Machine Learning Engineer

About Blank Bio

Blank Bio is an applied AI research lab focused on increasing the success rates of clinical trials. We do this by training RNA foundation models that learn the patterns that shape disease progression and patient response to treatment. We aim to help pharma make more informed decisions in clinical trials by capturing the biology that makes each patient’s tumour unique. 

We’re a technical team of AI scientists and engineers from companies including Recursion, Deep Genomics, DeepMind, and Amazon, and institutions including Memorial Sloan Kettering Cancer Centre, Stanford, and the Vector Institute.

The Role

As a machine learning engineer, you will be responsible for scaling our models, building the training infrastructure, and ensuring reproducibility across large-scale biological datasets. You’ll work closely with research scientists and biologists to turn cutting-edge machine learning into practical, high-impact tools for RNA biology. As an early-stage startup, we move fast, work across disciplines, and embrace ambiguity. We’re looking for people who thrive in dynamic environments, are eager to take ownership, and want to help define both the science and the culture of the company.  

Responsibilities

• Develop and optimize large-scale ML training pipelines for RNA foundation models.
• Implement distributed training systems (multi-GPU/TPU) and optimize performance at scale.
• Build infrastructure for dataset management, preprocessing, and benchmarking.
• Collaborate with scientists to translate biological questions into ML tasks.
• Contribute to the design and evaluation of new architectures, embeddings, and fine-tuning strategies.
• Maintain high-quality engineering standards, including reproducibility, testing, and deployment readiness.

Qualifications

Must-haves

• 3+ years of work experience
• Proficiency in Python and modern deep learning frameworks (PyTorch, JAX, or TensorFlow).
• Hands-on experience training large-scale models (transformers, diffusion, or sequence models).
• Strong background in distributed training, optimization, and performance profiling.
• Track record of building ML systems that scale and ship.

Nice-to-haves

• Experience with biological or messy, real-world scientific data.
• Background in computational biology, bioinformatics, or adjacent fields.
• Experience in early-stage startups or interdisciplinary ML-for-science projects.

Compensation & Benefits

• Competitive salary and meaningful early-stage equity.
• Comprehensive health, dental, and vision coverage.
• Generous vacation and parental leave policies.