Senior Software Engineer, AI Research

Dataminr•Arlington, VA

1d•$154,800 - $227,500•Hybrid

About The Position

As a Senior AI Engineer at Dataminr, you will work with Research Scientists and Data Scientists to train, adapt, and deploy state-of-the-art LLMs and agents across Predictive, Generative, and Agentic AI. The work involves multimodal data, including text, images, video, audio, and sensor data, on a platform that processes billions of inputs daily. This hybrid role requires two in-office days each week in Dataminr’s New York City or Arlington, Virginia office. Within the AI Platform organization and beyond, you will collaborate with Research Scientists, Engineers, and Data Scientists to build, adapt, optimize, and deploy LLM models in production at very large scale. You will own technical design and implementation of robust API services, libraries, and container strategies supporting the end-to-end LLMOps lifecycle; optimize deep-learning and LLM training and inference performance for speed and cost on specialized hardware; and drive complex AI problems from research and data exploration through implementation of novel in-house methods and productization. Dataminr’s AI work includes ReGenAI, which regenerates real-time Live Event Briefs as events unfold; Intel Agents, which autonomously generate context about real-time events, threats, and risks; and multimodal event detection across images, video, sensor data, audio, and text in more than 150 languages.

Requirements

Master’s degree in Computer Science or equivalent experience.
At least 5 years of experience as a Research Engineer or Machine Learning Engineer.
Experience building, optimizing, deploying, and operating deep-learning models using PyTorch, including Transformers and LLMs, rather than only using APIs.
Experience working with Research Scientists, reading technical research papers, and implementing state-of-the-art methods in Natural Language Processing, Computer Vision, Knowledge Representation and Management, Vector Databases, and Generative AI.
Expertise in Python.
Experience with containers, Kubernetes, AWS, Databricks, and agentic infrastructure such as LangGraph, LangFuse, and vector stores.

Responsibilities

Work with Research Scientists, Engineers, and Data Scientists to build, adapt, optimize, and deploy LLM models in production at very large scale.
Design and implement robust API services, libraries, and container strategies for the end-to-end LLMOps lifecycle.
Optimize deep-learning and LLM training and inference performance for speed and cost on specialized hardware.
Drive complex AI problems from research and literature review through data exploration, implementation of novel in-house methods, and productization.
Train, adapt, and deploy state-of-the-art LLMs and agents across Predictive, Generative, and Agentic AI.