Senior Software Engineer, AI Research

Dataminr•Arlington, NY

6d•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 every day to help organizations respond to critical events as they unfold. The role offers opportunities to grow in AI and software engineering. You will collaborate with Research Scientists, Engineers, and Data Scientists across the AI Platform organization to build, adapt, optimize, and deploy LLM models in production at very large scale. You will own the technical design and implementation of API services, libraries, and container strategies supporting the end-to-end LLMOps lifecycle, and optimize deep learning and LLM training and inference performance on specialized hardware. You will also take complex AI problems from research and literature review through data exploration, implementation of novel methods, and productization. This is a hybrid role requiring two in-office days each week in Dataminr’s New York City or Arlington, Virginia office. The annual base salary range is $154,800–$227,500, with eligibility for a discretionary bonus and company equity. Actual salary depends on factors including location, skills, and relevant experience.

Requirements

M.S. 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—not just using APIs.
Experience working with Research Scientists, reading technical research papers, and implementing state-of-the-art methods.
Expertise in Python.
Experience with containers, Kubernetes, AWS, Databricks, and agentic infrastructure such as LangGraph, LangFuse, and vector stores.

Responsibilities

Collaborate with Research Scientists, Engineers, and Data Scientists to build, adapt, optimize, and deploy LLM models in production at very large scale.
Own the technical design and implementation of robust API services, libraries, and container strategies for the end-to-end LLMOps lifecycle.
Optimize deep learning and LLM training and inference performance, including 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.