Senior AI/ML Engineer

Absentia Labs • Boston, MA

2d • $155,000 - $200,000

About The Position

Absentia Labs is building intelligent systems at the intersection of AI, biology, chemistry, and large-scale engineering, with the goal of translating complex scientific data into machine intelligence capable of reasoning, generalizing, and driving discovery. The Senior AI/ML Engineer will lead the design, training, and deployment of large-scale machine learning models that form the core of the company’s AI capabilities. The role works across model architecture, training systems, and production infrastructure, with significant ownership over technical direction.

The role is intended for engineers experienced in training large models in real production environments who can reason about learning dynamics and systems constraints. The engineer will own end-to-end training pipelines, make decisions about architectures and optimization, build distributed training systems, and guide model evaluation and iteration. They will also contribute to serving, inference efficiency, and lifecycle management, while providing technical leadership through design reviews, mentorship, and cross-team collaboration.

The position involves translating ambiguous scientific or product requirements into robust ML solutions and working closely with data engineers to develop ML-ready datasets and streaming interfaces. Prior experience with molecular or biomedical models is not required; the company values strong ML systems experience and the ability to transfer learning across domains.

Requirements

• At least 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 with PyTorch or an equivalent deep learning framework.
• 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, including clean code, testing, reproducibility, and debugging at scale.
• Ability to clearly communicate technical trade-offs to technical and non-technical stakeholders.

Nice To Haves

• Experience with reinforcement learning, fine-tuning, or preference-based optimization such as 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.

Responsibilities

• Design, train, and evaluate large-scale models, including LLMs, diffusion models, and 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, including data parallelism, model parallelism, sharding, and mixed precision.
• 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, focusing on generalization, stability, and reproducibility.
• Contribute to architectural decisions about model serving, inference efficiency, and lifecycle management.
• Provide technical leadership through design reviews, mentorship, and cross-team collaboration.

Benefits

• Competitive compensation, including meaningful equity participation.
• Opportunity to work on foundation-level ML systems applied to real scientific problems.
• Ownership over model design and training strategy, not just implementation.
• Close collaboration with data, infrastructure, and scientific teams.
• High autonomy and low bureaucracy in a culture that values technical depth.
• Flexible remote or hybrid work arrangements.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed