Software Engineer, AI Labs

AI Labs Overview

For more than 30 years, BlackRock has used technology, data, and analytical insight to improve how we serve clients and run our business. AI Labs builds on that foundation by applying data science, machine learning, generative AI, optimization, and statistics to strategic challenges across the firm.

AI Labs is a firm-wide hub focused on combining human and machine intelligence to create measurable business impact. Our solutions support alpha generation, operational efficiency, cost reduction, and better decision-making across investments, operations, product, client engagement, and other areas of BlackRock.

The team is multi-disciplinary, bringing together data scientists, engineers, researchers, product partners, and strategists with expertise across machine learning, statistical modelling, natural language processing, data visualization, graph analytics, ETL, data architecture, product delivery, and responsible production deployment.

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

We are looking for candidates with diverse backgrounds, strong engineering foundations, and fresh perspectives who can help AI Labs bring best-in-class technologies, analytics, and insights to the firm and to our clients.

About the role

We are looking for a software engineer to join BlackRock AI Labs. This role is suited to an engineer who enjoys building reliable production systems, working closely with data scientists and researchers, and helping turn AI and machine learning prototypes into robust products, platforms, and services.

You do not need to be a machine learning researcher. We are looking for someone with strong software engineering judgement who can design, implement, test, deploy, and operate well-scoped production components. You will contribute to cloud-native services, data and ML workflows, internal platforms, and tools that help AI Labs deliver impact safely, responsibly, and efficiently.

As an engineer, you will be expected to deliver meaningful features and improvements independently, while seeking guidance on more complex technical trade-offs. You will build breadth across the AI product lifecycle, strengthen your system design judgement, and contribute to the engineering quality and culture of the team.

In this role, you can expect to

• Design, build, test, deploy, and maintain secure, scalable software components that support machine learning and generative AI products in production.

• Own well-scoped features, services, tools, or platform capabilities from implementation through release and operational support.

• Collaborate closely with data scientists, researchers, product partners, and other engineers to translate exploratory AI workflows into reliable production systems.

• Write high-quality, maintainable, well-tested code that is fit for purpose and easy for others to understand, extend, and operate.

• Improve the reliability, observability, performance, security, and usability of existing products, tools, and services.

• Participate in production support, troubleshooting, incident resolution, and follow-up improvements that reduce future operational risk.

• Contribute to technical design discussions, document decisions clearly, and escalate architectural risks or trade-offs when appropriate.

• Participate in code reviews, testing practices, CI/CD workflows, and team engineering processes.

• Proactively contribute to team culture by sharing knowledge, giving constructive feedback, and helping the team improve how it works.

• Use modern engineering tools, including AI-enabled development tools where appropriate, to improve productivity and engineering quality.

Minimum requirements

• Typically 3+ years of professional software engineering experience, including experience shipping code to production environments.

• Strong programming skills in Python and practical experience with SQL.

• Experience building, testing, deploying, or maintaining cloud-native applications, services, APIs, databases, data pipelines, or distributed systems.

• Good understanding of software engineering fundamentals, including code quality, automated testing, version control, code review, and maintainability.

• Ability to independently deliver well-scoped features or components, while seeking guidance on complex design decisions.

• Strong problem-solving skills, with the ability to debug issues, reason through trade-offs, and learn new technologies.

• Strong communication and collaboration skills, including the ability to work effectively with technical and non-technical partners.

Preferred experience

Experience in one or more of the following areas would be helpful, but we do not expect candidates to have all of them:

• Data pipelines, ETL tooling, or workflow orchestration frameworks such as Spark, Airflow, Dagster, or Flyte.

• Cloud infrastructure, containers, deployment automation, CI/CD pipelines, or DevOps practices.

• Observability and production support practices, including metrics, logging, tracing, alerting, and incident follow-up.

• Relational databases, columnar stores, data modelling, data validation, or performance tuning.

• APIs, networking, security, authentication, authorisation, load balancers, or API gateways.

• Machine learning or generative AI product lifecycles, including model serving, evaluation, monitoring, experimentation, or fine-tuning.

• Platform engineering or internal developer tooling that improves the productivity of engineers, data scientists, or researchers.

• Front-end or full-stack development where useful for delivering internal tools or user-facing workflows.

• Accelerated compute infrastructure such as GPUs, TPUs, or AWS Inferentia.

• AI coding assistants or other modern developer productivity tools.

What success looks like

• Reliably deliver well-scoped features, fixes, and improvements that are adopted by users or improve team delivery.

• Build production software that is tested, observable, maintainable, secure, and operationally supportable.

• Help move AI ideas from prototype toward production by translating exploratory workflows into reliable, reusable engineering patterns.

• Develop stronger judgement around system design, trade-offs, reliability, and operational risk.

• Improve existing systems by reducing technical debt, increasing reliability, improving developer experience, or making services easier to operate.

• Communicate clearly, ask good questions, raise risks early, and contribute positively to team culture.