Contract-to-hire: Senior AI/ML Engineer

absentia-labs; Boston (MA); On-site; USD 150,000 - 190,000; Full time

Benefits offered by this job

Equity participation

Flexible work

Autonomy

Job summary

Absentia Labs is seeking a Senior AI/ML Engineer to lead the design, training, and deployment of large-scale models at the intersection of AI and biology. You will own end-to-end training pipelines and shape architectural decisions for scalable systems.

The role requires production experience with large models, strong PyTorch skills, and a deep understanding of distributed training. Flexible remote or hybrid work arrangements are offered.

Qualifications

• 5+ years of industry experience in ML or applied AI.
• Experience training large-scale models in production environments.
• Hands-on experience with LLMs, diffusion models, or Graph Neural Networks.
• Strong proficiency in PyTorch or equivalent frameworks.
• Deep understanding of distributed training, parallelism, and performance optimization.
• Experience with large datasets and high-throughput data pipelines.
• Strong software engineering fundamentals: clean code, testing, reproducibility.
• Ability to clearly communicate technical trade-offs to stakeholders.

Responsibilities

• Design, train, and evaluate large-scale models (LLMs, diffusion, GNNs).
• Own end-to-end training pipelines including data interfaces and distributed training.
• Decide on model architecture, objectives, optimization, and scaling laws.
• Build and optimize distributed training systems (data/model parallelism, sharding, mixed precision).
• Collaborate with data engineers to define ML-ready datasets and streaming interfaces.
• Translate scientific or product requirements into robust ML solutions.
• Drive model evaluation, ablation, and iterative improvement for generalization and reliability.
• Contribute to model serving and inference lifecycle decisions.
• Provide technical leadership through design reviews and mentorship.

Skills

LLM training

Diffusion models

GNNs

PyTorch

Distributed training

Data pipelines

Software engineering

Model evaluation

Tools

PyTorch

High-performance computing

Job description

About Absentia Labs

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.

Biomedical data is fragmented, noisy, and deeply interconnected. Turning it into a useful signal requires not only strong data foundations but also carefully designed learning systems that can scale across modalities, tasks, and uncertainty regimes. This role focuses on building and training those systems.

The Role

As a Senior AI/ML Engineer, you will lead the design, training, and deployment of large-scale machine learning models that form the core of Absentia Labs’ AI capabilities. You will work at the boundary between model architecture, training systems, and production infrastructure, with significant ownership over technical direction.

This role is intended for engineers who have trained large models in real production environments, understand the realities of scale, and can reason about both learning dynamics and systems constraints.

What You'll Do

• 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.

• 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.

Bonus If You 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.

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.

What We Offer

• 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.

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.