PhD ML Research Intern: Scale Large Models & Inference

United States Digital Space LLC; New York (NY); On-site; USD 55,104 - 82,656; Part time

Job summary

United States Digital Space LLC is seeking PhD-level researchers to join our team focused on reinforcement learning, machine learning, and foundation models. The internship aims to advance large-scale model training, optimization, and real-world deployment.

Ideal candidates have published work, strong programming skills, and a track record of collaboration across research and engineering. This role offers exposure to cutting-edge AI infrastructure and scalable systems in a fast-growing

Qualifications

• Currently pursuing a PhD in computer science, machine learning, or a related field.
• A demonstrated record of research in reinforcement learning, machine learning, foundation models, or related areas.
• Experience developing and evaluating large-scale models or machine learning systems.
• Familiarity with distributed training, large-scale inference, or multi-GPU environments.
• Publications at leading venues such as NeurIPS, ICML, ICLR, CVPR, CoRL, UAI, JMLR, or TMLR.
• Strong programming and engineering skills, with the ability to translate research ideas into working implementations.
• A collaborative, mission-driven mindset and the ability to work effectively across research and engineering teams.

Responsibilities

• Join our research team to improve existing methods and develop new techniques for large-scale model training, optimization, and inference.
• Extend models to long-context and long-horizon tasks and improve inference-time efficiency and robustness.
• Collaborate with engineering teams to translate research ideas into deployable systems.

Skills

PhD candidate

Research in ML/RS

Programming skills

Collaboration

Large-scale ML

Education

PhD in CS/ML

Job description

United States Digital Space LLC is seeking PhD-level researchers to join our team focused on reinforcement learning, machine learning, and foundation models. The internship aims to advance large-scale model training, optimization, and real-world deployment.

Ideal candidates have published work, strong programming skills, and a track record of collaboration across research and engineering. This role offers exposure to cutting-edge AI infrastructure and scalable systems in a fast-growing