ML Engineer

We are looking for a Machine Learning Researcher / Engineer to join our core ML research team.

Our team operates at the intersection of applied machine learning and fundamental research. We work closely with Converge’s scientific teams to develop models that solve concrete biological problems, while also investigating broader questions about how modern machine-learning methods behave in biological domains.

You will take ownership of ML projects from initial exploration through implementation and evaluation. You will work on questions such as how scaling laws, representations, model architectures, and metrics like perplexity translate, or fail to translate from fields such as NLP and computer vision to biological data.

This is a hands-on individual-contributor role for someone who enjoys both building models that work and understanding why they work.

What You’ll Do

• Design, train, and evaluate deep-learning models for biological sequences and other scientific data.
• Translate real biological and product challenges into well-defined machine-learning problems.
• Build robust experimentation and training pipelines using PyTorch and modern ML infrastructure.
• Adapt and critically evaluate techniques from NLP, computer vision, representation learning, and generative modeling in biological settings.
• Investigate broader research questions such as scaling behavior, model evaluation, representation quality, generalization, and the relationship between pretraining metrics and downstream biological performance.
• Collaborate closely with biologists, bioinformaticians, engineers, and other ML researchers.
• Communicate experimental results clearly and help turn successful research into reliable, reusable capabilities.
• Follow and contribute to the latest research in foundation models, generative modeling, and AI for biology.
  
  

• Master’s degree in Computer Science, Machine Learning, Applied Mathematics, Statistics, Physics, Engineering, Computational Biology, or another strongly computational field.
• 3 -5 years of relevant professional or research experience in machine learning.
• Strong understanding of deep learning, including modern architectures, training methods, and evaluation practices.
• Hands on experience developing and training models with SOTA frameworks.
• Experience designing experiments, analyzing results, and making sound decisions under uncertainty.
• Ability to independently own a research or applied-ML project while collaborating effectively across disciplines.
• Curiosity, intellectual honesty, and a willingness to question whether established ML assumptions hold in new domains.
  
  

• Knowledge of biology, bioinformatics, genomics, protein modeling, or related areas. Biology experience is an advantage, but not a requirement.
• Experience with transformers, biological language models, diffusion models, flow matching, or other generative-modeling approaches.
• Experience training models at scale or using distributed-training systems.
• Familiarity with self-supervised learning, representation learning, scaling laws, or foundation-model evaluation.
• Experience turning research prototypes into production-quality ML systems.
• Publications or other evidence of strong experimental research work.