This role is ideal for someone who loves pushing the frontiers of NeuroAI by combining deep learning with neuroscience; and identifies equally as a strong software engineer who cares about performance, clarity, reproducibility, and collaborative development.

Key Responsibilities****Model Design & Engineering

• Develop and optimize advanced ML models (generative models, video world models, sequence models) for neuroimaging data (EEG, MEG, fMRI).
• Write clean, efficient, testable code in Python; design components that scale across GPUs and multi-node clusters.
• Debug complex system-level issues (timing, memory, threading, distributed training).

Pipelines & Infrastructure

• Build and maintain scalable ETL, training, and evaluation pipelines (cloud-native on AWS/GCP).
• Contribute to model-serving frameworks and MLOps workflows (versioning, CI/CD, reproducibility, monitoring).
• Ensure experiments are reproducible via structured configs, logging, and artifact storage.

Collaboration & Translation

• Work closely with neuroscience, clinical, ML and software teams to turn prototypes into reliable components.
• Write technical documentation and communicate engineering constraints and research results clearly.

Qualifications****Required

• Ph.D. or Master’s in Computer Science, Computational Neuroscience, Electrical Engineering, Applied Math, or related field.
• 2+ years experience in an applied research engineering role or close collaboration with industry R&D teams.
• Strong deep learning experience (e.g., generative models, computer vision, sequence models).
• Demonstrated ability to develop and ship production-quality software in large collaborative codebases.
• Strong Python engineering skills (PyTorch required).
• Experience with cloud platforms (AWS/GCP/Azure), containers, and distributed training.
• Solid grounding in software engineering practices:
• Git/GitHub workflows
• testing frameworks
• CI/CD
• type checking / linting
• reproducible environments (conda, Docker)
• Excellent communication and documentation abilities.

Preferred

• Experience optimizing models for speed, memory, and multi-GPU performance.
• Experience with real-time video processing or low-latency streaming systems.
• Familiarity with EEG/MEG/fMRI preprocessing or time-series signal processing.
• Knowledge of Bayesian optimization or reinforcement learning for real-time adaptive systems.

Why Join Dandelion Science?

• Work on foundational NeuroAI technology with direct impact on real patients.
• Collaborate with leading teams across neuroscience, AI, clinical research, and product development.
• Push the frontiers of digital twins for the human brain with cutting-edge compute and experimental infrastructure.
• Equity participation and meaningful ownership in a rapidly growing company.
• Join a highly interdisciplinary, ambitious team pushing the boundaries of generative neuromodulation.

Application Process

Please submit your CV, a cover letter, and any relevant publications or code samples to careers@dandelion.science.

Dandelion Science is an equal-opportunity employer committed to diversity and inclusion.