Senior AI / Machine Learning Engineer

Absentia Labs is building intelligent systems at the intersection of AI, biology, chemistry, and large-scale engineering. They are seeking a Senior AI/Machine Learning Engineer to lead the design, training, and deployment of large-scale machine learning models, with significant ownership over technical direction and collaboration with data engineers.

Responsibilities:

• 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
• Make principled decisions about model architecture, objective functions, optimization strategies, and scaling laws
• 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
• Translate ambiguous scientific or product requirements into robust ML solutions
• Drive model evaluation, ablation, and iteration with a focus on generalization, stability, and reproducibility
• Contribute to architectural decisions around model serving, inference efficiency, and lifecycle management
• Provide technical leadership through design reviews, mentorship, and cross-team collaboration

Requirements:

• 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
• Experience working with large datasets and high-throughput data pipelines
• Strong software engineering fundamentals: clean code, testing, reproducibility, and debugging at scale
• Ability to clearly communicate technical trade-offs to both technical and non-technical stakeholders
• Experience with reinforcement learning, fine-tuning, or preference-based optimization (e.g., RLHF)
• Familiarity with model compression, distillation, or inference optimization
• Experience deploying models in production inference systems
• Exposure to multimodal learning or foundation models
• Prior work in startups or fast-moving R&D environments
• Contributions to open-source ML frameworks or research codebases