Member of Technical Staff
About the role
Lucid is a small team building real-time interactive video models. The role focuses on creating infrastructure to power large-scale ML training and petabyte-scale data processing with distributed GPU workloads.
Responsibilities
- Build systems to handle petabytes of training data efficiently
- Design and manage distributed training infrastructure across GPU clusters
- Evaluate and implement orchestration systems (we currently use SLURM)
- Work directly with ML researchers to identify and solve infrastructure bottlenecks
- Build the foundational systems that let our team iterate quickly on large-scale experiments
Qualifications
- Strong distributed systems experience with focus on performance optimization
- Deep understanding of ML training infrastructure and distributed training patterns
- Experience with Python and PyTorch in production environments
- Comfortable working with both cloud and bare metal infrastructure
- Self-starter who can evaluate technical trade-offs and make architectural decisions
- Excellent communication and collaboration skills
Skills
Benefits
- Health, dental, and vision insurance (US)
- Regular team events and offsites
- Learning and growth opportunities
About Lucid
interactive video models
We are building universe simulations powered by interactive video models. We train video models that simulate hyper-realistic environments with immersive control, replacing hard-coded game or physics engines with dynamic neural networks. We built the fastest action-conditioned diffusion video model (running at 20+fps on a 4090 gaming gpu) to simulate minecraft. It is 5x faster than other minecraft World Models and was trained with 100x less resources. Our unique insight was relying on aggressive compression in our tokenizer (128x versus the traditional 8x), and because attention scales quadratically with # of tokens our model can run blindingly faster. Now we’re training a hyper realistic world model!