Absentia Labs

Senior AI / Machine Learning Engineer

Absentia Labs

New York•Hybrid•Salary•5-8 Yrs•Jun 17

Job description

Hybrid

Overview

Absentia Labs is building intelligent systems that sit at the intersection of AI, biology, chemistry, and large-scale engineering. Our goal is to translate complex scientific data into machine intelligence capable of reasoning, generalizing, and driving discovery.

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.
Who You Are
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.
You Likely Have
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.

Qualifications

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.
Bonus If You Have
Familiarity with model compression, distillation, or inference optimization.
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.
Note: Prior experience with molecular or biomedical models is not required. We value strong ML systems experience and the ability to transfer learning across domains.

Benefits

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.
Ownership over model design and training strategy, not just implementation.
Close collaboration with data, infrastructure, and scientific teams.
High autonomy, low bureaucracy, and a culture that values technical depth.
Flexible remote or hybrid work arrangements.
How to Apply
Please submit your resume and a brief note describing your experience training large-scale models. Links to GitHub repositories, papers, or technical write-ups are encouraged.
Our Commitment
Absentia Labs is an equal opportunity employer. We believe diverse teams build better systems and stronger science, and we encourage applicants from all backgrounds to apply.
Compensation Range: $115K - $200K

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