Senior AI/ML Engineer, Large-Scale Models & Systems

Socket.dev; Boston (MA); On-site; USD 180,000 - 260,000; Full time

Benefits offered by this job

Equity participation

Remote or hybrid work

Autonomy

Job summary

Absentia Labs is building intelligent systems at the intersection of AI, biology, chemistry, and large-scale engineering. We seek a Senior AI/ML Engineer to lead design, training, and deployment of large-scale models with ownership of technical direction.

You will work at the boundary of model architecture, training systems, and production infrastructure, in a role suited for engineers who have trained large models in production environments and can reason about learning dynamics and system

Qualifications

• 5+ years of industry experience in machine learning or applied AI roles.
• Demonstrated experience training large-scale models in production settings, not just prototypes.
• 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.

Responsibilities

• Design, train, and evaluate large-scale models, including LLMs, diffusion models, and GNNs.
• Own end-to-end training pipelines, from dataset interfaces to distributed training and checkpointing.
• Translate ambiguous scientific or product requirements into robust ML solutions.
• Drive model evaluation, ablation, and iteration with emphasis on generalization and reproducibility.
• Contribute to architectural decisions around model serving and inference efficiency.

Skills

LLMs

Diffusion models

GNNs

PyTorch

Distributed training

Large-scale ML systems

Data pipelines

Model serving

Tools

CUDA

Job description

Absentia Labs is building intelligent systems at the intersection of AI, biology, chemistry, and large-scale engineering. We seek a Senior AI/ML Engineer to lead design, training, and deployment of large-scale models with ownership of technical direction.

You will work at the boundary of model architecture, training systems, and production infrastructure, in a role suited for engineers who have trained large models in production environments and can reason about learning dynamics and system