At BlackRock, technology has always been at the core of what we do – and today, our technologists continue to shape the future of the industry with their innovative work. We are not only curious but also collaborative and eager to embrace experimentation as a means to solve complex challenges. Here you’ll find an environment that promotes working across teams, businesses, regions and specialties – and a firm committed to supporting your growth as a technologist through curated learning opportunities, tech-specific career paths, and access to experts and leaders around the world.

AI Labs Overview

Since our founding over 30 years ago, BlackRock has brought together great minds to drive innovation. From the beginning, we have been demonstrating technology for insight and efficiency to make our business better and to help clients realize the objectives they desire. BlackRock manages ~$10+ trillion in assets on behalf of investors worldwide. As such, there is a rich problem space for data scientists and engineers across all areas of the business including investments, sales, marketing, operations, product, UX, etc. and the potential to have large scale impact. In 2018 BlackRock accelerated innovation and technology again with additional investment in artificial intelligence and data science and all the potential they represent. 

The AI Labs was formed to act as a central hub with a firm-wide remit to solve strategic business challenges for the firm by bringing to bear our expertise in machine learning, artificial intelligence, data science and optimization. Our mission is to combine human and machine intelligence to revolutionize asset management. The team is led by Dr. Rachel Schutt, and co-head Professor Stephen Boyd, Samsung Professor of Engineering at Stanford.  

We are a hybrid research and product team and have a responsibility to drive towards commercial impact in the form of alpha generation, operational efficiencies, and cost reduction. Building on the success of systematic investment teams with a history of demonstrating machine learning at BlackRock to create alpha, the goal was to apply these same techniques throughout the business. 

The team has grown to 75+ data scientists and engineers. Working collaboratively, the team is multi-disciplinary with the following skills and capabilities: machine learning, optimization, statistical modeling, signal detection, natural language processing, data visualization, generative AI,  network/graph modeling, ETL, data pipelines, data architecture, ML engineering, communication, product management and strategy. We work with data from a wide variety of sources including text, news feeds, financial reports, time series transactions, user behavior logs, and real-time data. 

AI Labs has offices in New York, Palo Alto, Edinburgh, San Francisco, Atlanta and Gurgaon. The team has several Stanford professors as senior advisors with world-class expertise in machine learning, statistics, optimization and stochastic control. These advisors include Stephen Boyd, Emanuel Candes, Trevor Hastie, and Mykel Kochenderfer who dedicate time in our Palo Alto office and provide advice and guidance for the team. 

We are looking for candidates with unique backgrounds and diverse skill sets with fresh perspectives to accelerate and amplify our efforts to make an impact at BlackRock. AI Labs aims to bring best of class technologies, analytics, and insights to the entirety of the firm and to our clients utilizing data from a wide variety of sources including text, news feeds, financial reports, time series transactions, logs, imagery, and real-time data.

In this role, you can expect to:

• Design and deliver secure and scalable solutions to enable the rapid delivery of machine learning products to production environments

• Continuously improve existing products, tools, and services, by maintaining close relationships with data scientists, researchers, and other BlackRock users

• Write quality maintainable code that is comprehensively tested and fit for the intended objective

• Provide ongoing maintenance and support for internal tools and services, including troubleshooting and resolution of production incidents

• Advise data scientists and researchers on product architecture and system design, and collaborate on complex deliverables

• Participate in team agile processes, and proactively contribute to team culture

The minimum requirements for this position are:

• Experience shipping code to production environments

• Strong programming language skills in Python and SQL

• Experience building cloud native applications

• Great problem-solving and communication skills

Knowledge and experience we are looking for includes:

• DevOps automation, idempotent deployment testing, and continuous delivery pipelines

• Networking and security protocols, load balancers, API Gateways

• ETL tooling and workflow engines (e.g., Spark, Airflow, Dagster, Flyte)

• Data modeling, and strategies for cleaning and validating data at scale

• Performance tuning on RDBMS or Big Data tools for row- and column-oriented use-cases

• Telemetry and related tools (metrics, event logging, tracing, etc.)

• Ability to traverse full stack