Applied ML Researcher

Ironsite AI•San Francisco, CA

16h•Onsite

About The Position

Ironsite Construction is one of the most complex and labor-intensive industries, spending $7 trillion annually on labor, but productivity losses cost $1.6 trillion per year due to outdated management tools. Ironsite leverages wearable cameras combined with human labeling and AI vision language models to drive on-site productivity, safety & training for crafts workers. We put cameras on construction workers' hard hats and vests, then use advanced computer vision to analyze what's actually happening on job sites. We help teams reduce labor costs, improve safety, and deliver projects faster. To date, we’ve captured 50,000+ hours of construction footage across 7 states and have recently partnered with the nation’s #2 hard-hat manufacturer, Studson, to develop custom hardware purpose-built for the field. We’ve raised over $13M from 8VC, South Park Commons, and 30+ leading operators and technologists, including Eric Glyman (Ramp), Jeff Dean (Google), and Scott Wu (Cognition). Now, we’re building the team to scale nationwide.

Requirements

Fast learner eager to grow and expand their knowledge and capabilities, welcoming new challenges with a passion for their work
Background in Computer Science, Machine Learning, AI, Robotics, Data Science or a related field with multiple relevant classes completed.
Prior experience, internship, or personal project with hands-on experience designing and training deep learning models, particularly transformer-based architectures.
Familiarity with one deep learning framework (e.g., PyTorch, TensorFlow, JAX).
Proficiency in Python

Nice To Haves

At least one publication in an AI/ML/CV conference.
Experience doing research as part of a larger research lab or team
Demonstrated experience with major deep learning frameworks (e.g., PyTorch, TensorFlow, JAX).
Strong proficiency in Python and a solid foundation in software engineering principles.
Experience working with and creating large-scale vision and/or language datasets.
A strong interest in vision language models and the application of AI to solve real-world physical problems, including working with and understanding the day-to-day lives of construction workers.

Responsibilities

Architect & Train Novel VLMs: Design, train, and iterate on general-purpose Vision-Language Models fine-tuned for "Construction Intelligence" using our massive, proprietary dataset of first-person video.
Drive the Research Roadmap: Take a leading role in executing our research goals, including establishing baselines with state-of-the-art models and developing novel fine-tuning methodologies, long context architectures, and visual reasoning techniques.
Build Scalable Pipelines: Develop and own the model training and evaluation pipelines, ensuring we can rapidly experiment, measure performance, and deploy models into production.
Optimize for the Edge: Design and implement a hierarchical set of models for efficient, on-device inference on our wearable hardware as well as server-side inference. This includes developing lightweight, coarse classifiers for real-time analysis (e.g., safety event detection, info density classification), as well as heavy-weight server-side VLMs to deeply understand complex tasks.
Define the Future of Spatial Intelligence: Spearhead the development and expansion of our "Construction Intelligence" benchmark, a comprehensive suite of tasks including video-question answering, temporal reasoning, activity recognition, and higher level data analysis reasoning across the construction site that will guide our research.
Collaborate on System Design: Work closely with the hardware and data teams to explore model architectures such as a two-model system (lightweight segmenter, heavyweight insight extractor) and tool-use agents, to improve spatial understanding.