ML Engineer (ONSITE IN SF)
About The Position
We are hiring ML Engineers to implement research ideas reliably and operate full training pipelines end-to-end. This is not a research-only role. This is research-engineering at scale. A seed-stage research-driven ML company focused on mechanistic understanding of model architectures and optimizers. The team studies: Optimizer–architecture co-design Orthogonalized optimizers and manifold-based training Sparse attention mechanics Data-efficient reasoning models Learning dynamics in data-sparse regimes The environment blends academic rigor with industrial compute and speed. The team is deliberately long-term oriented and avoids premature commercialization pressure.
Requirements
- Strong PyTorch or JAX proficiency
- Hands-on transformer training experience
- Experience with distributed training setups
- Debugging divergence and instability
- Ability to read and implement research papers
- Research intuition around optimization and learning dynamics
- High growth slope
Nice To Haves
- Megatron-LM, DeepSpeed, xformers
- End-to-end pipeline ownership
- Research-engineering team experience
- Mathematical depth (optimization, information theory, etc.)
- Competitive programming / theory-heavy background
Responsibilities
- Translate research papers into working PyTorch/JAX implementations
- Run distributed transformer training
- Debug divergence and instability
- Optimize throughput
- Build full pipelines (data → training → evaluation)
- Reason about learning dynamics and architecture tradeoffs
Benefits
- Equity: 0.5% – 1%
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What This Job Offers
Job Type
Full-time
Career Level
Entry Level
Education Level
No Education Listed
