Build and optimize large-scale ML systems for safe, steerable AI, handling infrastructure, experiments, and dev tooling. Requires strong software engineering and interest in ML research.
350k – 500k/yr
HybridML Engineering
About the role
You may be a good fit if you:
Have significant software engineering experience
Are results-oriented, with a bias towards flexibility and impact
Pick up slack, even if it goes outside your job description
Enjoy pair programming
Want to learn more about machine learning research
Care about the societal impacts of your work
Strong candidates may also have experience with:
High performance, large-scale ML systems
GPUs, Kubernetes, Pytorch, or OS internals
Language modeling with transformers
Reinforcement learning
Large-scale ETL
Representative projects:
Optimizing the throughput of a new attention mechanism
Comparing the compute efficiency of two Transformer variants
Making a Wikipedia dataset in a format models can easily consume
Scaling a distributed training job to thousands of GPUs
Writing a design doc for fault tolerance strategies
Creating an interactive visualization of attention between tokens in a language model
Compensation
Annual Salary: $350,000 — $500,000 USD
Skills
PyTorchKubernetesGpusTransformersReinforcement LearningDistributed TrainingETLMachine Learning Systems
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