Absentia Labs

Senior AI / Machine Learning Engineer

Boston · Senior

Sponsorship not specifiedDetected 35 days ago

GitMachine LearningDeep LearningPyTorchLLMsResearchLeadershipCollaborationMentoring

About the role

You will work at the boundary between model architecture, training systems, and production infrastructure, with significant ownership over technical direction.
This role is intended for engineers who have trained large models in real production environments, understand the realities of scale, and can reason about both learning dynamics and systems constraints.

Design, train, and evaluate large-scale models, including Large Language Models (LLMs), diffusion models, and Graph Neural Networks (GNNs).
Own end-to-end training pipelines, from dataset interfaces and batching strategies to distributed training and checkpointing.
Build and optimize distributed training systems (data parallelism, model parallelism, sharding, mixed precision).
Collaborate closely with data engineers to define ML-ready datasets and streaming interfaces.
Drive model evaluation, ablation, and iteration with a focus on generalization, stability, and reproducibility.
Provide technical leadership through design reviews, mentorship, and cross-team collaboration.
Ownership over model design and training strategy, not just implementation.

You are a senior ML engineer who thinks holistically about models as systems.
You are comfortable operating under uncertainty, making trade-offs between compute, data, and performance, and owning outcomes from research through production.
You care deeply about training dynamics, failure modes, and scaling behavior, and you have the scars to prove it.
Demonstrated experience training large-scale models in production settings, not just prototypes.
Hands-on expertise with LLMs, diffusion models, and/or GNNs.
Experience working with large datasets and high-throughput data pipelines.
Ability to clearly communicate technical trade-offs to both technical and non-technical stakeholders.
Familiarity with model compression, distillation, or inference optimization.
Experience deploying models in production inference systems.
Note: Prior experience with molecular or biomedical models is not required.

Competitive compensation, including meaningful equity participation, allows you to share directly in the long-term success and growth of the company.

Competitive compensation, including meaningful equity participation, allows you to share directly in the long-term success and growth of the company.
The opportunity to work on foundation-level ML systems applied to real scientific problems.
Experience with reinforcement learning, fine-tuning, or preference-based optimization (e.g., RLHF).
Exposure to multimodal learning or foundation models.
5+ years of industry experience in machine learning or applied AI roles.
Flexible remote or hybrid work arrangements.

This listing is sourced directly from Absentia Labs's careers page and normalized into a canonical job model.