Senior AI/ML Engineer

Absentia Labs

. Design, train, and evaluate large-scale models, including LLMs, diffusion models, and GNNs .

Posted 9/15/2026contractBoston • California • United StatesSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and training large-scale machine learning models, including LLMs, diffusion models, and GNNs, while optimizing distributed training systems and ensuring model evaluation and lifecycle management. Strong ability to communicate technical concepts effectively to diverse stakeholders.

Highest-signal resume keywords

Large-Scale Model TrainingPyTorch ProficiencyDistributed Training OptimizationData Pipeline ManagementTechnical Leadership

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Large-Scale ModelsLLMsDiffusion ModelsGNNsDistributed TrainingData ParallelismModel ParallelismMixed PrecisionOptimization StrategiesSoftware Engineering Fundamentals

Soft Skills

Technical CommunicationMentorshipCollaboration

Industry Keywords

Machine LearningApplied AIModel EvaluationAblationReproducibilityLifecycle Management

Tech Stack

Tools & technologies

PyTorch

About the role

Key responsibilities & impact

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

Requirements

What you’ll need

5+ years of industry experience in machine learning or applied AI roles
Demonstrated experience training large-scale models in production settings
Hands-on expertise with LLMs, diffusion models, and/or GNNs
Strong proficiency in PyTorch or equivalent deep learning frameworks
Deep understanding of distributed training, including parallelism strategies and performance optimization
Experience working with large datasets and high-throughput data pipelines
Strong software engineering fundamentals, including clean code, testing, reproducibility, and debugging at scale
Ability to communicate technical trade-offs to technical and non-technical stakeholders
Prior experience with molecular or biomedical models is not required
Must be legally authorized to work in the U.S. (application question)
Undergraduate degree details required in the application form

Benefits

Comp & perks

Competitive compensation, including meaningful equity participation
Opportunity to work on foundation-level ML systems applied to real scientific problems
Ownership over model design and training strategy
Close collaboration with data, infrastructure, and scientific teams
High autonomy and low bureaucracy
Flexible remote or hybrid work arrangements