Real-Time Adaptive ML Engineer | Cross-Stack Optimizer

adaption; New York (NY); Remote; USD 120,000 - 180,000; Full time

Job summary

adaption in New York is seeking a machine learning engineer to help build adaptive algorithms that learn from real-time feedback. You will collaborate across software, hardware, and ML to drive system-wide efficiency and measure real-world impact via product signals.

We value practical results, strong Python skills, and experience with DL frameworks. This role emphasizes remote collaboration and end-to-end ownership of outcomes.

Qualifications

• We value impact over credentials and adaptability in a remote environment.
• A CS degree or equivalent research/engineering experience is expected.
• Projects touching model efficiency, synthetic data, interfaces, real-time alignment, or algorithmic optimization are relevant.

Responsibilities

• Innovation: Build adaptive algorithms that learn and adapt in real time.
• Cross-stack optimization across software, hardware, and ML domains for system-wide efficiency.
• Measure impact through real-world product signals and feedback.

Skills

Python

DL frameworks

Systems thinking

RLHF & fine-tuning

Remote collaboration

Real-time alignment

Education

CS degree or equivalent

Tools

PyTorch

JAX

TensorFlow

Job description

adaption in New York is seeking a machine learning engineer to help build adaptive algorithms that learn from real-time feedback. You will collaborate across software, hardware, and ML to drive system-wide efficiency and measure real-world impact via product signals.

We value practical results, strong Python skills, and experience with DL frameworks. This role emphasizes remote collaboration and end-to-end ownership of outcomes.