Research Engineer: Multi-Modal Modeling
The modeling team at Enigma is seeking ML Research Engineers to build and scale the next generation of brain foundation models. You will develop robust infrastructure for training large-scale transformer architectures that process continuous, multi-dimensional neural and behavioral time series data. This role focuses on implementing efficient training pipelines, optimizing model architectures, and solving the unique engineering challenges of working with massive neurophysiological datasets. The ideal candidate will have extensive experience implementing and scaling multimodal foundation models and a drive to tackle the computational challenges of modeling biological intelligence. This position offers an opportunity to build the technical foundation for a new understanding of how the brain processes information.
The Enigma Project (https://enigmaproject.ai) is a Stanford-based non-profit research organization, launched in August 2024 with $30M in funding. Our core mission is to leverage deep learning to crack the neural code. We own the full neuroAI pipeline: from neurotechnology development, to neural data collection, to modeling, theory, & analysis.
Role & Responsibilities:
- Implement and optimize the latest machine learning algorithms/models to train multimodal foundation models on neural data
- Develop and maintain scalable, efficient, and reproducible machine-learning pipelines
- Conduct large-scale ML experiments, using the latest MLOps platforms
- Run large-scale distributed model training on high-performance computing clusters or cloud platforms
- Collaborate with machine learning researchers, data scientists, and systems engineers to ensure seamless integration of models and infrastructure
- Monitor and optimize model performance, resource utilization, and cost-effectiveness
- Stay up-to-date with the latest advancements in machine learning tools, frameworks, and methodologies
Key Qualifications:
- Master's or Ph.D. in Computer Science, Machine Learning, or a related field
- 2-3 years of practical experience in implementing and optimizing machine learning algorithms with distributed training using common libraries (e.g., Ray, DeepSpeed, HF Accelerate, FSDP)
- Strong programming skills in Python, with expertise in machine learning frameworks like TensorFlow or PyTorch
- Experience with orchestration platforms
- Experience with cloud computing platforms (e.g., AWS, GCP, Azure) and their machine learning services
- Familiarity with MLOps platforms (e.g., MLflow, Weights & Biases)
- Strong understanding of software engineering best practices, including version control, testing, and documentation
Preferred Qualifications:
- Familiarity with training, fine-tuning, and quantization of LLMs or multimodal models using common techniques and frameworks (LoRA, PEFT, AWQ, GPTQ, or similar)
- Familiarity with modern big data tools and pipelines such as Apache Spark, Arrow, Airflow, Delta Lake, or similar
- Experience with AutoML and Neural Architecture Search (NAS) techniques
- Contributions to open-source machine learning projects or libraries
What We Offer:
- Work on a collaborative and uniquely positioned project spanning several disciplines, from neuroscience to artificial intelligence and engineering
- Competitive salary and benefits
- Strong mentoring in career development
Application: Please send your CV and one-page interest statement to: recruiting@enigmaproject.ai