Technical Project Manager, AIA Labs

Bridgewater Associates•New York, NY

13h•$225,000 - $275,000•Onsite

About The Position

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.

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.

Nice To Haves

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.

Responsibilities

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.
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.
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.
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.
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.