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 createmeasurablebusiness 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 withexpertiseacross 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 AtlantaSan Franciscoand Seattle.

We are looking for candidates withdiversebackgrounds strong engineering foundationsand fresh perspectiveswho can helpAI Labs bring best-in-class technologies analytics and insights to the firm and to our clients.

About the role

We are looking forasoftwareengineerto join BlackRock AI Labs. This role is suited to an engineer who enjoys building reliable production systems working closely with data scientists and researchers andhelping turnAI and machine learningprototypesinto robust productsplatforms 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 andoperatewell-scopedproductioncomponents. You will contribute to cloud-native services data and ML workflows internal platforms and tools that help AI Labs deliver impact safely responsiblyand efficiently.

Asanengineer you will be expected to deliver meaningful features and improvements independently while seeking guidance onmorecomplex technical trade-offs.You will build breadth across the AI product lifecyclestrengthen yoursystem design judgement and contribute to the engineering quality and culture of the team.

In this role you can expect to

• Design build test deploy andmaintainsecure 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 andoperate.

• 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 whenappropriate.

• 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 whereappropriate to improve productivity and engineering quality.

Minimumrequirements

• Typically3 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 ormaintainingcloud-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 learnnew technologies.

• Strong communicationand 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 AirflowDagster 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 authenticationauthorisation 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.

• Acceleratedcomputeinfrastructure such as GPUs TPUs or AWSInferentia.

• 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 fromprototypetoward 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 tooperate.

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