Senior Applied AI Researcher, Vice President

About this role

We are looking for a Senior Applied AI Researcher (6+ years of experience) to join our data science team working on advanced AI-driven solutions. This is primarily an individual contributor role, with responsibility for owning end-to-end ownership of complex modelling / AI problem areas and technical ownership of AI capabilities critical to the product.

The role focuses on the research, prototyping, evaluation, and improvement of AI solutions, with hands-on work across LLM-based systems, including agent-style workflows and retrieval-augmented generation (RAG) where appropriate. You will work end-to-end: collaborating with stakeholders and product managers to define problems, building and validating prototypes, presenting findings to diverse audiences, and supporting engineering teams during implementation and production rollout.

Key ResponsibilitiesEnd-to-end AI solution ownership

Own AI projects or functional modules from problem definition through prototype validation and production support.

Partner with product managers and business stakeholders to translate real-world problems into clearly scoped data science and AI initiatives.

Independently plan and execute research, experimentation, and iteration cycles in ambiguous problem spaces.

Design AI solutions with a system‑level perspective, ensuring scalability, maintainability, and long‑term sustainability.

Applied AI, LLMs, and agentic systems

Design and prototype LLM-powered solutions, including RAG-based systems and agent-like workflows (e.g. tool use, orchestration, multi-step reasoning).

Contribute to defining system behavior, scope, and constraints, with attention to quality, robustness, and operational considerations.

Stay current with emerging AI techniques and apply them pragmatically to solve business problems.

Evaluation, validation, and performance improvement

Build and maintain evaluation frameworks to assess AI system performance (accuracy, reliability, relevance, robustness, safety).

Develop quantitative and qualitative metrics, benchmarks, and testing approaches to validate prototypes and track improvements.

Analyze existing solutions to identify gaps and drive continuous, data-driven performance enhancements.

Collaboration and communication

Work closely with data scientists, engineers, and product teams to ensure smooth transition from prototype to production.

Clearly communicate methods, assumptions, results, and limitations to technical and non-technical audiences.

Support engineering teams during implementation by clarifying evaluation criteria, edge cases, and expected system behavior.

Serve as a technical authority and actively mentor junior data scientists, shaping best practices in experimentation, evaluation, and AI system design.

Ways of working

Contribute actively within an Agile / SCRUM development environment.

Apply good engineering hygiene in research and prototype code to enable reproducibility and collaboration.

Required Qualifications

6+ years of experience in data science, applied machine learning, or a closely related role.

Strong mathematical, statistical, and machine learning foundations, including probability, statistics, optimization, and model evaluation.

Proven ability to select, apply, and critically evaluate ML models and algorithms for real-world problems.

Strong Python skills for analysis, modelling, experimentation, and prototyping.

Strong SQL skills for data exploration, transformation, and analytical workflows.

Excellent analytical thinking and problem-structuring abilities; comfort operating independently with loosely defined goals.

Experience using Git for version control and collaborative development.

Strong English communication skills, both written and verbal.

Preferred Qualifications (Strong Plus)

Hands-on experience with LLMs, including prompt/system design and building real-world applications.

Experience with RAG systems, including retrieval strategies, chunking, evaluation, and performance tuning.

Experience designing or contributing to agent-style AI systems and familiarity with concepts such as agent evaluation, guardrails, and reliability testing.

ML modeling experience (e.g. supervised learning, ranking, classification) beyond exploratory analysis.

Understanding of software engineering best practices, including testing strategies and CI/CD concepts.

Experience working in Azure or similar cloud environments.

Familiarity with Snowflake (and optionally Snowflake AI) as part of a modern data stack.

Experience collaborating closely with domain experts; financial domain exposure is a plus but not required for strong technical candidates.

What Success Looks Like in This Role

You independently deliver high-quality AI prototypes and evaluation frameworks for a defined subdomain or application module.

You proactively define scope, success metrics, and experimentation plans, helping shape architecture and design decisions.

Your evaluation approaches enable reliable comparison, regression prevention, and continuous improvement of AI solutions.

Engineering teams can confidently productionize your work thanks to clear designs, metrics, and collaboration.

Over time, you raise the technical maturity of AI development and evaluation practices within the team.

The salary range for Hungary is 20100000 - 32400000 HUF

Additionally, employees are eligible for an annual discretionary bonus, and benefits including healthcare, leave benefits, and retirement benefits. BlackRock operates a pay-for-performance compensation philosophy and your total compensation may vary based on role, location, and firm, department and individual performance.

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

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