Senior AI/ML Engineer at Absentia Labs

You will lead the design, training, and deployment of foundation-level models including LLMs, diffusion models, and Graph Neural Networks. As a senior engineer, you will own the full lifecycle from dataset interfaces to distributed training and production serving, making critical architectural decisions that drive discovery in complex scientific domains.

• Design and train large-scale models across LLM, diffusion, and GNN architectures to handle noisy, multimodal scientific data.
• Build and optimize distributed training systems using data and model parallelism, sharding, and mixed precision to ensure performance at scale.
• Collaborate with data and scientific teams to define ML-ready datasets and translate ambiguous biological requirements into robust machine learning solutions.

• At least 5 years of industry experience with a proven track record of training and deploying large-scale models in production environments.
• Deep technical proficiency in PyTorch and distributed training strategies, with hands-on expertise in LLMs, diffusion models, or Graph Neural Networks.
• Strong software engineering fundamentals, including clean code and reproducibility, with the ability to communicate technical trade-offs to diverse stakeholders.

• Work at the frontier of AI and biology, building foundation-level systems for mechanistic toxicology and virtual drug testing alongside an MIT/postdoc-led team.
• High-autonomy environment where you own model design and training strategy rather than just implementation, reporting directly to founding technical leadership.
• Unique contract-to-hire structure ensures a mutual fit before converting to a permanent role with a $175k-$210k salary and meaningful equity.