Grafton Biosciences is a biotech startup focused on solving disease through groundbreaking innovations in early detection and therapeutics. We are combining cutting-edge synthetic biology, machine learning, and manufacturing to fundamentally extend healthy human lifespans. We’re looking for passionate team members who want to shape the future.

The Role

As the Lead Machine‑Learning Engineer you will spearhead the design and deployment of the large‑scale generative models that propose new therapeutic candidates for cancer and aging. You will collaborate closely with computational‑physics, data‑engineering and biophysics teams.

Key Responsibilities

Architect and implement a high‑capacity generative model (e.g., diffusion or transformer‑based) capable of handling multi‑modal molecular inputs and constraints.
Integrate real‑time feedback from molecular‑physics evaluations and experimental data into model training and inference loops.
Optimize large‑scale distributed training on multi‑GPU clusters, including mixed‑precision, gradient‑checkpointing and fault‑tolerant job orchestration.
Build robust MLOps pipelines (data versioning, automated testing, continuous deployment) to support rapid iteration and reproducibility.

Minimum Qualifications

BS/MS or PhD in Computer Science, Machine Learning, Applied Math, Computational Biology or related field.
4+ years building and shipping large‑scale deep‑learning models (e.g., LLMs, diffusion, graph transformers) in production or open‑source settings.
Expert‑level proficiency with PyTorch and modern distributed‑training frameworks (e.g., PyTorch DDP, Ray, DeepSpeed).
Hands‑on experience optimising GPU workloads (mixed‑precision, kernel fusion, memory‑efficient attention).
Proven MLOps skills: CI/CD, experiment tracking, reproducible data pipelines.
Strong communication skills and a collaborative mindset.

Preferred Qualifications

Prior work with 3‑D or graph neural networks, equivariant architectures or generative models for scientific data.
Familiarity with molecular‑simulation or structural‑biology datasets.
Experience coupling external scoring or physics functions into reinforcement‑learning or diffusion workflows.
Leadership experience in a start‑up or cross‑disciplinary research environment.

What We Offer

Competitive salary ($170 k – $250 k, DOE).
Comprehensive health, dental and vision coverage.
Flexible work arrangement and generous PTO.
Annual budget for conferences, courses and cloud credits.
Opportunity to define a new therapeutic‑design paradigm and see your models progress from silicon to clinic.

How to Apply

Submit your résumé, a link to relevant publications, code repos, or model demos, and a brief note on why you are a good fit for this role. If there is a fit, we will reach out within 2 days and decisions will be made within 1 week.

Job Type: Full-time

Pay: $170,000.00 - $250,000.00 per year

Benefits: