ML Scientist: Adversarial Robustness & Efficient Vision

Obsidian; Chicago (IL); On-site; USD 130,000 - 190,000; Full time

Job summary

Obsidian is seeking experienced machine learning researchers to tackle open-ended empirical ML problems across vision and language. You will train and improve deep learning models end-to-end, from data preparation to deployment, focusing on robustness and efficiency.

Responsibilities include training image classifiers and generative models, mitigating data and compute constraints, and refining LLM post-training behaviors.

Qualifications

• 3+ years of machine learning research experience (PhD counts toward this requirement).
• Strong experience with PyTorch, JAX, TensorFlow, or similar ML frameworks.
• Degree from a top-100 university, experience at a FAANG or comparable AI company, or an equivalent research track record through publications or impactful open-source contributions.

Responsibilities

• Train and improve deep learning models end-to-end across vision and language.
• Train image classifiers and generative image models from scratch and fine-tune language models.
• Improve models under limited data, compute, and latency constraints.
• Diagnose and resolve training issues.

Skills

Adversarial robustness

Efficient vision methods

Generative image modeling

LLM fine-tuning

Multilingual pre-training

Data augmentation

Education

3+ years ML research

PhD counts towards requirement

Tools

PyTorch

JAX

TensorFlow

Job description

Obsidian is seeking experienced machine learning researchers to tackle open-ended empirical ML problems across vision and language. You will train and improve deep learning models end-to-end, from data preparation to deployment, focusing on robustness and efficiency.

Responsibilities include training image classifiers and generative models, mitigating data and compute constraints, and refining LLM post-training behaviors.