BlackRock
Software Engineer, AI Labs
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Senior Software Engineer, AI Labs – Production DevOps (or equivalent, depending on experience level)
AI Labs Overview
For over 30 years, BlackRock has leveraged technology, data, and analytical insight to improve our client service and business operations. AI Labs is the company’s dedicated hub for integrating machine learning, generative AI, optimization, and statistical methods to address key strategic challenges. By combining human insight with machine intelligence, the team delivers alpha generation, operational efficiency, cost savings, and stronger decision-making across investments, operations, product development, client engagement, and other areas.
The multi-disciplinary team brings together:
- Data scientists & researchers
- Engineers & architects
- Product managers
- Strategists
Specialized expertise spans:
- Machine learning, statistical modeling, and natural language processing (NLP)
- Data visualization and graph analytics
- ETL, data architecture, and responsible AI production
- Supporting global offices in New York, Edinburgh, Atlanta, San Francisco, and Seattle
AI Labs seeks candidates with diverse backgrounds, strong engineering fundamentals, and innovative perspectives to propel BlackRock into the future of AI-driven business solutions.
About the Role
Foremost, this is a role for a software engineer who:
- Thrives in production environments, building reliable systems.
- Collaborates with data scientists and researchers to translate AI prototypes into robust products.
- Does not require deep ML research expertise but must excel as a full-stack production engineer—designing, testing, deploying, and maintaining efficient, scalable components.
As an engineer, you will:
- Work across cloud-native services, ML workflows, internal platforms, and tools supporting real-world problems.
- Build systems from ideation to release, with gravity—both in the structures you devise and in ensuring reliability.
- Own feature development independently, while consulting partners on complex trade-offs.
- Strengthen the team’s engineering judgement, culture, and quality standards.
Core Responsibilities:
- Design, build, test, deploy, and maintain scalable, secure software supporting ML and generative AI products.
- Translate exploratory models and data workflows into production-ready systems with clear ownership.
- Write clean, maintainable, high-quality code that balances performance, security, and maintainability.
- Improve reliability, observability, performance, and usability of existing systems.
- Participate in production support, troubleshooting, and incident reviews to reduce future risks.
- Engage in design discussions, document decisions, and escalate architecturally-relevant risks.
- Contribute to code reviews, CI/CD processes, testing, and team knowledge-sharing.
- Leverage AI-powered development tools to enhance productivity and specification.
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Responsibilities
You will focus on real-world application—not just models—between prototype and scalable production.
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Product Lifecycle Collaboration
- Work with data scientists, researchers, and product teams to operationalize AI features.
- Ensure solutions are efficient, secure, and meet business needs.
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Sure-footed Engineering
- Implement and maintain robust services in cloud environments.
- Monitor, log, and secure systems at scale.
- Troubleshoot efficiently and mitigate incident impacts.
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Normative Leadership in Engineering
- Commit to best practices (e.g., code reviews, SDLC, maintainability).
- Drive team standards (e.g., testing, documentation, performance benchmarks).
- Share expertise and encourage constructive feedback for craft improvements.
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Continuous Innovation
- Evaluate emerging technologies (e.g., ML Ops tools, (AI/ML) hardware optimizations, new SDKs).
- Optimize workflows, reduce technical debt, and enhance the team’s collective workflow.
Requirements
Minimum
- 3+ years of professional software engineering experience shipping code to production environments.
- Strong Python skills and practical SQL expertise, with a data-driven mindset.
- Experience with:
- Building cloud-native applications, services, APIs, or distributed systems.
- Core software engineering fundamentals (e.g., code quality, automated testing, version control, maintainability).
- Self-driven implementation of well-scoped features.
- Diagnostic and problem-solving skills—ability to debug and navigate trade-offs.
- Excellent communicative and collaborative skills with both technical and non-technical stakeholders.
Preferred (Not Exhaustive)
Chauffeured through ETL, ML pipelines, and DevOps:
- Data pipeline frameworks: Spark, Airflow, Dagster, Flyte.
- Cloud infrastructure: containers, pipelines, CI/CD tools (e.g., Kubernetes, Terraform).
- Tools for observability: metrics/logs/tracing/lang-handling (OpenTelemetry, Prometheus, ELK, Grafana).
- Serving ML workloads: GPU/TPU integration, accelerated inference.
- Generation models: LLMs, fine-tuning, evaluation, deployment.
- Platform engineering: developer-facing productivity tools.
- Pipeline and task automation: Airflow, Dagster, workflow orchestration.
- Networking, security: APIs, ID/OAuth, load balancing, gateway integration.
What Success Looks Like
For Us: Quality & Impact
- Deliver scoped features wisely: adopted by users or better team development.
- Ensure production-grade software with high standards of reliability, observability, and secure deployments.
- Convert prototypes to production-ready components without sacrificing value.
- Pair expertise with system thinking: design where trade-offs matter the most.
- Reduce Technical Debt, improve developer experience, and operationalize systems confidently.
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For You: Growth & Culture
- Build a mental model of AI Research → Engineering Lifecycle.
- Advance collaborative skills, communication clarity, and risk communication.
- Grow as an engineering leader, sharing knowledge and reinforcing habits.
- Show responsibility—from contribution and maintenance to mentoring peers.
Our Benefits
Our full spectrum of benefits is designed to support engagement, professional growth, and holistic well-being:
- Financial well-being: Retirement tools, mentorship, educational reimbursement.
- Health & wellness: Comprehensive coverage.
- Family support: Caregiver resources, parental programs for on/off-site support.
- FTO (Flexible Time Off): Balance for teams and personal life.
Our Hybrid Work Model
At BlackRock, hybrid work is both fixed and flexible. Employees must work at least 4 days/month in the office (paring for 1 remote day). This structure encourages productivity and collaboration. The in-person time is intentional—to foster the hip rapport and growth experience one gets from co-location.
- Flexible: Adapt beyond 4-days if required by the role/type/function.
- Intentional Prevalence: Enhances onboarding, skill exchange, and team bonding.
AI Use—A Note for Candidates
AI assists most workflows today. We encourage its thoughtful use in:
- Learning and research prep.
- Organizational efficiency.
But—all assignments/outputs during our interview process must include evidence of original reasoning, thinking, and expertise. While tools may help, demonstrate your interpretative/technical judgement.
Note: Detailed guidance provided on BlackRock’s application platform.
About BlackRock
BlackRock’s mission is to:
[!note] Connect more and more people to financial well-being.
Since 1988, we’ve powered together to help clients—from firms to individuals—plan ahead. With assets facilitating education grants, businesses, and climates transitions, our work embodies ** Waste Not, Want Not**.
Our culture thrives when all thrive: We invest in:
- Diversity, Equity, and Inclusion.
- Networks for people to connect & grow.
- Tools and mentorship to thrive.
See where you can have an impact:
- Explore our jobs https://Careers.BlackRock.com/
Follow us for deeper insights: LinkedIn | Instagram | YouTube | TikTok (X).
BlackRock is an Equal Opportunity Employer—evaluating applications without bias, safeguarding the rights of all to a workplace where individual difference is both celebrated and leveraged.
"Own Well and Grow."
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