Level

BlackRock

Engineering Director, AI Software Engineering – Private Markets

AI in this role

fine-tuningai-safetyai-research

About this role

Your team

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.


Your role and impact

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.


Your responsibilities

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. The 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.

Your experience

  • 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.

Our benefits

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.

Our hybrid work model

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 learn more about BlackRock, please visit Careers.BlackRock.com. We also encourage you to get to know us on LinkedIn, Instagram, YouTube, X, and TikTok.


BlackRock is proud to be an Equal Opportunity Employer.  We evaluate qualified applicants without regard to age, disability, race, religion, sex, sexual orientation and other protected characteristics at law.

How we rate this

Engineering Director, AI Software Engineering – Private Markets at BlackRock rates 86 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.

Prepare for this job

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

Fine TuningAI SafetyAI Research

Questions you could be asked

  1. Walk me through fine-tuning a model: what data did you use, and how did you check the result?
  2. How do you think about the risk of an AI system in this kind of role failing silently?
  3. Tell me about a research question you investigated. What did you find?
  4. How would you decide a model or AI system is ready to ship?
  5. Tell me about a time a model underperformed in production. How did you find out, and what did you change?

Adapt your resume

  • List these exact terms on your resume: Fine Tuning, AI Safety, and AI Research. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
  • Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.

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