Research Engineer, Post-Training (All Industry Levels)
Develops alignment algorithms, data pipelines, and sampling methods to optimize post-training AI models for performance and efficiency. Requires PhD or equivalent, ML expertise including reinforcement learning and transformers, and production code experience.
225k – 400k/yr
RemoteML Engineering
About the role
Responsibilities
Develop alignment algorithms and loss functions to improve data sample efficiency.
Write data pipelines to process diverse web data into a format models can ingest.
Identify quality signals to understand our model’s performance in the real world.
Design sampling algorithms to improve serving efficiency of large generative models.
Requirements
At least PhD (or equivalent).
Write clear and clean production-facing and training code.
Experience working with GPUs (training, serving, debugging).
Experience with data pipelines and data infrastructure.
Strong understanding of modern machine learning techniques (reinforcement learning, transformers, etc).
Track-record of exceptional research or creative applied ML projects.
Nice to Have
Experience with product experimentation and A/B testing.
Experience training large models in a distributed setting.
Familiarity with ML deployment and orchestration (Kubernetes, Docker, cloud).
Publications in relevant academic journals or conferences in the field of machine learning.
Skills
Reinforcement LearningTransformersPyTorchGpusData PipelinesKubernetesDockerGCPAlignment AlgorithmsDistributed Training
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