Senior Director, AI Engineering

Dataminr•New York, NY

2d•Remote

About The Position

Dataminr is searching for a visionary Senior Director of AI Engineering to lead and inspire the team of engineers building the AI and data engine powering Dataminr’s real-time intelligence platform. This pivotal role demands a leader who can bridge the gap between cutting-edge AI research and scalable, real-world product deployment. You will drive technical strategies that translate complex AI, Deep Learning and Machine Learning innovations into tangible business impact. We are seeking a candidate with a strong technical foundation and a proven ability to deliver high-quality, end-to-end AI solutions. You are passionate about tackling challenging AI problems, fostering collaboration across product and research teams, and empowering your team to build products that make a meaningful, real-world difference. This US-based role can be remote or based out of our New York City office.

Requirements

Graduate degree in Computer Science or Electrical Engineering.
At least 7 years of industry experience as a software engineer in a cloud-native environment (AWS, Kubernetes).
At least 5 years experience leading software engineers in an AI team.
Experience with deep learning frameworks, LLMs, agent harnesses, compound AI systems (PyTorch, Langfuse, LangGraph, Weaviate, A2A, etc)
Hands-on experience in developing and deploying Machine Learning/Deep Learning/LLMs at large scale in NLP, CV, IR, or a related field, preferably in a real-time setting.
Demonstrated track record of driving and delivering on multiple complex AI projects.
Excellent communication skills.
Deep understanding of research stages, and the ability to connect multiple complex technologies in end-to-end solutions.

Responsibilities

Oversee all technical aspects of how different AI models’ outputs impact the quality of content produced by our AI platform.
Work closely with scientists, engineers, and product managers to drive and deliver state of the art solutions at scale in multiple areas relevant to real-time detection of events in public data sources (NLP, CV, IR, Knowledge Graphs, etc).
Define and track quality and cost metrics for models as well as for their integrated end-to-end performance in relation to key business goals.
Hands-on modification of workflows, evaluations, and outputs of models.
Lead other engineers and scientists, directing changes to technical approaches.
Excel in placing a human-centered focus on the work (context, end-user impact, etc), finding solutions that work in practice, and have significant impact.