AI Research Scientist

Absentia Labs

. Own research problems in predictive toxicology, drug safety, and computational biology from hypothesis through experimental validation .

Posted 9/15/2026contractBoston • Massachusetts • United StatesMid-LevelSenior💰 $160,000 - $208,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in predictive toxicology and computational biology, with a strong focus on developing and evaluating deep learning architectures and methodologies. Proficient in translating complex research into scalable modeling systems while ensuring rigorous experimental validation and effective communication.

Highest-signal resume keywords

PhD In Machine LearningDeep Learning Model DevelopmentExperience With PyTorchGraph Neural NetworksStrong Research Track Record

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Predictive ToxicologyComputational BiologyDeep LearningExperimental DesignModel EvaluationGenerative ModelingProbabilistic ModelingRepresentation LearningSelf-Supervised LearningMultimodal Learning

Soft Skills

Strong Written CommunicationStrong Verbal CommunicationIndependent Research Identification

Tools & Technologies

PyTorchJAX

Industry Keywords

Drug SafetyMachine LearningComputational ChemistryStatistical AnalysisRegulatory Evaluation

Tech Stack

Tools & technologies

PyTorch

About the role

Key responsibilities & impact

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

Requirements

What you’ll need

PhD in machine learning, artificial intelligence, computer science, computational biology, computational chemistry, applied mathematics, statistics, or a closely related field, or equivalent demonstrated research experience
Strong research track record demonstrated through publications, novel methods, significant open-source research, or technically substantial research projects
Deep understanding of modern machine learning and deep learning
Hands-on experience developing and evaluating models in PyTorch, JAX, or equivalent frameworks
Experience with graph neural networks, transformers, representation learning, multimodal learning, generative modeling, self-supervised learning, probabilistic modeling, or foundation models
Strong experimental instincts and experience designing controlled evaluations, ablations, and reproducible research
Ability to reason about dataset construction, leakage, confounding, generalization, and evaluation methodology
Ability to independently identify important research questions and carry projects from initial hypothesis through rigorous experimental results
Strong written and verbal communication skills
Legal authorization to work in the U.S. is requested in the application form
At least 6 years of industry experience, excluding internships and school projects, is requested in the application form

Benefits

Comp & perks

Competitive compensation, including meaningful equity participation
Bonus opportunity
Flexible remote or hybrid work arrangements
High autonomy and low bureaucracy
Close collaboration with data, AI/ML, infrastructure, and scientific teams