The role involves tackling complex core problems related to agents acting on behalf of users, covering identity, permissions, and memory structure.
Mindset
What you'll do
You pick work that teaches you something. You’re the kind of researcher who reads the paper, questions the setup, builds the missing system, and shows the work. Original thinking matters to you, but it is not enough on its own. You turn ideas into code, experiments, prototypes, and products people can actually use.
What you'll do
You will work on the core problems that appear when agents need to act on behalf of people and organizations: identity, delegation, permissions, memory, provenance, and auditability. The work starts with unclear questions and ends with systems others can run. You might study how an agent should inherit authority, how permissions should persist across tools, how actions should be recorded and reviewed, or how memory and context should be structured so behavior stays reliable over time.
Mem Protocol San Francisco, California, USA Office
548 Market St, San Francisco, California, United States, 94104
Similar Jobs
Fintech • Machine Learning • Payments • Software • Financial Services
Conduct applied AI research and develop foundation models from design through production deployment. Collaborate with data scientists, software and machine learning engineers, and product managers to build scalable AI products. Research deep learning, language, graph, sequential, and behavioral models; optimize training and inference; reproduce emerging research; deliver platform or solution code; and publish internal or external technical findings.
Top Skills:
AWSAws UltraclustersDeep LearningDglHugging FaceLarge Language ModelsLightningPyTorchPytorch GeometricReinforcement Learning From Human Feedback (Rlhf)
Fintech • Machine Learning • Payments • Software • Financial Services
Conduct applied research and build AI foundation models, including large language and agentic AI systems, through design, training, evaluation, validation, and deployment. Partner with data scientists, engineers, and product managers to translate research into scalable customer-facing products. Lead research initiatives, investigate state-of-the-art methods, optimize large-model training and inference, and guide junior researchers or engineers.
Top Skills:
Aws UltraclustersDeep LearningHugging FaceLarge Language ModelsLightningPyTorchReinforcement Learning From Human Feedback (Rlhf)
Fintech • Machine Learning • Payments • Software • Financial Services
Conduct applied AI research and build foundation models from design through production deployment. Partner with data scientists, software engineers, machine learning engineers, and product managers to develop scalable AI products. Lead research initiatives, optimize training and inference, evaluate emerging methods, translate research into business outcomes, and deliver model libraries or platform code. Work may focus on large language models, deep learning optimization, fine-tuning, robustness, explainability, or reinforcement learning.
Top Skills:
Aws UltraclustersCloud ComputingCompiler DesignDeep LearningGradient CheckpointingHugging FaceInstruction TuningLarge Language ModelsLightningModel CompressionModel SparsificationPyTorchQuantizationReinforcement Learning From Human FeedbackSelf-Supervised LearningSupervised Fine-TuningTransfer Learning
What you need to know about the San Francisco Tech Scene
San Francisco and the surrounding Bay Area attracts more startup funding than any other region in the world. Home to Stanford University and UC Berkeley, leading VC firms and several of the world’s most valuable companies, the Bay Area is the place to go for anyone looking to make it big in the tech industry. That said, San Francisco has a lot to offer beyond technology thanks to a thriving art and music scene, excellent food and a short drive to several of the country’s most beautiful recreational areas.
Key Facts About San Francisco Tech
- Number of Tech Workers: 365,500; 13.9% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Google, Apple, Salesforce, Meta
- Key Industries: Artificial intelligence, cloud computing, fintech, consumer technology, software
- Funding Landscape: $50.5 billion in venture capital funding in 2024 (Pitchbook)
- Notable Investors: Sequoia Capital, Andreessen Horowitz, Bessemer Venture Partners, Greylock Partners, Khosla Ventures, Kleiner Perkins
- Research Centers and Universities: Stanford University; University of California, Berkeley; University of San Francisco; Santa Clara University; Ames Research Center; Center for AI Safety; California Institute for Regenerative Medicine
