Investment Engineer

Investment Engineer

New York City

For 50 years, Bridgewater has pursued one idea: the world can be understood. Markets and economies follow cause-and-effect relationships—and by understanding them, we think we can beat the markets and generate true uncorrelated returns ("alpha") at significant scale. Our clients include some of the world's most sophisticated institutional investors who turn to us to use our unique insights to solve their biggest problems.

Generating alpha at scale is an exceptionally difficult task. It requires predicting the future and doing so better than the millions of other extremely smart, highly motivated people who are trying to do the same. Our investment strategies seek to understand and navigate macroeconomic shifts that drive the world's most liquid markets (bonds, currencies, equities, commodities, credit). These shifts have a very limited sample size (some of them haven't yet happened in our lifetimes).

Our approach is to start by digging deep to develop a fundamental cause-effect understanding of the economic and financial relationships that drive markets. We represent this understanding in a model of the world that we call our System—code and algorithms that generate views automatically, by ingesting vast amounts of significantly cleaned data and reflecting the relationships we've learned over decades of intense study and experience. Being systematic allows us to stress-test the quality of our ideas through time, and also helps ensure that at any given point, our positions reflect everything we've ever learned, so that our people can spend all their time focused on compounding on our understanding at a faster rate than markets are learning.

This approach requires us to be at the forefront of human-machine collaboration. Since 2012, Bridgewater has aggressively pursued the vision of the artificial investor that can do everything a human can, with computers not just representing the insights but also generating the insights themselves. In 2023, we introduced AIA (Artificial Investment Associate), our fully machine-powered investing strategy. Our learnings in building AIA are now transforming even our human investors' jobs, allowing us to rapidly discover and systemize new insights, and accelerating our transformation toward a fully integrated system that combines the best of human and machine intelligence.

Every day we obsessively interrogate our systems against our independent investor insights, and work to evolve our systems to reflect how the world is changing. Beating the markets with this approach requires intense collaboration between brilliant people who constantly push themselves and each other to improve every week, to arrive at the best ideas without ego or politics. We rapidly elevate the best thinkers to greater responsibility.

We are looking to hire great talent for our Investment Engineer roles. Does this sound like you?

Investment Engineers are the builders behind how Bridgewater's investment ideas become real, running systems. They are equal parts engineer, architect, and toolmaker—designing, implementing, and scaling the technology that turns research insights into daily investment decisions across global markets. We are looking for people with strong software engineering and systems backgrounds—computer science, machine learning engineering, distributed systems, data engineering, or related fields—who want to apply world-class engineering to one of the hardest and most consequential problem domains in the world.

Who You Are

A builder and an inventor. You don't just want to understand how investment systems work—you want to build them, reinvent them, and own them end-to-end. You're already experimenting with new technologies before most people understand them, because you immediately see how they can solve hard problems. You don't wait for a roadmap; you build the first version yourself. And your inventiveness is grounded in real engineering discipline—you know that production-grade craft is what separates a clever prototype from lasting edge.

A technologist with range and speed. You stay close to the leading edge of software engineering, data infrastructure, and machine learning tooling. You evaluate new technologies with a sharp eye—not chasing hype, but recognizing when a new framework, paradigm, or platform can meaningfully improve how we build. When a problem demands a tool or technique you haven't used before, you pick it up fast and deploy it with confidence.

Analytically sharp, even when your job isn't analysis. You may not be writing the investment logic yourself, but you understand it well enough to implement it faithfully, to spot when something doesn't look right, and to ask the hard engineering questions that surface hidden assumptions or edge cases. You know that the gap between "works in research" and "works in production" is where most value is created—or destroyed.

A pragmatic problem solver with high standards. You bring rigor to every stage of the development lifecycle—from scoping and design through testing and deployment. You know when to build for durability and when to prototype for speed, and you communicate those tradeoffs clearly. You don't gold-plate, but you also don't ship fragile systems into environments where reliability matters enormously.

Deeply collaborative and low-ego. The problems we solve require tight partnership between engineers, researchers, and investors. You translate fluently between these groups—turning abstract investment questions into concrete system requirements, and surfacing technical constraints that reshape how a problem gets framed. You give and receive direct feedback without defensiveness, and you care more about the outcome than about who gets credit.

Driven to understand the domain, not just serve it. You aren't satisfied writing code to spec. You want to understand why a system is built a certain way, what the investment logic is trying to capture, and how the markets it touches actually behave. That curiosity makes you a far better engineer—and over time, a more complete contributor to the investment process.

What You'll Do

Design, build, and own the systems that power our investment process. Our algorithms process vast, diverse data on global economic conditions and translate it into market views and trades every day. You will architect and implement the systems that make this possible—ensuring they are performant, reliable, testable, and built to evolve as our investment thinking advances.

Bridge the gap between research and production. You'll take investment ideas, models, and analytical frameworks developed by researchers and associates and turn them into robust, production-grade systems. This means working closely with researchers to deeply understand intent, designing clean abstractions, building thorough test harnesses, and ensuring that what runs in production faithfully reflects what was designed in research.

Build and evolve our technology platform. Invest in the shared infrastructure, tooling, and frameworks that make the entire team faster and more effective. This includes data pipelines, execution systems, monitoring and observability, backtesting frameworks, and the developer experience that shapes how quickly new ideas can be tested and deployed.

Push the frontier of how we use emerging technology. With the rise of AI-native development tools, large language models, and new paradigms in data processing and systems design, we need engineers who can evaluate these technologies critically and integrate them where they create real leverage—accelerating development velocity, improving system quality, or enabling entirely new capabilities.

Operate, monitor, and continuously improve live systems. Your systems trade global markets every day. You will own their operational health—building the monitoring, alerting, and diagnostic tooling needed to ensure they perform as intended, and driving rapid resolution when they don't. You'll use production behavior as a feedback loop to identify improvements in both the technology and the underlying investment logic.

What You Bring

• Strong software engineering fundamentals—data structures, algorithms, system design, and a track record of building and shipping production systems
• Several years of professional experience in software engineering, infrastructure, data engineering, or ML engineering—in technology, finance, or another demanding environment
• Proficiency in one or more languages commonly used in quantitative systems (e.g., Python, Java, C++) and comfort picking up new tools quickly
• Experience designing systems that handle complex data processing, real-time or near-real-time workloads, or high-reliability requirements
• A strong interest in financial markets, economics, or quantitative investing—you don't need a finance background, but you should be genuinely excited to learn the domain deeply
• A collaborative, low-ego working style and a drive to grow rapidly through direct feedback and hard problems

Compensation

The total compensation range across these roles is $225,000–$450,000 inclusive of base salary and discretionary target bonus. The expected base salary is typically 50%–75% of the relevant range, depending on team, level, and experience.

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