AI Labs Overview  

For more than three decades, BlackRock has combined world-class talent and technology to redefine what's possible in asset management. Today, AI is reshaping every aspect of our industry, from investments and client experiences to operations, risk management, and product innovation. BlackRock AI Labs sits at the center of that transformation. 

AI Labs is BlackRock's advanced AI science and engineering organization. We partner with teams across the firm to solve some of the most complex and consequential challenges in financial services, applying generative AI, machine learning, optimization, and statistics to deliver measurable business impact. Our mission is simple but ambitious: combine human and machine intelligence to revolutionize asset management. 

What sets AI Labs apart is our ability to move from idea to impact. We are not a research lab detached from the business, nor are we a traditional software engineering organization. We are a hybrid team of scientists and engineers who build solutions that run at scale in production environments and create capacity for our business partners. From AI-powered investment insights and risk management solutions to next-generation agentic workflows and enterprise AI platforms, our work is helping to shape the future of BlackRock. 

Our teams work on some of the firm's highest priority strategic initiatives, including large-scale generative AI systems, intelligent agents, predictive analytics, optimization engines, operational risk solutions, and AI platforms that accelerate innovation across BlackRock. We help define best practices, establish technical standards, and enable teams throughout the organization to adopt AI safely, effectively, and at scale. 

AI Labs has offices in New York, Edinburgh, Atlanta, San Francisco and Seattle. 

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. 

In this role, you can expect to: 

• Provide technical leadership, set architectural direction, and drive key design decisions across multidisciplinary teams.  

• Design, build, and operate production AI/ML systems across areas such as generative AI, intelligent agents, predictive modeling, and optimization.  

• Develop reusable AI platforms, services, and infrastructure that accelerate experimentation, deployment, monitoring, and adoption across the firm.  

• Translate emerging AI capabilities into robust, scalable solutions that deliver measurable business impact.  

• Define evaluation, testing, and observability approaches for AI systems, including non-deterministic and agentic applications.  

• Optimize AI systems and workloads for latency, throughput, compute efficiency, reliability, security, and cost.  

• Partner with business, engineering, data, risk, and control teams to take solutions from concept through production deployment and ongoing operation.  

• Establish engineering standards, mentor engineers, and influence technical strategy across teams. 

The minimum requirements for this position are: 

• 12+ years of software engineering experience, including significant experience designing, building, and operating complex production systems. 

• 6+ years of experience building and deploying AI/ML, optimization, or other model-driven applications in production. 

• Strong proficiency in Python and a track record of developing maintainable, well-tested production software. 

• Strong SQL skills and experience working with data-intensive applications and large-scale datasets. 

• Experience designing and building APIs, services, data pipelines, or distributed systems that support AI/ML applications. 

• Experience with containerization and orchestration technologies, such as Docker and Kubernetes. 

• Demonstrated ability to lead complex technical solutions from architecture and design through production deployment, operation, and continuous improvement. 

• Strong problem-solving, collaboration, and communication skills, with the ability to influence technical decisions across multidisciplinary teams. 

Additional knowledge and experience we value includes: 

• Experience collaborating with research teams in fast-moving environments, ideating quickly on research prototypes. 

• Generative AI, large language models, AI agents, and architectures for production AI systems. 

• Evaluation, experimentation, observability, debugging, and testing approaches for AI/ML systems, including non-deterministic and agentic applications. 

• Machine learning and optimization frameworks such as PyTorch, TensorFlow, or JAX. 

• Distributed systems, cloud infrastructure, model serving, orchestration, data pipelines, and API design. 

• Performance optimization across latency, throughput, compute utilization, reliability, scalability, and cost. 

• Responsible AI, security, governance, and controls in enterprise or regulated environments. 

• Experience with a systems- or performance-oriented programming language such as Rust, C++, C, Go, or Zig. 

• Experience in financial services or another regulated industry.