ML Research Intern

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

About Us:AI needs a new infrastructure layer. We're building it at the company.

Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now.

Our customers include category-defining companies like Lovable, Ramp, Cognition, DoorDash, and Suno. They rely on the company for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale.

We recently raised a $355M Series C at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September.

Our team includes creators of popular open-source projects (e.g.,Seaborn,Luigi), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience.

The Role:

We are looking for PhD research interns with strong research experience in reinforcement learning, machine learning, and foundation models, including large language and multimodal models, to join our research team. This internship is well suited to candidates interested in improving existing methods and developing new techniques for large-scale model training, optimization, and inference, extending models to long-context and long-horizon tasks, and improving inference-time efficiency, reliability, and robustness in high-stakes real-world deployments.

Preferred 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.