Real-Time AI Systems Research Scientist

adaption; New York (NY); Sur place; USD 180 000 - 260 000; Plein temps

Avantages offerts par ce poste

Flexible work

Annual travel stipend

Lunch stipend

Comprehensive medical benefits

Résumé du poste

Adaption seeks a PhD-level researcher to drive real-world impact by building efficient, gradient-free learning and real-time interaction into products. You will push the boundaries of model efficiency and global data interaction in a world where synthetic data enables broader experimentation.

Join a team that values tangible results, cross-disciplinary collaboration, and scalable computing in a Bay Area–leaning research culture, with a distributed global footprint.

Qualifications

• PhD or equivalent research experience in computer science.
• 4-5+ years of industry experience.
• Deep expertise in at least one area: model efficiency, real-time alignment or algorithmic optimization.
• Systems thinking ability to understand and optimize across the full ML stack.
• Strong programming skills in Python. Experience with deep learning frameworks (PyTorch, JAX, TensorFlow).
• Knowledge of model optimization techniques (RLHF, finetuning).
• Experience in an industry lab with computing at scale.

Responsabilités

• Innovation: innovate on our product to co-design algorithms that react real-time to product signal and feedback. Design new ways of giving feedback that drive better algorithms.
• Cross-Stack Optimization: collaborate across software, hardware, and algorithmic domains to achieve system-wide efficiency gains
• Measure what matters: we believe strongly that the ultimate signal of value is whether we have real world impact. Our current algorithms are capable of interacting with the world which is why product matters so much.

Connaissances

Python programming

PyTorch

JAX

TensorFlow

Model optimization

Real-time learning

Systems thinking

DL scale computing

Formation

PhD in Computer Science or related field

4-5+ years of industry experience

Outils

PyTorch

JAX

TensorFlow

Description du poste

Adaption seeks a PhD-level researcher to drive real-world impact by building efficient, gradient-free learning and real-time interaction into products. You will push the boundaries of model efficiency and global data interaction in a world where synthetic data enables broader experimentation.

Join a team that values tangible results, cross-disciplinary collaboration, and scalable computing in a Bay Area–leaning research culture, with a distributed global footprint.