Senior Software Engineer, AI & ML, Vice President

Senior Software Engineer – Machine Learning 

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. At BlackRock, 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. 

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 data science, machine learning, generative AI, optimization, stochastic control and statistics. Our mission is to combine human and machine intelligence to revolutionize asset management. The team is led by Dr. Rachel Schutt.  

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 is to apply these same techniques throughout the business. 

The team has grown to 30+ data scientists and data engineers. Working collaboratively, the team is multi-disciplinary with the following skills and capabilities: machine learning, statistical modeling, signal detection, natural language processing, data visualization, network/graph modeling, ETL, data pipelines, data architecture, 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, Atlanta and Seattle. The team has several Stanford professors as senior advisors with world-class expertise in machine learning, statistics, optimization and stochastic control. These advisors include AI Labs co-head Stephen Boyd (Samsung Professor of Engineering at Stanford), Emmanuel Candes, Trevor Hastie, and Mykel Kochenderfer who dedicate time in our Palo Alto office and provide advice and guidance for all members of the global 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. 

Responsibilities 

• Help lead architecture on a multi-discipline, multi-region team of data scientists, engineers, and investment professionals on a corporate-wide set of client, investor, and operational problems 

• Build and maintain tools and services supporting the full model development lifecycle for statistical models, machine learning, optimization, and deep learning models (e.g., feature engineering, backtesting and simulation, validation, deployment) 

• Maintain and monitor production models and experimentation 

• Tune performance in both single-threaded and distributed environments 

• Enforce high-quality patterns and practices for maintaining model pipelines 

Requirements 

• 7+ years in software engineering, with 3+ years in API-backed ML deployment 

• Strong programming language skills in Python 

• Significant experience with SQL (e.g., RDBMS, Spark, Presto, or BigQuery) 

• Experience with machine learning, optimization, and data manipulation tools (e.g., scikit-learn, XGBoost, cvxpy, Pandas, Spark, or PyTorch) 

• Experience with at least one low-level or scientific language (e.g., C, Rust, Go, Julia, or R) 

• Experience with accelerated compute (GPU, TPU, Inferencia) for research and deployment  

• Experience efficiently training and fine-tuning of deep learning models 

• *Bonus* Deployment of enterprise quality model endpoints for high throughput and low latency