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 Responsibilities:

End-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.

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.

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.

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You'll build the data pipelines that ingest legal sources, the model stack that produces structured evidence, the evaluation frameworks that measure accuracy, and the fine-tuning loops that improve performance. The unusual constraint: you need speed, scale, correctness, and robustness simultaneously , at millisecond latency, zero downtime, heading toward billions of transactions where a single error costs a customer $20K.

Within weeks:

• Lead development of new features aimed at increasing TRAM’s test-time accuracy
• Work on the underlying data and retrieval pipelines that help power our AI workflows
• Work directly with our internal tax experts to understand how TRAM can better reason like them

Within months:

• Own TRAM’s eval framework and workflows
• Work directly with leading frontier labs to reinforce fine tune models on our proprietary data

Requirements:

• Prior experience building AI enabled products, particularly RAG systems
• Experience fine tuning base models, ideally via RFT
• Willingness to dive into tax technical problems - if you aren’t willing to dive deep on how the model should reason through the tax research process you won’t be effective
• A strong understanding of how LLMs and reasoning models function

Nice to haves:

• Experience working with LLMs on legal applications
• Experience with RAG data pipelines and collecting/curating data for the pipeline

Who you are:

• You'll thrive here if: you're a dog, early stage is in your bones, you own it end to end, you believe speed and accuracy are both possible, and being in the room is a feature, not a cost.
• This won't be a fit if: you need structure handed to you or ambiguity feels draining rather than motivating, you want to manage people more than own hard problems, you're used to 'good enough' shipping, or being in the room five days a week feels like a cost instead of a benefit.

Compensation Range: $250K - $300K

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As a Machine Learning / AI Engineer, you will design, build, and productionize ML models for Search, Discovery, Ranking, Retrieval-Augmented Generation (RAG), and generative AI features. You will also build and maintain scalable data pipelines to collect high-quality training and evaluation datasets, including annotation systems and human-in-the-loop workflows.

You will collaborate closely with engineers, researchers, designers, and product managers across multiple teams to deliver high-quality ML-driven features and infrastructure. This is a high-impact, cross-functional role where you will shape both foundational systems and user-facing capabilities.

Key responsibilities include:

• Design, build, and productionize ML models for Search, Discovery, Ranking, Retrieval-Augmented Generation (RAG), and generative AI features.
• Build and maintain scalable data pipelines to collect high-quality training and evaluation datasets, including annotation systems and human-in-the-loop workflows.
• Collaborate with AI researchers to iterate on datasets, evaluation metrics, and model architectures to improve quality and relevance.
• Work with product engineers to define and deliver impactful AI features across Figma's platform.
• Partner with infrastructure engineers to develop and optimize systems for training, inference, monitoring, and deployment.
• Explore new ideas at the edge of what's technically possible and help shape the long-term AI vision at Figma.

Requirements include:

• 5+ years of industry experience in software engineering, with 3+ years focused on applied machine learning or AI.
• Strong experience with end-to-end ML model development, including training, evaluation, deployment, and monitoring.
• Proficiency in Python and familiarity with ML libraries like PyTorch, TensorFlow, Scikit-learn, Spark MLlib, or XGBoost.
• Experience designing and building scalable data and annotation pipelines, as well as evaluation systems for AI model quality.
• Experience mentoring or leading others and contributing to a culture of technical excellence and innovation.

Preferred qualifications include:

• Familiarity with search relevance, ranking, NLP, or RAG systems.
• Experience with AI infrastructure and MLOps, including observability, CI/CD, and automation for ML workflows.
• Experience working on creative or design-focused ML applications.
• Knowledge of additional languages such as C++ or Go is a plus, but not required.
• A product mindset with the ability to tie technical work to user outcomes and business impact.
• Strong collaboration and communication skills, especially when working across functions (engineering, product, research).

At Figma, one of our values is Grow as you go. We believe in hiring smart, curious people who are excited to learn and develop their skills. If you're excited about this role but your past experience doesn't align perfectly with the points outlined in the job description, we encourage you to apply anyways. You may be just the right candidate for this or other roles.

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With tools that amplify your impact and a culture that backs your ambition, you won't just contribute. You'll make things happen–fast.

The Opportunity:

ZoomInfo built the industry's most sophisticated GTM data acquisition infrastructure. Now we're applying that same rigor to context engineering,the emerging discipline that determines whether AI systems deliver transformative value or incremental improvement.

This role architects the context layer powering our AI intelligence across Copilot, GTM Studio, and MarketingOS. You'll transform how ZoomInfo's agentic workflows access, compress, and deliver precisely the right information at exactly the right moment.

The impact is organization-wide: every AI interaction, every intelligent recommendation, every autonomous agent action depends on the context infrastructure you’ll build.

