Adaptive ML is building a reinforcement learning platform to tune, evaluate, and serve specialized language models. We are pioneering the development of task-specific LLMs using synthetic data, creating the foundational tools and products needed for models to self-critique and self-improve based on simple guidelines. Adaptive Engine enables companies to build and deploy the best LLMs for their business. Our founders previously worked together to create state-of-the-art open LLMs. We closed a $20M seed with Index & ICONIQ in early 2024 and are live with our first enterprise customers (e.g., AT&T).
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-role, describing a generic position in our Technical Staff. If any of the below seems like a fit, please apply!
As a Member of Technical Staff, you will contribute to building the foundational technology that powers Adaptive ML, primarily by working on our internal LLM Stack, Adaptive Harmony. We believe that generative AI is best approached as a “big science”--combining large-scale engineering with rigorous empirical research. As such, we emphasize scalability and systematic, empirical demonstrations in our approach. We are looking for self-driven, business-minded, and ambitious individuals interested in supporting real-world deployments of a highly technical product. As this is an early role, you will have the opportunity to shape our research efforts and product as we grow.
This is an in-person role based at our Paris or New York 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.
Generally,
Build the foundational technology powering Adaptive, with a focus on high-performance software engineering and large-scale RL research;
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.
On the engineering side,
Write high-quality software in Rust, with a focus on performance and robustness;
Profile dedicated GPU kernels in CUDA or Triton, optimizing across latency/compute-bound regimes for complex workloads;
Identify and resolve bugs in large distributed systems, at the intersection of software and hardware correctness.
On the research side,
Conduct research on large language models or diffusion models, systematically exploring how reinforcement learning can be used to personalize models;
Reproduce results from the RL, LLM, and diffusion literature, distinguishing the noise from the groundbreaking;
Own a research agenda, with a bias for at-scale, systematic empirical research.
Nearly all members of our Technical Staff hold a position that is a blend of engineering and research.
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 programming skills, especially regarding distributed problems where performance is key;
Contributions to relevant open-source projects, such as efficient implementations of models and RL;
A track record of publications at top-tier machine learning venues (e.g., NeurIPS, JMLR);
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.