Search
Go
Senior AI/ML Engineer
Summary by AIEngineer.careers
Senior AI/ML Engineer is a AI Engineer role (full-time). with Absentia Labs. in REMOTE, BOSTON, US. Compensation shown: $155K–$200K. Imported listing (source: ottoroles.com). Apply on the employer's site (ottoroles.com).
Imported listing (source: ottoroles.com) · Apply on ottoroles.com
REMOTE · BOSTON · US
$155K–$200K
Full-time
SENIOR
The sections below reproduce the third-party job description for reference. AIEngineer.careers does not write or control this text.
Imported job description
Sourced from ottoroles.com
Absentia Labs is seeking a Senior AI/ML Engineer to lead the design, training, and deployment of large-scale machine learning models at the intersection of AI and biotechnology. You will own end-to-end training pipelines for LLMs, diffusion models, and GNNs, optimizing distributed systems for production scale. This role requires deep expertise in model architecture, scaling laws, and engineering fundamentals to translate complex scientific data into robust ML solutions.
Responsibilities
- Design, train, and evaluate large-scale models including LLMs, diffusion models, and Graph Neural Networks.
- Own end-to-end training pipelines from dataset interfaces to distributed training and checkpointing.
- Build and optimize distributed training systems utilizing data/model parallelism and mixed precision.
- Collaborate with data engineers to define ML-ready datasets and streaming interfaces.
- Drive model evaluation, ablation, and iteration focusing on generalization and stability.
- Provide technical leadership through design reviews and mentorship.
Requirements
- 5+ years of industry experience in machine learning or applied AI roles.
- Demonstrated experience training large-scale models in production settings.
Absentia Labs
Somerville · US · 5+ employees
Absentia Labs is a biotechnology company developing AI-driven foundation models to predict drug behavior within the human body. Their platform aims to transform drug discovery by replacing traditional animal testing with computational, organ-wide models to improve safety and efficacy predictions.
