Engineering Director, AI Software Engineering - Private Markets

BlackRock, Inc.; United States; Hybrid; USD 240,000 - 360,000; Full time

Benefits offered by this job

Education reimbursement

Flexible Time Off

Health and well-being programs

Job summary

BlackRock seeks an Engineering Director to lead multidisciplinary AI/ML engineering for private-markets data and workflows. You will shape strategy, architecture, and production systems while partnering with researchers, data specialists and product teams.

You will drive governance, quality, and measurable customer value, overseeing end-to-end AI products and complex platform initiatives in a hybrid work model with strong executive collaboration.

Qualifications

• Extensive experience designing and delivering production AI/ML systems across domains.
• Strong leadership of multidisciplinary engineering or applied-AI organizations.
• Deep grounding in document intelligence, unstructured-data processing, knowledge graphs and data-quality engineering.

Responsibilities

• Define and own the AI engineering strategy and roadmap for private-markets data, research, quality and workflow apps.
• Build and lead forward-deployed engineering teams working with researchers, data specialists and product teams.
• Direct applied AI research and model improvement across adaptation, fine-tuning, retrieval and knowledge systems.
• Lead end-to-end agentic and full-stack AI products integrating models, APIs and user experiences.
• Establish evaluation and trust through datasets, testing, provenance and auditable controls.
• Own production outcomes across architecture, delivery, reliability and adoption.

Skills

AI engineering leadership

Forward-deployed engineering

Architectural strategy

Cross-functional communication

Talent development

Tools

APIs design

Job description

The Private Markets engineering team builds artificial intelligence (AI) capabilities that transform fragmented and unstructured information across documents, filings, websites, spreadsheets, contributed data and communications into structured, connected and decision-ready intelligence. Working alongside private-markets researchers, data specialists, product managers and business leaders, the team combines AI, domain expertise, strong data foundations and human judgment to create secure, scalable and trusted products. The team works across multimodal document understanding, model adaptation, agentic research, entity resolution, knowledge graphs, data validation and enrichment, human-in-the-loop research, AI-assisted outreach and full-stack applications. It also develops reusable engineering foundations that can support multiple private-markets workflows, including fundraising, due diligence, asset allocation, market analysis, portfolio monitoring and business development.

As Engineering Director, you will lead a multidisciplinary, forward-deployed engineering organization spanning AI engineers, machine-learning researchers, forward-deployed engineers and full-stack product engineers. This is a player-coach leadership role combining technical direction, architecture, product strategy, direct engagement with users and organizational leadership. You will shape how AI is applied across private-markets research and data management, taking capabilities from problem discovery and applied research through evaluation, governance and production. You will make informed choices among model adaptation, fine-tuning, retrieval, deterministic services, agentic orchestration and conventional software engineering, while ensuring that solutions deliver measurable improvements in data quality, research speed, coverage, adoption, reliability and customer value. In every role at BlackRock, you'll be expected to apply sound judgement and critical thinking to solve complex problems, adapt as the business evolves, and combine the curiosity to explore new approaches and technologies with the rigor to challenge the results.

Scope of this role also includes the following responsibilities:

• Define and own the AI engineering strategy and roadmap for private-markets data, research, quality and workflow applications, making clear architecture and investment choices across model adaptation, retrieval, agentic orchestration and conventional software engineering.
• Build and lead forward-deployed engineering teams that work directly with researchers, data specialists and product teams, taking high-value problems from discovery through governed production while converting reusable patterns into shared capabilities.
• Direct applied AI research and model improvement across adaptation, fine-tuning, multimodal reasoning, retrieval and knowledge systems, using transparent trade-offs and structured human feedback to improve measurable product outcomes.
• Lead end-to-end agentic and full-stack AI products that integrate models, orchestration, data, application programming interfaces (APIs) and intuitive user experiences, with defined authority boundaries, meaningful human control and appropriate escalation.
• Establish evaluation and trust as core engineering disciplines through representative datasets, regression testing, explicit quality standards, provenance, observability and auditable controls designed with risk, privacy, information security, legal and compliance partners.
• Own production outcomes across architecture, delivery, reliability and adoption, connecting technical measures to customer and business outcomes and maintaining rigorous standards for testing, security, observability and operational ownership.
• Build an inclusive, high-performing engineering organization by recruiting and developing technical talent, creating durable teams and leaders, scaling expertise through reusable standards and representing the function credibly with senior leaders, clients and technical communities.
  Extensive experience designing and delivering production AI and machine learning (ML) systems, with strong technical depth in foundation and multimodal models, retrieval, model adaptation, agentic architectures and rigorous evaluation of probabilistic systems.
• Proven success working directly with users or customers to turn ambiguous problems into production AI products, with strong full-stack engineering judgment and an ability to convert domain solutions into reusable capabilities and make pragmatic build, buy, partner and reuse decisions.
• Strong grounding in document intelligence, unstructured-data processing, entity resolution, semantic retrieval, knowledge graphs, provenance and data-quality engineering, including systems where domain experts provide ground truth and structured feedback.
• Significant experience leading multidisciplinary engineering or applied-AI organizations, setting technical direction across teams, developing senior talent and influencing product strategy, investment priorities and operating models across organizational boundaries.
• Strong understanding of private markets, alternative investments, financial data or adjacent institutional-investment workflows is highly desirable, with familiarity across investor, fund-manager, fund, company, deal or portfolio workflows advantageous.
• Exceptional communication and sound judgment, with the ability to explain complex AI trade‑offs to technical, business and executive audiences and a commitment to responsible AI, privacy, security, provenance, collaboration, customer value and accountable human decision‑making.
  BlackRock's hybrid work model is designed to enable a culture of collaboration and apprenticeship that enriches the experience of our employees, while supporting flexibility for all. Employees are currently required to work at least 4 days in the office per week, with the flexibility to work from home 1 day a week. Some business groups may require more time in the office due to their roles and responsibilities. We remain focused on increasing the impactful moments that arise when we work together in person - aligned with our commitment to performance and innovation. As a new joiner, you can count on this hybrid model to accelerate your learning and onboarding experience here at BlackRock.

Guidance on AI use for candidates

At BlackRock, AI has long been part of how we work - enhancing decision-making, improving operations, and helping us deliver better outcomes for clients. We encourage candidates to use AI thoughtfully to learn, prepare, and work more effectively; but during our interview process, we want to focus on getting to know you through your own experiences, thinking, and judgment. To support you, we've provided guidance on when and how to use AI during our hiring process so you can approach each step with confidence and showcase your best self.

About BlackRock

At BlackRock, we are all connected by one mission: to help more and more people experience financial well-being. Our clients, and the people they serve, are saving for retirement, paying for their children's educations, buying homes and starting businesses. Their investments also help to strengthen the global economy: support businesses small and large; finance infrastructure projects that connect and power cities; and facilitate innovations that drive progress. This mission would not be possible without our smartest investment - the one we make in our employees. It's why we're dedicated to creating an environment where our colleagues feel welcomed, valued and supported with networks, benefits and development opportunities to help them thrive.

To help you stay energized, engaged and inspired, we offer a wide range of employee benefits including: retirement investment and tools designed to help you in building a sound financial future; access to education reimbursement; comprehensive resources to support your physical health and emotional well-being; family support programs; and Flexible Time Off (FTO) so you can relax, recharge and be there for the people you care about.