We've transitioned to AI-first product thinking company-wide. The context pipelines exist but remain nascent,creating a rare opportunity to define architectural patterns and platform standards that compound value across multiple product teams in the years to come.

What You'll Do:

Architect Context Acquisition Pipelines

Design and optimize how ZoomInfo retrieves, transforms, and delivers context from our semantic data layer, memory systems, and data producers. You'll balance retrieval quality against latency and cost constraints, implementing hybrid search strategies, intelligent caching, and context compression techniques that maintain information density while respecting token budgets.

Own the Context Layer Platform

Build infrastructure serving multiple product teams,Copilot, GTM Studio, MarketingOS,as internal customers. Establish API contracts, developer experience standards, and integration patterns that accelerate feature velocity.

Maintain the delicate balance between providing flexible building blocks and opinionated solutions that encode best practices.

Drive Quality Through Measurement

Implement evaluation frameworks using RAGAS metrics and custom benchmarks. Monitor retrieval precision, context relevance, hallucination rates, and system performance in production.

Translate quality signals into architectural improvements, working closely with ML engineers to iterate on embedding models, reranking strategies, and retrieval algorithms.

Navigate Emerging Research

Context engineering evolves weekly. You'll continuously evaluate innovations,GraphRAG for multi-hop reasoning, test-time compute scaling, multimodal retrieval, compression techniques,determining which advances warrant production investment versus which remain academic curiosities.

Bring external best practices to ZoomInfo while contributing learnings back to the broader community.

Orchestrate Cross-Functional Execution

Translate between three distinct worlds: ML engineers optimizing retrieval algorithms, platform engineers building scalable infrastructure, and product teams shipping customer features.

Establish communication cadences, prioritization frameworks, and decision-making processes that balance urgent requests against strategic platform development.

What You’ll Bring:

• 4-6 years of product management experience with 2+ years in ML/AI infrastructure

• Direct experience with production RAG systems, vector databases, or semantic search, context management

• Experience with graph databases (e.g. Neo4j)

• Track record building platform products serving multiple internal or external customers

• Familiarity with context compression, embedding models, and retrieval evaluation frameworks

• History of defining product vision in nascent technical domains where best practices are still emerging

Who You Are:

Technical Foundation

Expert-level understanding of RAG system architecture,you can discuss embedding dimensionality trade-offs, vector database indexing strategies, and reranking approaches with depth.

You've built or significantly contributed to production retrieval systems, not just managed them at arm's length.

Python and SQL proficiency enables you to review code, analyze retrieval issues, and prototype solutions for concept validation.

Platform Product Mindset

Experience building infrastructure products where internal engineering teams are your customers.

You measure success through downstream product velocity improvements and developer satisfaction scores, not just uptime metrics.

You understand platform economics,how each additional team using your infrastructure increases its value through shared learnings and amortized costs.

Intellectual Velocity

You read recent research papers from arXiv, ACL, NeurIPS.

You prototype emerging techniques to understand their practical constraints.

You maintain strong opinions weakly held, updating your architectural assumptions as evidence accumulates.

The discipline moves too fast for static expertise,continuous learning is non-negotiable.

Strategic Communication

You translate between technical depth and business impact fluently.

You can explain to executives why implementing GraphRAG takes 6 months but unlocks $10M in product capabilities.

You can communicate to engineers why business constraints require shipping 'good enough' in 3 weeks rather than 'optimal' in 3 months.

You influence without formal authority through data, clear reasoning, and earned credibility.

The Environment:

Reporting & Collaboration

Report to the Senior Product Director for Context Engineering, Semantic Data Layer, and Agentic Memory within ZoomInfo's Intelligence team.

Work alongside PMs responsible for signals and ML scoring/recommendation models.

Together, you ensure our agentic workflows fill context windows with high-quality, information-dense content exactly when needed.

Pace & Problems

Fast-moving engineering team that understands the space.

Company-wide AI adoption push creates both urgency and opportunity.

Expect interesting problems: How do we maintain sub-200ms retrieval latency at scale?

When does GraphRAG justify its indexing cost?

How do we balance context freshness with cache efficiency?

You'll shape answers that become architectural patterns across the organization.

Impact

Define a nascent discipline at a company that's already AI-first in product thinking and organizational structure.

Your architectural decisions compound,every improvement to context quality multiplies across Copilot, GTM Studio, MarketingOS, and future products we haven't imagined yet.

This is infrastructure work with direct line-of-sight to customer value.

