Absentia Labs

Machine Learning Engineer

• Design, train, and evaluate large-scale models, including LLMs, diffusion models, and GNNs
• Own end-to-end training pipelines, from dataset interfaces and batching strategies to distributed training and checkpointing
• Make decisions about model architecture, objective functions, optimization strategies, and scaling laws
• Build and optimize distributed training systems using data parallelism, model parallelism, sharding, and mixed precision
• Collaborate with data engineers to define ML-ready datasets and streaming interfaces
• Translate ambiguous scientific or product requirements into robust ML solutions
• Drive model evaluation, ablation, and iteration focused on generalization, stability, and reproducibility
• Contribute to model serving, inference efficiency, and lifecycle management architecture
• Provide technical leadership through design reviews, mentorship, and cross-team collaboration
• Submit a resume and brief note describing experience training large-scale models

Senior AI/ML Engineer

AI Research Scientist

• Own research problems in predictive toxicology, drug safety, and computational biology from hypothesis through experimental validation
• Develop and evaluate novel deep learning architectures and training methods, including graph neural networks, transformers, multimodal models, and generative approaches
• Investigate representations connecting molecular structure, biological targets, pathways, dose and exposure, pharmacology, toxicology, and clinical outcomes
• Develop approaches for heterogeneous, sparse, noisy, and partially observed scientific datasets
• Explore cross-domain and out-of-distribution generalization for novel compounds, chemical spaces, biological contexts, and endpoints
• Design experiments, benchmarks, ablations, and evaluation frameworks
• Develop uncertainty estimation, calibration, applicability-domain assessment, and confidence-aware prediction methods
• Make model predictions more mechanistically interpretable
• Explore multimodal and foundation-model approaches combining chemical, biological, experimental, literature-derived, and clinical evidence
• Identify model limitations and failure modes and turn them into research directions
• Work with AI/ML and data engineers to translate research into reproducible, scalable modeling systems
• Contribute to scientific strategy, validation studies, external collaborations, publications, and regulatory evaluation support
• Collaborate closely with the CTO, engineers, data engineers, and scientists