Machine Learning Researcher

Location: New York, NY
Work Model: Not specified in source; New York-based role
Compensation: $400K to $1M+ total compensation, with flexibility for exceptional candidates

Overview

This role sits at the intersection of machine learning research and high-performance systems engineering within a technically rigorous, well-resourced organization operating in stealth. The mandate is direct: identify what works in modern ML, prove it empirically, and build it into production systems that run at scale.

The position spans the full arc from experimentation to deployment. Work includes model development, performance engineering, GPU computing, and the design of large-scale training and experimentation infrastructure. Research ideas are expected to become real systems, and the environment provides the compute, autonomy, and organizational support to pursue difficult problems properly rather than superficially.

Candidates will work alongside strong researchers, engineers, and quantitative thinkers, with the opportunity to shape computational and architectural strategy for ambitious ML initiatives. The output of this work directly informs high-impact decisions at significant scale.

What You'll Do

• Rapidly prototype, implement, and evaluate state-of-the-art machine learning techniques
• Translate promising research into high-performance production implementations
• Shape the computational and architectural strategy behind large-scale ML initiatives
• Diagnose and remove performance bottlenecks across models, software, GPUs, and hardware
• Design efficient training and experimentation infrastructure for large-scale deep learning
• Build proofs of concept and drive successful ideas into broader production environments

What We're Looking For

• Exceptional foundation in machine learning, computer science, mathematics, or a related quantitative discipline
• Demonstrated track record of turning sophisticated ML research into working systems
• Deep experience with modern machine learning and deep learning frameworks
• Strong command of model training, experimentation, optimization, and computational architecture
• Hands-on experience with GPU computing, performance optimization, or low-level ML systems engineering
• Fluency with PyTorch, JAX, CUDA, XLA, Triton, or comparable technologies
• Ability to reason rigorously about complex computational problems and test new approaches quickly
• Exceptional record of academic, research, technical, or professional achievement

Preferred

• Experience spanning both research publication and production ML systems
• Background in compiler-level or kernel-level optimization for ML workloads

Compensation

Total compensation of $400K to $1M+, with flexibility for candidates who demonstrate exceptional depth. Full-time position based in New York.