Head of AlphaGen - Investor Research Product Engineering, Director

BlackRock • London, CA

1d • $225,000 - $285,000 • Hybrid

About The Position

About this role Role Overview: We are seeking a visionary, strategic, and execution-oriented leader to serve as the Head of AlphaGen Investor Research Product Engineering. This role centralizes end-to-end ownership of the AlphaGen’s research product estate spanning research lifecycle with an explicit focus on eliminating fragmented operational burden and establishing a single accountable organization for AlphaGen’s modernization and future growth. You will drive a unified product and transformation strategy that accelerates evolution toward a cloud-native, AI-first research ecosystem. Key initiatives include retiring legacy tooling, modernizing core runtimes, and creating scalable self-service workflows that shorten research-to-production from months to hours. This transformation enables AlphaGen’s Investor Research Solutions & Services to focus on high-value quantitative partnership with the Portfolio Management Group (PMG), while Investor Research product (under your leadership) provides a robust foundation, reusability, and productized research capabilities to support and scale alpha generation efforts. AlphaGen Investor Research Product is PMG’s strategic research-technology engine. In this role, you will lead the organization responsible for delivering a cloud-native, AI-powered, self-service suite of products that accelerates the end-to-end lifecycle of data, signals, and models all while working closely with the Alphagen platform team in providing the operational backbone required for reliability, resiliency, security, and scale.

Requirements

• Education: Bachelor’s degree in Computer Science, Engineering, or a related field; or equivalent practical experience. An advanced degree is a plus.
• Strategic Engineering Leadership: Proven experience leading large-scale engineering organizations through product transformation initiatives (e.g., platform modernization, cloud migration, operating model changes). Demonstrated success in setting vision and executing across complex, multi-year technology programs.
• Technical Depth (Strong understanding of): Alpha generation workflows or closely related quantitative research processes in finance/investments.
• Cloud technologies and cloud-native architectural patterns (e.g., microservices, containerization, distributed computing e.g. Ray).
• API-first platform design and developer experience best practices.
• AI/ML applications for workflow automation, data validation, and operational excellence Modern stack knowledge and skills – e.g. Python, Polars, Ray, MLFlow or equivalent technology
• Collaboration & Influence: Demonstrated strength in partnership-building and cross-functional collaboration. Able to influence and align product teams, platform/infrastructure teams, and governance bodies around a common vision and roadmap.
• Execution in Complexity: Ability to operate in complex market and organizational contexts, translating strategic objectives into actionable plans. Proven track record of driving innovation, navigating ambiguity, and delivering measurable outcomes at scale.
• Leadership Attributes: This leader exemplifies operating principles that set the tone for how the team works and evolves: AI-First, Human-in-the-Loop: Embraces AI-driven solutions as a default for efficiency and scale, while keeping expert human judgment in critical loops to ensure quality and trust.
• Platform Over People: Prioritizes building robust platforms and automation over relying on individual heroic efforts, enabling sustainable and scalable growth.
• Single Front Door: Advocates for a unified user experience where researchers and stakeholders have one intuitive portal for all their needs, simplifying access and support.
• Standardization Everywhere: Drives consistency in tools, processes, and data across the board, reducing complexity and improving interoperability and maintainability.
• Self-Service as the Default: Champions self-service capabilities, empowering users to accomplish tasks independently (from data ingestion to model deployment) without bespoke engineering work.
• Deep Co-Ownership with PMG: Fosters a culture of partnership with the Portfolio Management Group, aligning closely on priorities, sharing roadmaps and KPIs, and co-owning outcomes to ensure the platform truly meets investment teams’ needs.

Responsibilities

• Strategic Leadership & Transformation Execution Strategy Development: Develop and execute a cohesive engineering strategy that aligns AlphaGen’s product direction with major transformation initiatives (e.g., cloud migration, AI-first workflows, standardization, legacy system retirement).
• Clear Ownership: Establish clear product ownership, accountability, and governance across the entire AlphaGen investor research product estate
• Partnership Building & Cross-Functional Collaboration Cross-Organization Partnerships: Forge and nurture strong partnerships across stakeholders, broader Aladdin product and platform engineering organizations to drive shared roadmaps, shared outcomes, and frictionless execution of initiatives.
• Governance & Standards: Strengthen collaboration with internal governing bodies and central teams to ensure consistent standards, regulatory compliance, and seamless integration with broader enterprise systems.
• User Engagement & Adoption (Self-Service by Default) User-Centric Design: Drive user adoption strategies in partnership with product management and key stakeholders, creating a “single front door” experience for researchers to access data, tools, documentation, and support covering spectrum of research personas.
• Frictionless Pipeline: Deliver a frictionless research-to-production pipeline by productizing building blocks to enable data onboarding, signal development, model validation, production release, thereby enabling researchers to go from idea to production with minimal hand-offs.
• Product Engineering Leadership (What Researchers Use) Core Frameworks & SDKs: Own and evolve the core libraries, frameworks, and SDKs that enable consistent signal and model development (e.g., standardized APIs, templates, archetypes), ensuring researchers have a cohesive development experience.
• AI-Enabled Tooling: Lead development of AI-powered research tools and automation (spec-to-signal workflows, validation agents, automated quality checks) to shift scaling from people-driven processes to platform-driven capabilities.
• Model Lifecycle Governance: Own end-to-end model lifecycle governance tooling, from experiment to validation to production, including configuration management, metadata tracking, reproducibility, and control frameworks.
• Product Infrastructure Leadership (Systems the Platform Runs On) Foundational Systems Collaboration: collaborate with the Alphagen platform team to help optimize the foundational systems that power AlphaGen’s products, including:
• Compute & Runtime: scalable GPU/CPU execution environments, define efficient scaling policies, drive performance optimizations, and manage resource allocation models for diverse research workloads.
• Observability & Operations: end-to-end telemetry and monitoring, enable proactive incident detection, and automate recovery mechanisms to ensure high platform uptime and resiliency.
• DevOps & CI/CD: Champion infrastructure-as-code, robust deployment pipelines, and standardized build/test/release workflows to accelerate delivery and improve reliability of platform updates.
• Reliability & Production Standards: apply resiliency patterns, and harden systems to enterprise production standards for security and stability.
• Operational Automation: Expand automation for operational tasks (data quality checks, pipeline health monitoring, backfills, change management) to reduce manual intervention and error risk.
• Legacy Modernization & Tech Stack Evolution Cloud Migration: key player in the end-to-end product modernization journey, including the migration from legacy systems to cloud-native architectures, and the timely retirement and consolidation of legacy tools.
• Next-Gen Platform Architecture: Drive an API-first, cloud-native platform design to improve scalability, performance, and interoperability across the ecosystem. Leverage AI/ML where it materially improves speed, quality, and control (e.g., intelligent automation, anomaly detection).
• Market and Business Insight / “North Star” Alignment Industry & Internal Insight: Stay attuned to market trends and internal demand signals, such as the rising need for adaptable alpha-generation platforms and AI-driven research capabilities.
• Define the North Star: Partner with senior leadership to define and communicate a clear business “North Star” for AlphaGen Product Engineering — reduce operational barriers, enable rapid iteration, scale platform services, and ultimately capture more alpha opportunities for the firm.

Benefits

• employees are eligible for an annual discretionary bonus, and benefits including healthcare, leave benefits, and retirement benefits.
• strong retirement plan
• tuition reimbursement
• comprehensive healthcare
• support for working parents
• Flexible Time Off (FTO)

What This Job Offers

• Job Type: Full-time
• Career Level: Director
• Number of Employees: 5,001-10,000 employees