Senior AI / Machine Learning Engineer

NLP PEOPLE; Seattle (WA); Hybrid; USD 115,000 - 200,000; Full time

Benefits offered by this job

Competitive compensation

Meaningful equity participation

Flexible remote or hybrid work arrangements

Job summary

NLP PEOPLE is seeking a Senior AI/ML Engineer to lead the design, training, and deployment of large-scale machine learning models. The role involves significant ownership over technical direction and collaboration with interdisciplinary teams on machine intelligence applied to complex scientific data.

The ideal candidate has over 5 years of experience in machine learning, particularly in production environments, and strong expertise in training large-scale models. We offer competitive compensation with equity opportunities and flexible work arrangements.

Qualifications

• 5+ years of experience in machine learning or applied AI roles.
• Hands-on expertise with LLMs, diffusion models, and/or GNNs.
• Strong software engineering fundamentals including clean code and debugging.

Responsibilities

• Design, train, and evaluate large-scale models.
• Own end-to-end training pipelines.
• Collaborate closely with data engineers to define ML-ready datasets.

Skills

Training large-scale models

Experience with LLMs

Proficiency in PyTorch

Strong software engineering fundamentals

Distributed training

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 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 (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.

What We Offer

• Competitive compensation, including meaningful equity participation.
• 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.

Compensation Range: $115K – $200K