Senior Applied ML Researcher

About Ironsite

The Role

What You'll Build

• Foundation Models from Scratch: Design model architectures, training objectives, and optimization strategies to train large language models and multimodal models (combining vision + language) specifically for construction use cases
• Domain-Specific Pre-training: Develop pre-training strategies using construction documentation, safety protocols, building codes, and industry knowledge to create models that understand construction contexts deeply
• Multimodal Model Training: Build and train vision-language models that can reason about construction footage, technical drawings, and textual data simultaneously
• Training Infrastructure: Design and implement distributed training systems capable of handling billion+ parameter models across GPU clusters
• Model Evaluation & Alignment: Create comprehensive evaluation frameworks and fine-tuning pipelines (supervised fine-tuning, RLHF) to align models with construction domain requirements
• Data Pipeline Architecture: Build scalable data ingestion, cleaning, and preprocessing systems for training on diverse construction data sources
• Production ML Systems: Deploy and monitor computer vision models in challenging real-world environments with strict latency and reliability requirements
• Human-AI Collaboration Platform: Develop systems that seamlessly integrate human labeling with AI predictions for continuous model improvement
• Cross-modal Understanding: Create models that combine visual data with contextual information for deeper construction insights

Technical Challenges You'll Solve

• Training large-scale models efficiently with limited compute budgets while maximizing performance
• Developing novel pre-training objectives that capture construction-specific knowledge and temporal reasoning
• Implementing efficient attention mechanisms and architectural innovations for long-context understanding of construction projects
• Designing evaluation metrics that measure real-world construction task performance beyond standard benchmarks
• Balancing model capability with deployment constraints for edge and mobile applications

What We're Looking For

• [REQUIRED] 5+ years of experience in deep learning research with focus on large-scale model training
• [REQUIRED] Demonstrated experience training models at scale (100M+ parameters) from scratch
• [REQUIRED] Deep understanding of transformer architectures, attention mechanisms, and modern training techniques (mixed precision, distributed training, gradient accumulation)
• Production experience with ML systems at scale (PyTorch, distributed training, model serving)
• Experience with language model pre-training, including tokenization strategies, training objectives (CLM, MLM), and scaling laws
• Understanding of fine-tuning techniques including instruction tuning, RLHF, and preference optimization (DPO, PPO)
• Knowledge of efficient training techniques: LoRA, QLoRA, flash attention, gradient checkpointing
• Experience with model evaluation, benchmarking, and safety considerations

• Background in vision-language models or multimodal architectures strongly preferred
• Experience building and optimizing data pipelines for large-scale training
• Systems-level thinking about training efficiency, hardware utilization, and cost optimization
• Familiarity with MLOps for model versioning, experiment tracking, and reproducibility

Preferred Qualifications

• Advanced degree (M.S./Ph.D.) in Computer Science, Electrical Engineering, or related field with focus on deep learning
• First-author publications at top ML venues (NeurIPS, ICML, ICLR, ACL, CVPR) on language models, multimodal learning, or efficient training
• Experience training or contributing to open-source foundation models
• Background in domain-specific model development (code, science, medical, etc.)
• Experience with video understanding models or temporal reasoning in transformers
• Contributions to major ML frameworks or training libraries
• Track record of transitioning research prototypes to production systems

Location & Compensation

• San Francisco Bay Area (on-site)
• 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

Compensation