On the Technical Staff at Adaptive ML, you'll build core infrastructure for large-scale ML systems, including Rust and Python interfaces for high-performance distributed training on hundreds of GPUs, GPU inference kernels in Triton/CUDA, hardware correctness tests, and data pipelines for RL from noisy user interactions. You will support research on large language models and reinforcement learning by designing fair experiments (e.g., DPO vs. PPO), reproducing and analyzing ML literature, experimenting with model steering via adapters, and running and documenting empirical studies. You’ll also write clear, well-structured code, help debug distributed and ML-heavy systems, improve performance and reliability, communicate clearly in a distributed team, and work across both engineering and research domains.
Commitments
This is an open, in-person 6‑month internship within the Technical Staff, based in Adaptive ML’s Paris or New York City office, with the listed background being suggestive rather than strictly required.
Responsibilities
Develop robust software in Rust, interfacing between easy-to-use Python recipes and high-performance, distributed training code running on hundreds of GPUs;
Profile and iterate GPU inference kernels in Triton or CUDA, identifying memory bottlenecks and optimizing latency—and decide how to adequately benchmark an inference service;
Develop and execute an experiment analyzing nuances between DPO and PPO in a fair and systematic way;
Build data pipelines to support reinforcement learning from noisy and diverse user' interactions across varied tasks;
Experiment with new ways to combine adapters and steer the behavior of language models;
Build hardware correctness tests to identify and isolate faulty GPUs at scale.
Generally,
Contribute to the foundational technology powering Adaptive ML, with support and guidance from the team
Help advance projects by implementing features, running experiments, or improving reliability
Communicate clearly about your work and learn to collaborate in a distributed team environment
On the engineering side,
Write clear, well-structured code (primarily in Python; exposure to systems programming is a plus, not a requirement)
Help debug issues in distributed or ML-heavy systems
Learn best practices for performance, testing, and robustness
On the research side,
Assist with research on large language models and reinforcement learning
Reproduce and analyze results from recent ML literature
Support empirical experiments and help document findings
Nearly all members of our Technical Staff work across both engineering and research, and interns are encouraged to explore both areas.
Commitments
This is an open internship role within our Technical Staff.This is an in-person 6 months internship based at our Paris or NYC office.The background below is only suggestive.
Not Met Priorities
What still needs stronger evidence
Requirements
Write clear, well-structured code (primarily in Python; exposure to systems programming is a plus, not a requirement)
Help debug issues in distributed or ML-heavy systems
Learn best practices for performance, testing, and robustness
Assist with research on large language models and reinforcement learning
Reproduce and analyze results from recent ML literature
Comfortable programming in Python
Interest in machine learning, AI systems, or large language models
Curious, proactive, and eager to learn in a fast-paced environment
Familiarity with PyTorch, JAX, or similar frameworks
Preferred Skills
Interest in machine learning, AI systems, or large language models
Coursework or projects in machine learning, distributed systems, or systems programming
Familiarity with PyTorch, JAX, or similar frameworks
Experience with research projects or open-source contributions
Education
(Not required)
– You are in the final year of pursuing (or recently completed) a Master’s degree in computer science, engineering, or a related field
About The Team Adaptive ML is a frontier AI startup building a Reinforcement Learning Operations (RLOps) platform that enables enterprises to specialize and deploy LLMs into production with measurable impact. We provide the core infrastructure to tune, evaluate, and serve specialized models at scale — pioneering task-specific LLM development and running production-ready workflows that serve millions of requests while optimizing for both cost and performance across distributed systems. Our tightly-knit team was previously involved in the creation of state-of-the-art open-access large language models. We raised a $20M seed led by Index Ventures and ICONIQ in early 2024, and we're already live in production with customers including Manulife, AT&T, Deloitte, across travel and financial services — with much more to be announced soon. Our Technical Staff develops the foundational technology that powers Adaptive ML in alignment with requests and requirements from our Commercial and Product teams. We are committed to building robust, efficient technology and conducting at-scale, impactful research to drive our roadmap and deliver value to our customers. About The Role This is an open internship role within our Technical Staff. If any of the below sounds interesting to you, we encourage you to apply. As a Technical Intern, you will contribute to building parts of the foundational technology that powers Adaptive ML, primarily by working on our internal LLM stack, Adaptive Harmony . We believe that generative AI benefits from combining strong engineering with careful experimentation, and interns are exposed to both. You will work closely with experienced engineers and researchers, receive mentorship, and contribute to real projects that support production systems and ongoing research. This role is designed for motivated students or early-career engineers who want hands-on experience in applied machine learning systems. This is an in-person 6 months internship based at our Paris or NYC office. Examples of tasks our Technical Team pursue on a daily basis:
Develop robust software in Rust, interfacing between easy-to-use Python recipes and high-performance, distributed training code running on hundreds of GPUs; Profile and iterate GPU inference kernels in Triton or CUDA, identifying memory bottlenecks and optimizing latency—and decide how to adequately benchmark an inference service; Develop and execute an experiment analyzing nuances between DPO and PPO in a fair and systematic way; Build data pipelines to support reinforcement learning from noisy and diverse user' interactions across varied tasks; Experiment with new ways to combine adapters and steer the behavior of language models; Build hardware correctness tests to identify and isolate faulty GPUs at scale. Your Responsibilities Generally,
Contribute to the foundational technology powering Adaptive ML, with support and guidance from the team Help advance projects by implementing features, running experiments, or improving reliability Communicate clearly about your work and learn to collaborate in a distributed team environment On the engineering side,
Write clear, well-structured code (primarily in Python; exposure to systems programming is a plus, not a requirement) Help debug issues in distributed or ML-heavy systems Learn best practices for performance, testing, and robustness On the research side,
Assist with research on large language models and reinforcement learning Reproduce and analyze results from recent ML literature Support empirical experiments and help document findings Nearly all members of our Technical Staff work across both engineering and research, and interns are encouraged to explore both areas. Your (ideal) background The background below is only suggestive. We welcome applications from candidates with diverse experiences—please apply even if you don’t meet every requirement.
You are in the final year of pursuing (or recently completed) a Master’s degree in computer science, engineering, or a related field Comfortable programming in Python Interest in machine learning, AI systems, or large language models Curious, proactive, and eager to learn in a fast-paced environment Nice-to-haves (not Required)
Coursework or projects in machine learning, distributed systems, or systems programming Familiarity with PyTorch, JAX, or similar frameworks Experience with research projects or open-source contributions Benefits
Paid internship Mentorship and close collaboration with senior engineers and researchers Exposure to real-world, production AI systems
Adaptive ML
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New York, NY