To apply for the Machine Learning Engineer role at Output Biosciences, the following topics and skills are required:
- Machine Learning Fundamentals:
- Model architectures
- Optimization techniques
- Evaluation metrics
- Deep Learning:
- Generative models (e.g., transformers, diffusion models, autoencoders)
- Experience with deep learning frameworks (PyTorch, TensorFlow, or JAX)
- Distributed Systems:
- Working with multi-GPU and multi-node setups
- Scaling models and optimizing performance across large datasets
- Data Pipelines:
- Efficient data management and processing for large-scale biological datasets
- Data loading, splitting, and memory optimization
- Programming:
- Proficiency in Python
- Frameworks:
- Expertise in at least one major deep learning framework (PyTorch, TensorFlow, or JAX)
- Distributed Computing:
- Experience with AWS for training, inference, and deployment
- High-Performance Computing (HPC):
- Experience optimizing ML models for HPC environments (bonus)
- Biological Applications:
- Familiarity with applying ML to biology or chemistry
- Knowledge of systems biology and biological reasoning models
- ML-Ops:
- Managing ML experiments and deployments (bonus)
- Evaluation Frameworks:
- Developing robust evaluation methods
- Ensuring data integrity and avoiding leakage in datasets
- Code Organization:
- Version control and collaborative development practices
- Problem Solving:
- Excellent problem-solving skills and adaptability
- Communication:
- Ability to articulate complex technical concepts clearly
- Ownership & Proactivity:
- A proactive approach to problem-solving and a sense of ownership
- Adaptability:
- Ability to handle ambiguous situations and make decisions in uncertainty
- Open-Source Contributions:
- Contributions to ML open-source projects or publications in AI/ML
- Research Experience:
- Publishing research papers in AI/ML fields
By mastering these areas, you'll be well-prepared for this role.