Alljoined
Software Engineer
About this role
Alljoined seeks a Software Engineer to build infrastructure powering brain-computer interface research. You'll develop data pipelines for processing large-scale EEG datasets and create tools that enable neural decoding experiments alongside world-class researchers.
What you'll do
- Design and maintain high-performance data pipelines for massive EEG dataset processing and storage
- Build infrastructure for physical experiments and real-time data analysis workflows
- Develop visualization tools and front-end systems for experimental stimuli
- Manage cloud infrastructure, dependencies, and full software stacks
- Debug low-level system performance issues and architect foundational services
- Own complete technical lifecycle from design through production deployment
What they're looking for
- Python (advanced proficiency)
- Systems-level architecture
- High-performance data pipeline development
- Cloud infrastructure management
- Full-stack software engineering
- Real-time visualization systems
- Python performance optimization (Cython, profiling)
- Machine learning infrastructure development
Benefits
- Comprehensive health, vision, and dental insurance
- Competitive equity compensation
- Housing support options
- Visa sponsorship
- Unlimited PTO
- 3% 401k matching
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Alljoined
Alljoined builds infrastructure and machine learning systems for non-invasive brain-computer interface research, focusing on processing and decoding EEG neural signals at scale. The company is hiring Software Engineers, Machine Learning Researchers, and Data Infrastructure Engineers to develop data pipelines, deep learning models, and backend systems that power neural decoding experiments.
View all jobs at AlljoinedLikely interview questions
- Walk us through a high-performance data pipeline you've built for processing large datasets. What were the bottlenecks and how did you optimize for speed and reliability?
- Describe your experience with Python systems-level architecture. Have you worked on performance optimization, and if so, what tools or techniques did you use?