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About the Role

As a Principal Applied ML Researcher, you will report directly to the Chief Science Officer and own high-risk, high-impact research: training, benchmarking, and deploying state-of-the-art VLMs that can interpret the complexity of a real-world construction site, built on data no other lab has.

Responsibilities

• Architect & Train Novel VLMs: Design, train, and iterate on general-purpose Vision-Language Models fine-tuned for spatial intelligence in the construction site.
• Drive the Research Roadmap: Take a leading role in executing our research goals, from establishing baselines with state-of-the-art models to developing post-training recipes (SFT and RL), long-context architectures, and visual reasoning techniques.
• Own the Construction Intelligence Benchmark: Build and expand our benchmark suite: video question answering, temporal reasoning, activity recognition, and site-level analytical reasoning.
• 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 Inference at Scale: Apply distillation, quantization, and model routing so state-of-the-art understanding runs affordably across thousands of hours of daily footage, working with the hardware and data teams on system design.

Requirements

• Have 6+ years of hands-on experience designing and training large-scale deep learning models, particularly transformer-based architectures.
• Have a background in Computer Science, Machine Learning, AI, Robotics, or a related field.
• Have demonstrated experience with major deep learning frameworks (e.g., PyTorch, JAX).
• Are strongly proficient in Python, with a solid foundation in software engineering principles.
• Have experience working with and creating large-scale vision and/or language datasets.
• Enjoy rapid iteration on immediate blockers in service of long-term research goals.

Skills

• Transformer-based architectures
• PyTorch
• JAX
• Python
• Software engineering principles
• Large-scale vision and/or language datasets
• Fine-tuning and post-training large language or vision-language models (SFT, GRPO and other RL methods, parameter-efficient tuning such as LoRA)
• Video data
• Temporal reasoning
• Long-context modeling
• Efficient processing
• Optimizing inference
• Quantization
• Distillation
• Sparsity
• Efficient serving
• MLOps tools for scalable model training and deployment
• Vision-language models
• AI to real-world physical problems

Location

• San Francisco Bay Area

Work Type

• On-site

Experience Level

• 6+ years of hands-on experience

Education Level

• Background in Computer Science, Machine Learning, AI, Robotics, or a related field

Salary/Compensations

• $250,000 – $350,000 per year

Benefits

• Competitive salary and significant equity package
• Full benefits including health, dental, vision, and 401k +6% match
• Access to dedicated GPU compute resources for research and experimentation
• Daily catered breakfast and lunch
• Great location next to Oracle Park and the Cal Train

About the Company

• Ironsite is a spatial intelligence construction technology company building the intelligence layer for the physical world.
• We are accelerating the speed, efficiency, and predictability of construction, especially for complex, mission-critical infrastructure projects including data centers, LNG facilities, sports stadiums, hospitals, and other large-scale developments, by training AI models on egocentric construction footage and labor productivity data.
• Ironsite is the productivity system built for the people who build America.
• We design our own wearable hardware, deploy it alongside craft workers, and transform a shift's footage into a next-morning report.
• We label the data overnight and deliver actionable insights to superintendents by 5 AM every day.
• We are built with a pro-worker philosophy at our core.
• We believe technology should empower the workforce, not replace it, and we give craft workers and project leaders real visibility into what is happening on-site, so the reality of each day is finally available to the people running the project.
• By capturing and labeling the world's largest dataset of first-person video from active construction sites, we are building a general-purpose Vision-Language Model with the spatial intelligence to understand the most dynamic and complex physical environment in the world: the construction site.
• Our mission is to create general models that deeply understand dynamic, unstructured environments to supercharge human productivity and eventually enable robots to fill critical labor gaps.
• Our culture is one of radical transparency, intellectual honesty, low ego, rapid experimentation, and curiosity.
• We are relentlessly focused on the long-term goal of building truly generalizable intelligence that benefits everyone, starting with the construction workers responsible for our built world.
• Ironsite is deployed across several of the top ten largest active construction projects in the country.
• We currently collect ~1,000 hours of new video every day from 8 states and expect to reach ~10,000 hours per day by the end of the year, growing further from there, all while maintaining a worker opt-out rate below two percent.
• This is enabled by a workforce-first architecture that anonymizes devices, captures no audio, and never releases raw video.
• Ironsite is backed by leading investors (8VC, South Park Commons, Saga Ventures) and prominent operators across technology and construction, including Eric Schmidt, Jeff Dean, Jeff Rothschild, Mark Leslie, Scott Wu, Eric Glyman, Karim Atiyeh, Russell Kaplan, and others, alongside 12 construction industry operators who have joined us as partners in building this.