ML Research Intern

Modal

New York +1 more

Career-pivot friendly

Who this role is best for

Geared toward PhD candidates comfortable with large-scale model training and inference, with a focus on collaborative research and engineering environments in US-based locations.

Best fit for

PhD researchers with a track record in reinforcement learning and foundation models, especially large language and multimodal systems.
— “PhD research interns with strong research experience in reinforcement learning, machine learning, and foundation models”
Individuals who have published in top-tier machine learning conferences and can translate theoretical research into practical implementations.
— “Publications at leading venues such as NeurIPS, ICML, ICLR, CVPR, CoRL, UAI, JMLR, or TMLR”
Candidates with experience in distributed training and multi-GPU environments who are mission-driven and thrive in cross-functional teams.
— “Familiarity with distributed training, large-scale inference, or multi-GPU environments”

Things to consider

There is an expectation of full-time engagement, as the role is an internship and not a part-time or remote arrangement.
— “We are looking for PhD research interns”

How to stand out

Highlight specific contributions to model optimization or inference efficiency in your research experience.
— “improving inference-time efficiency, reliability, and robustness in high-stakes real-world deployments”
Emphasize your ability to work across research and engineering teams with concrete examples from prior projects.
— “the ability to work effectively across research and engineering teams”
Showcase your experience with large-scale machine learning systems or foundation models in your resume and interview responses.
— “Experience developing and evaluating large-scale models or machine learning systems”
Demonstrate familiarity with containerization and GPU access through relevant technical projects or coursework.
— “instant GPU access, sub-second container starts, and native storage”
Include details about your research in reinforcement learning or foundation models, especially in the context of real-world applications.
— “improving existing methods and developing new techniques for large-scale model training, optimization, and inference”

Pace · SteadyCollaboration · HighAutonomy · MediumDecision Impact · TeamLevel · Intern

Derived from job-description analysis by Serendipath's career intelligence engine.

What success looks like

improves existing methods and develops new techniques for large-scale model training, optimization, and inference
extends models to long-context and long-horizon tasks
improves inference-time efficiency, reliability, and robustness in high-stakes real-world deployments

Typical background

currently pursuing a PhD in computer science, machine learning, or a related fielda demonstrated record of research in reinforcement learning, machine learning, foundation models, or related areas

Skills & requirements

Required

Machine LearningReinforcement LearningFoundation ModelsLarge-scale Model Training

Preferred

Distributed TrainingLarge-scale InferenceMulti-gpu Environments

About the role

Original posting from Modal via Ashby

ABOUT US:
AI needs a new infrastructure layer. We're building it at Modal.
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 https://modal.com/blog/lovable-case-study, Ramp https://modal.com/blog/how-ramp-built-a-full-context-background-coding-agent-on-modal, Cognition, DoorDash, and Suno. They rely on Modal 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 https://modal.com/blog/modal-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 https://github.com/mwaskom/seaborn,Luigi https://github.com/spotify/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.

Source: Modal careers (Ashby)