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.
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.
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
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
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.
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
Paid internship
Mentorship and close collaboration with senior engineers and researchers
Exposure to real-world, production AI systems
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