Adaptive ML is helping companies build singular generative AI experiences by democratizing the use of reinforcement learning. We are building the foundational technologies, tools, and products required for models to learn directly from users' interactions and for models to self-critique and self-improve from simple written guidelines. Our tightly-knit team was previously involved in the creation of state-of-the-art open-access large language models such asFalcon-180B. We have closed a $20M seed with Index & ICONIQ, and are already shipping a first version of our platform, Adaptive Engine, to early customers.
Our Technical Staff is responsible for building the foundational technology powering Adaptive ML, in line with requests and requirements identified by our Product and Commercial Staff. We strive to build excellent, robust, and efficient technology, and to conduct at-scale, honest research with high-impact for our roadmap and customers.
As a Member of Technical Staff, you will help build the foundational technology powering Adaptive ML, typically by contributing to our internal LLM Stack, Adaptive Harmony. Harmony is a unified inference+training system, which lowers users' requests and recipes into atomic instructions for GPU workers. Model distribution and ressource scheduling are abstracted away from our users, and fully managed by Harmony itself. This results in extreme flexibility for research scientists in implementing complex agentic workflows involving dozens of models interacting and teaching one another. The codebase makes extensive usage of Rust for most of its logic, Python for modeling and end-users, and dedicated CUDA/Triton kernels for LLM performance.
Some examples of tasks members of our Technical Staff 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;
Build hardware correctness tests to identify and isolate faulty GPUs at scale.
We are looking for self-driven, intense individuals, who value technical excellency, honesty, and growth.
Build the foundational technology powering Adaptive, with a focus on high-performance software engineering;
Write high-quality software in Rust, combining performance and robustness;
Identify and resolve bugs in large distributed systems, at the intersection of software and hardware correctness.
Contribute to our product roadmap, by identifying promising trends and high-impact findings;
Report clearly on your work to a distributed collaborative team, with a bias for asynchronous written communication.
The background below is only suggestive of a few pointers we believe could be relevant; we welcome applications from candidates with diverse backgrounds, do not hesitate to get in touch if you think you could be a great fit even if the below doesn't fully describe you.
A M.Sc./Ph.D. in computer science, or demonstrated experience in software engineering, preferably with a focus on machine learning;
Strong Rust skills, or an inclination to learn, especially regarding distributed problems where performance is key
Contributions to relevant open-source projects, such as efficient implementations of models and RL;
Passionate about the future of generative AI, and eager to build foundational technology to help machines deliver more singular experiences.
Comprehensive medical (health, dental, and vision) insurance;
401(k) plan with 4% matching (or equivalent);
Unlimited PTO — we strongly encourage at least 5 weeks each year;
Mental health, wellness, and personal development stipends;
Visa sponsorship if you wish to relocate to New York or Paris.
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