Machine Learning Researcher

Machine Learning Research Engineer

Tech Start-Up

San Francisco-based

We’ve partnered with one of the most ambitious and technically rigorous AI research labs in the world. Based in San Francisco, this team is developing foundation models from the ground up — across LLMs, generative video, and robotics — without relying on pre-trained architectures or fine-tuning shortcuts.

They are hiring Senior ML Researchers with deep expertise in model development, experimentation, and deployment. If you've built state-of-the-art models end-to-end — from raw data to production — and thrive on solving complex modeling problems at scale, this could be a perfect fit.

Key Responsibilities:

Design and implement deep learning models from scratch — across language, vision, and robotics tasks.

Own the full model development lifecycle: data prep, architecture design, training, evaluation, and deployment.

Make informed tradeoffs between model choices (e.g., ViTs vs CNNs, diffusion vs discriminative models).

Explore cutting-edge techniques: ViT, CLIP, LLMs, YOLO, diffusion models, transformers, sparse convolutions.

Integrate domain knowledge (e.g. physics constraints, simulation data) into models for better generalization and control.

Work closely with infra engineers to scale model training and evaluation.

Requirements:

3–15 years of experience in deep learning or applied ML research roles.

Proven track record building and deploying models end-to-end — not just fine-tuning existing ones.

Deep understanding of model architectures, training dynamics, evaluation metrics, and optimization techniques.

Familiarity with modeling challenges like class imbalance, domain adaptation, simulation-to-real gaps, and transfer learning.

Strong intuition for architecture design and tradeoffs across vision, language, and generative tasks.

Mission-driven, collaborative, and comfortable in a fast-paced, research-grade environment.