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ML Engineer — Tilde Research
Location:
San Francisco, CA (Onsite)
Compensation: $150,000 – $350,000 base + 0.5% – 1% equity
About Tilde Research
Tilde Research is a frontier AI research lab focused on mechanistic understanding of model architectures and optimization. They work on reasoning, pretraining, scaling laws, and architecture-level experimentation. Backed with strong funding and compute resources, Tilde operates with a small, highly selective team at the intersection of research and engineering.
About The Role
This is a research engineering role focused on implementing and scaling model development work at the frontier of AI. You will translate research ideas into working systems, own end-to-end training pipelines, and operate across transformer training, distributed systems, and evaluation. The role sits between research and engineering — you're expected to read papers, implement architectures, debug large-scale training issues, and reason about model behavior and optimization at a deep level.
What You'll Own
Compensation: $150,000 – $350,000 base + 0.5% – 1% equity
About Tilde Research
Tilde Research is a frontier AI research lab focused on mechanistic understanding of model architectures and optimization. They work on reasoning, pretraining, scaling laws, and architecture-level experimentation. Backed with strong funding and compute resources, Tilde operates with a small, highly selective team at the intersection of research and engineering.
About The Role
This is a research engineering role focused on implementing and scaling model development work at the frontier of AI. You will translate research ideas into working systems, own end-to-end training pipelines, and operate across transformer training, distributed systems, and evaluation. The role sits between research and engineering — you're expected to read papers, implement architectures, debug large-scale training issues, and reason about model behavior and optimization at a deep level.
What You'll Own
- Implement research ideas in PyTorch or JAX and translate them into production-level systems
- Run transformer training across distributed environments
- Build and operate end-to-end pipelines covering data, training, and evaluation
- Identify and resolve divergence, instability, and throughput issues at scale
- Optimize training performance and system efficiency
- Work closely with researchers to evaluate architectures and learning dynamics
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Contribute to experimentation across model behavior and optimization strategies
- Strong experience with PyTorch or JAX
- Hands-on experience with transformer training, distributed systems, and evaluation frameworks
- Ability to debug large-scale training issues including instability and performance bottlenecks
- Experience implementing research ideas and custom architectures
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Strong understanding of model behavior, optimization, and learning dynamics
- Experience with Megatron-LM, DeepSpeed, xformers, or similar frameworks
- Background at frontier AI labs or top research engineering teams
- Open source contributions to ML frameworks
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Strong mathematical foundation in optimization or learning theory
- MLOps or infrastructure-focused profiles without model training experience
- Applied ML roles not involved in pretraining or model development
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Backgrounds focused on deployment rather than training systems
- Role is fully onsite in San Francisco — please only apply if you can commit to this
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Multiple headcount | Completely open pipeline — strong opportunity to be first in
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