Senior AI/ML Engineer

The role

You will lead the design, training, and deployment of large-scale machine learning models. You will work on model architecture, training systems, and production infrastructure.

What you will do

• Design, train, and evaluate large-scale models including LLMs, diffusion models, and GNNs
• Own end-to-end training pipelines
• Make decisions about model architecture, objective functions, optimization strategies, and scaling laws
• Build and optimize distributed training systems
• Collaborate 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
• Contribute to architectural decisions around model serving, inference efficiency, and lifecycle management
• Provide technical leadership through design reviews, mentorship, and cross-team collaboration

Required qualifications

• 5+ years of industry experience in machine learning or applied AI roles
• Demonstrated experience training large-scale models in production settings
• 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

Nice to have

• 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

Benefits

• Competitive compensation
• Meaningful equity participation
• Opportunity to work on foundation-level ML systems applied to real scientific problems
• Ownership over model design and training strategy
• Close collaboration with data, infrastructure, and scientific teams
• High autonomy, low bureaucracy
• Flexible remote or hybrid work arrangements

Before you apply

• Submit your resume and a brief note describing your experience training large-scale models

About the company

Absentia Labs is building intelligent systems at the intersection of AI, biology, chemistry, and large-scale engineering. Their goal is to translate complex scientific data into machine intelligence.