Modelling Resident (8-Month Contract)
adaption · United States
- Hybrid
- Contract
- $80,000 / year
- United States
Tailored resume — keyword-matched to this role.
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Job highlights
- Join a frontier lab as a Modelling Resident.
- Work on key production and research projects.
- Develop adaptive algorithms for real-time learning.
- Optimize across software, hardware, and algorithms.
- Focus on real-world impact and technical excellence.
About the role
The Role
A modelling residency puts you in the center of a frontier lab. You'll be embedded in real work: placed on key production and research projects and mentored directly by our modelling team. We're building our frontier innovation around efficiency, gradient-free exploration, real-time learning, and interface design. By the end of the residency, our hope is that you leave with a track record you're proud to put on your resume and experiences that meaningfully upskill you.Responsibilities
- Innovation: Build the future of adaptive algorithms that continuously learn. Co-design algorithms that react in real-time to product signal and feedback, and explore new ways of capturing feedback that make those algorithms better.
- Cross-Stack Optimization: Collaborate across software, hardware, and algorithmic domains to drive system-wide efficiency gains.
- Measure What Matters: We believe the ultimate signal of value is real-world impact. The smartest algorithm is one capable of interacting with the world — that's why product signal matters so much to how we work.
Qualifications
We value impact over credentials. Above all, we're looking for great teammates who make work feel lighter and aren't afraid to go out on a limb with bold ideas. You don't need to check every box - but you do need to be adaptable.- A degree or equivalent research/engineering experience in a computer science field.
- Genuine interest in, and at least one project touching: model efficiency, synthetic data, interfaces, real-time alignment, or algorithmic optimization.
- Systems thinking — the ability to understand and optimize across the full ML stack.
- Strong Python skills and experience with deep learning frameworks (PyTorch, JAX, or TensorFlow).
- Familiarity with model optimization techniques such as RLHF and fine-tuning.
- Strong communication and self-awareness — you know how to collaborate in a remote environment and you're open to feedback.
- You care about technical excellence and last mile impact. We value impact more than effort, and about owning outcomes end to end.
About Us
Most AI is frozen in place - it doesn't adapt to the world. We think that's backwards. Our mandate is to build efficient intelligence that evolves in real-time. Our vision is AI systems that are flexible, personalized, and accessible to everyone. We believe efficiency is what makes this possible - it's how we expand access and ensure innovation benefits the many, not the few. We believe in talent density: bringing together the best and most driven individuals to push the boundaries of continual adaptation. We're looking for builders and creative thinkers ready to shape the next era of intelligence.Key skills/competency
- Modelling Resident
- Adaptive Algorithms
- Machine Learning
- Python
- Deep Learning
- PyTorch
- JAX
- TensorFlow
- Model Optimization
- Real-time Learning
Skills & topics
- Modelling Resident
- Machine Learning
- Python
- Deep Learning
- AI
- Algorithm Optimization
- Real-time Learning
- Frontier Lab
- Research
- Contract
How to get hired
- Tailor your resume: Highlight projects in model efficiency, synthetic data, or real-time alignment.
- Showcase your impact: Quantify your achievements and focus on outcomes, not just effort.
- Demonstrate systems thinking: Emphasize your ability to optimize across the full ML stack.
- Prepare for technical interviews: Be ready to discuss Python, deep learning frameworks, and optimization techniques.
- Communicate your collaboration skills: Highlight experience working effectively in a remote environment.
Technical preparation
Practice Python and deep learning frameworks.,Study model optimization techniques.,Develop projects in relevant AI fields.,Prepare to discuss ML system design.
Behavioral questions
Describe a bold idea you pursued.,How do you handle feedback on your work?,Give an example of cross-stack collaboration.,How do you ensure your work has impact?
Frequently asked questions
- What is the duration of the Modelling Resident contract at Adaption?
- The Modelling Resident position at Adaption is an 8-month contract, offering a focused period to deeply engage with their frontier lab projects.
- What kind of projects will I work on as a Modelling Resident?
- As a Modelling Resident, you'll be embedded in real work, contributing to key production and research projects focused on adaptive algorithms, gradient-free exploration, real-time learning, and interface design.
- What are the essential qualifications for the Modelling Resident role?
- Adaption values impact over credentials. Essential qualifications include a degree or equivalent experience in computer science, strong Python skills with deep learning frameworks, systems thinking, and a genuine interest in areas like model efficiency or real-time alignment.
- Does Adaption require a specific degree for the Modelling Resident position?
- Adaption values a degree or equivalent research/engineering experience in a computer science field for the Modelling Resident role. They prioritize practical experience and impact over formal credentials.
- What deep learning frameworks are used at Adaption for the Modelling Resident role?
- Adaption utilizes popular deep learning frameworks such as PyTorch, JAX, or TensorFlow. Experience with at least one of these is a key qualification for the Modelling Resident position.
- How important is remote collaboration experience for the Modelling Resident role?
- Strong communication and self-awareness are crucial for the Modelling Resident role, especially in a remote environment. Adaption values candidates who know how to collaborate effectively and are open to feedback.
- What is Adaption's approach to AI development?
- Adaption's core philosophy is building efficient intelligence that evolves in real-time, contrasting with static AI. They focus on adaptive, flexible, and personalized AI systems accessible to everyone, driven by efficiency and talent density.
- How does Adaption measure success for its Modelling Residents?
- Adaption believes the ultimate signal of value is real-world impact. They emphasize product signal and the ability of algorithms to interact with the world, valuing technical excellence and last-mile impact.