Machine Learning Researcher

This job was selected by the SpringCube team to help AI, Data and Cloud Engineers discover relevant San Francisco Bay Area employers. Sign up to view the full employer details and apply directly with the hiring company.

Company Overview

A technology organization is developing solutions designed to improve how humans interact and communicate with computers. Its research focuses on non-invasive brain-computer interfaces, applying deep learning techniques to large-scale neural datasets to decode internal thought and advance neural decoding technologies.

The organization is seeking a Machine Learning Researcher to join its core research and development team. The role will focus on designing and implementing advanced machine learning models for EEG-based neural decoding, contributing to high-impact research, and developing foundational infrastructure for brain-decoding systems.

The successful candidate will work closely with experts in neural decoding and artificial intelligence to explore new approaches in brain-computer interfaces. The position combines machine learning research with rigorous software engineering, requiring the ability to translate promising research concepts into reliable, production-quality systems.

Key Responsibilities

• Develop, train, and refine state-of-the-art deep learning models for neural decoding.
• Apply recent advances in machine learning architectures, including transformers and diffusion models.
• Explore novel approaches for modeling high-frequency, time-series EEG data and related data modalities.
• Translate research findings into production-grade code that integrates with internal brain-computer interface systems.
• Collaborate with neuroscientists and machine learning engineers to develop scalable, end-to-end neural-decoding systems.
• Conduct research that advances machine learning and neural decoding capabilities.
• Publish research findings at leading machine learning and artificial intelligence conferences, including NeurIPS, ICML, ICLR, and CVPR.
• Contribute to open-source machine learning and research communities where appropriate.
• Participate in the development of foundational infrastructure supporting neural-decoding research and applications.

Required Qualifications

• Bachelor’s degree in Computer Science or a related field such as Artificial Intelligence, Computational Neuroscience, Mathematics, or Biomedical Engineering, with 5–7 years of experience in machine learning research or applied machine learning engineering; or
• Master’s or Ph.D. degree in Computer Science or a related field such as Artificial Intelligence, Computational Neuroscience, or Biomedical Engineering, with at least 3 years of experience in machine learning research or applied machine learning engineering.
• Demonstrated record of high-quality machine learning research through publications at leading conferences or respected journals.
• Research experience involving venues such as NeurIPS, ICML, ICLR, or CVPR.
• Strong proficiency in Python and PyTorch.
• Familiarity with modern machine learning tooling and distributed training.
• Experience contributing to production-quality codebases using modern code-review and software development practices.
• Strong understanding of deep learning and machine learning research methodologies.

Preferred Qualifications

• Ph.D. in Computer Science or a related field.
• Experience working in a high-profile machine learning research laboratory.
• Experience developing machine learning models for neural decoding or brain-computer interfaces.
• Experience with multimodal representation learning, including CLIP-style contrastive objectives and masked autoencoding.
• Experience with generative modeling, including diffusion models, transformer decoders, and latent GANs.
• Experience with temporal sequence modeling, including state-space models, STFT-aware transformers, and RWKV.
• Experience working with multimodal or time-series datasets.

Areas of Relevant Expertise

• Multimodal Representation Learning: CLIP-style contrastive objectives and masked autoencoding.
• Generative Modeling: Diffusion models, transformer decoders, and latent GANs.
• Temporal Sequence Modeling: State-space models, STFT-aware transformers, and RWKV.

Disclaimer

SpringCube curates tech job listings from various company websites to support tech professionals globally.

1.No Endorsement: Job ads on SpringCube do not imply endorsement of their authenticity or quality.

2.No Client Relationship: This company is not a client of SpringCube unless stated.

3.To Apply: Click the “Apply” button to be redirected to the hiring company’s application page for this job.

4.No Liability: SpringCube is not liable for inaccuracies.**