped features alone.
What Success Looks Like in This Role
- Research scientists and ML engineers are able to focus on high-impact work with minimal operational friction.
- Confidential research initiatives progress efficiently while maintaining appropriate information controls.
- Research efforts are well-prioritized, resourced appropriately, and aligned to strategic objectives.
- Leadership has clear visibility into progress and trade-offs without compromising sensitive details.
- Execution rigor improves over time without constraining scientific creativity or experimentation.
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.
Explore more information about Bridgewater on our website here.
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.
Physical Requirements
- The anticipated onsite requirement for this role is 4 days per week at our New York City office location.
Compensation Band
- The wage range for this role is $225,000 - $275,000 inclusive of base salary and discretionary 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. Explore more information about Bridgewater's benefits on our website here.
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