#LI-PS1 #LI-remote

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Responsibilities:

• End-to-End Product Ownership: Define and own the product vision, strategy, and roadmap for Clinical AI tools with no PM layer above you.
• RAG Pipeline Design & Iteration: Architect, implement, and continuously improve the RAG infrastructure powering clinical decision support: chunking strategies, embedding models, vector database design, retrieval and reranking approaches, and evaluation frameworks.
• Prompt Engineering & Model Behavior: Design and iterate on prompt strategies, system instructions, and guardrails to produce reliable, clinically appropriate outputs.
• AWS Infrastructure: Deploy and maintain scalable, HIPAA-compliant AI infrastructure on AWS.
• Clinical Data & Integration: Work with healthcare data sources (EHR-adjacent, structured and unstructured clinical content) and integrate AI outputs into clinical workflows in ways that are accurate, auditable, and safe.
• Clinical Stakeholder Partnership: Own the relationship with clinicians and clinical operations directly , there is no PM intermediary.
• Technical Collaboration: Partner with backend and frontend engineers to integrate AI capabilities into the broader product.
• Staying Current: Actively track developments in applied AI research, RAG techniques, and agentic workflow design.
• Stakeholder Communication & Alignment: Translate technical AI concepts and tradeoffs for clinical and operational stakeholders.

Requirements:

• 5–8 years of experience in software or AI/ML engineering, with a meaningful portion in applied AI product development.
• Hands-on experience building and operating RAG systems in production.
• Strong Python skills; comfortable building pipelines, writing evaluation harnesses, and iterating on model behavior programmatically.
• SQL proficiency sufficient to query data independently, pull your own product metrics, and answer analytical questions without waiting on a data team.
• Experience deploying AI workloads on AWS; familiarity with relevant services (e.g., Bedrock, SageMaker, Lambda, RDS/Aurora, S3) and the tradeoffs between them.
• A product mindset , you think about user problems and outcomes, not just technical implementation, and you can write a clear spec as readily as a pull request.
• Experience with API design and integration, and the ability to collaborate closely with frontend and backend engineers without being a bottleneck.
• Healthcare or regulated-domain experience preferred; you understand why accuracy, auditability, and safe failure modes matter more in clinical contexts than in most.
• Familiarity with LLM safety tooling (e.g., guardrails, output validation frameworks) and an instinct for where AI systems can fail quietly.

Additional Information:

• Pay & Perks: Fully remote opportunity, medical, dental, and vision insurance, 401K retirement plan with company match, up to 20 days of PTO per year + company holidays, up to 14 weeks of parental leave (12 for non-birthing parents), annual work from home stipend for remote employees.

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As a Forward Deployed Engineer, you are the bridge between Hebbia's platform and the real-world complexity of our customers' environments. You sit with the customer's team, understand their hardest problems, and build solutions that make Hebbia indispensable. Then you bring what you've learned back to our engineering and product teams to make the platform better for everyone.

This role is for engineers who want to combine deep technical work with direct customer impact. You will see your code create value in days, not months. The FDE team operates at the intersection of engineering and go-to-market. You will work closely with our core engineering team,shared code review, architecture alignment, deploy pipelines,and with our account teams who direct where you deploy and what you focus on. Our team works in person 5 days a week at our offices in NYC and SF.

Responsibilities:

• Embed with strategic accounts to deeply understand their domain, data, and workflows
• Build custom integrations, workflow automations, and domain-specific solutions on top of Hebbia's platform
• Write production code that deploys through our CI/CD pipelines and meets our engineering standards
• Own the technical relationship with the customer's team during your engagement
• Prototype fast, validate with the customer, iterate, and ship
• Return from engagements and work with engineering and product to generalize reusable patterns into platform capabilities
• Participate in code review, on-call rotation, and architecture discussions alongside core engineering
• Build connectors to customer data sources and document management systems

Who You Are:

• 5+ years software development experience at a venture-backed startup or top technology firm
• Strong full-stack engineering skills. You build across the stack: APIs, data pipelines, frontend when needed, infrastructure when needed.
• Comfortable working in ambiguity. Customer problems are messy and underspecified. You figure it out.
• High customer empathy. You enjoy sitting with users, understanding their workflows, and translating pain points into technical solutions.
• Fast and pragmatic. You prototype, validate, and ship in days and weeks, not quarters.
• Strong communicator. You are the primary technical point of contact for the customer. You can talk to both engineers and executives.
• Experience with cloud platforms (e.g., AWS) and modern backend technologies (Python, TypeScript, Go)
• Experience with data integrations, ETL pipelines, or enterprise data systems (S3, Snowflake, SharePoint, etc.) is a plus
• Experience with LLMs, RAG systems, or applied AI is a plus but not required
• Prior experience in finance, legal, or consulting domains is a plus
• Experience with customer-facing engineering roles (solutions engineering, professional services, or similar) is a plus

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