Director of AI/ML -Grata

Datasite•New York City, NY

1d•Hybrid

About The Position

Grata is the leading private market dealmaking platform. We make it easy to find, research, and engage with private companies while powering end‑to‑end M&A business development workflows. Our platform delivers the most comprehensive, accurate, and searchable proprietary data on private companies, their financials, and their owners. We are looking for a Director of AI/ML to lead the AI/ML team building the intelligence behind Grata’s next generation of dealmaking workflows. This role owns the models and evaluation frameworks that power how customers discover opportunities, research companies, and act faster. You will lead Grata’s AI/ML team end‑to‑end: setting the multi‑quarter AI strategy, owning execution and quality of production AI systems, and growing the engineers and applied scientists on the team, while partnering with senior leaders across Engineering, Product, Data, and Platform to deliver AI capabilities that move the business. Grata is a hybrid company, with in-office collaboration in NYC on Mondays, Tuesdays, and Thursdays.

Requirements

Multiple years of experience leading AI, ML, or applied science teams as a director, senior manager, or equivalent.
Track record of shipping AI/ML systems into production and improving them post‑launch through evaluation, monitoring, and iteration.
Strong technical depth in modern AI/ML: LLMs, retrieval and RAG, embeddings, prompting, fine‑tuning, and the trade‑offs between them.
Experience designing evaluation and quality frameworks for AI systems, including offline benchmarks, online metrics, and human‑in‑the‑loop review.
Language‑agnostic technical credibility: comfortable partnering with engineers and applied scientists across modern AI stacks to guide architecture and review technical decisions, regardless of the specific language or framework.
Sound judgment on AI cost, latency, reliability, and safety in production, with clear ownership of incidents and regressions.
Demonstrated ability to hire, develop, and retain senior engineers and applied scientists, and to coach team members on growth plans and promotions.
Strong judgment, ownership, and collaboration skills.
Ability to set a clear AI strategy and align the team and cross‑functional partners behind it.
Ability to translate ambiguous product problems into well‑scoped AI work, and knowing when AI is not the right tool.
Ability to anticipate cross‑team constraints and balancing technical risk with delivery goals.
Ability to hold the team to a high‑quality bar on evaluation, reliability, and customer impact while reinforcing sustainable execution.
Ability to forecast capacity, negotiate scope, and plan effectively across quarters.
Ability to communicate clearly with technical and non‑technical partners, including executive stakeholders, about what AI can and cannot do.
Ability to guide the team through a fast‑moving model and tooling landscape without chasing every new release.
Ability to coach engineers and applied scientists, not just driving delivery, to grow into the next level of their careers.
Ability to proactively improve processes, metrics, and engineering effectiveness.
Being a strong culture add — raising the bar on collaboration, ownership, and how the team works together.

Nice To Haves

Experience leading teams that build agentic or autonomous workflows , multi‑step LLM systems, or tool‑use frameworks.
Hands‑on familiarity with foundation model APIs and fine‑tuning , including frontier and open‑weight models.
Background in or familiarity with data engineering, ETL, and large‑scale data processing systems , such as: Databricks or Spark‑based pipelines Kafka, Pulsar, or similar event streaming platforms Airflow, dbt, or workflow orchestration tools
Exposure to cloud‑native architectures (AWS, GCP, or Azure), Kubernetes (EKS or similar), and modern observability stacks.
Experience with MLOps or AI platforms , including experimentation, feature stores, model registries, and deployment tooling.
Background in financial data, M&A, private markets, or similarly data‑heavy domains.

Responsibilities

Set and own a multi‑quarter vision for Grata’s AI/ML stack, including agentic workflows, retrieval and RAG systems, model selection and fine‑tuning, and evaluation; and translate it into a prioritized, funded roadmap.
Set the product direction for AI-powered functionality, partnering with Product and senior leadership to define what AI experiences Grata builds, why they matter to customers, and how they differentiate the platform.
Lead the AI/ML team directly, including hiring, leveling, performance management, coaching, and career development for the engineers and applied scientists on the team.
Own the outcomes of the team’s efforts: successful AI feature launches, measurable customer impact, and shared commercial wins.
Own the quality of AI systems in production, including accuracy, reliability, latency, cost, and safety; and the evaluation and monitoring frameworks that keep them honest.
Make principled build‑vs‑buy decisions on models, vendors, and tooling, and partner with Finance and engineering leadership on AI spend and capacity planning.
Drive cross‑functional initiatives that span AI, Product, Data, and Platform, including major architectural shifts as the model and tooling landscape evolves.
Balance near‑term delivery with long‑term technical health, making explicit trade‑offs on prototype velocity, productionization, and technical debt.
Represent AI/ML in company‑level planning and communicate strategy clearly to technical and non‑technical audiences, up to and including the executive team.
Establish and evolve healthy team practices across research, productionization, evaluation, and operational excellence; and foster a culture of high ownership, psychological safety, and sustainable pace.
Building the intelligence that powers Grata’s M&A business development platform and the agentic workflows on top of it.
Designing and operating retrieval, ranking, and reasoning systems that help customers find the right private companies, investors, and opportunities.
Developing agents and tool‑use systems that automate workflows for dealmakers, with the evaluation frameworks needed to ship them reliably.
Investing in the AI development lifecycle, including experimentation tooling, evaluation harnesses, and feedback loops that compound over time.
Generating proprietary data signals from Grata’s exclusive data to power discovery, research, and ranking.
Partnering with Product, Data, and Platform leadership to align AI investments with where the business is heading next.