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For Companies At a Glance
- Tasks: Lead innovation in real-time AI adaptation and collaborate on cross-stack optimisation.
- Company: Join a mission-driven team at Adaption, shaping the future of adaptable intelligence.
- Benefits: Competitive salary, equity, learning budget, medical benefits, and generous PTO.
- Why this job: Be part of the founding team, making a real impact in AI efficiency research.
- Qualifications: Deep expertise in model efficiency or algorithmic optimisation; strong Python programming skills.
- Other info: Dynamic environment with high ownership and career-defining opportunities.
The predicted salary is between 36000 - 60000 £ per year.
Join to apply for the Efficiency Research Engineer role at Adaption. We believe the future is adaptable, and not one-size-fits-all. We will lead in real-time efficient adaptation that combines algorithm with innovative interface design. Our global team—based in SF and beyond—brings together top talent in AI innovation. Backed by world-class investors, we’re building Adaptable Intelligence.
Our Research Values
We are extremely driven and focused on one goal: building highly efficient, adaptable intelligence. We work as a single team, focusing on a few key bets at a time. We publish only when our work has real-world impact, prioritising simplicity and performance. Our choices are always motivated by rigor and experimental success, and we share insights to advance the wider ecosystem.
The Role
We are obsessed with efficiency—allowing for real-time evolution of AI depends on making adaptable intelligence extremely efficient. We co-design our algorithms with hardware requirements and serving in mind, and explore new research and algorithms within severe compute budgets. This role is part of the founding team, shaping both the research agenda and the product direction. You’ll collaborate with world-class peers and work at the cutting edge of AI efficiency research, where constraints drive creativity.
Responsibilities
- Innovation: lead our focused bets on real-time adaptation, innovating algorithmic recipes that result in large real-time gains.
- Cross-Stack Optimization: collaborate across software, hardware, and algorithmic domains to achieve system-wide efficiency gains.
- Research & Development: explore new research directions in efficient machine learning, alignment, inference-time scaling, and adaptable systems, focusing on gradient-free and data-efficient techniques for rapid alignment and adaptation.
Qualifications
- Deep expertise in at least one area: model efficiency, distributed systems, hardware acceleration, or algorithmic optimization.
- Systems thinking ability to understand and optimize across the full ML stack.
- Strong programming skills in Python with experience in deep learning frameworks (PyTorch, JAX, TensorFlow).
- Knowledge of model optimization techniques (RLHF, fine-tuning).
- A plus is experience in an industry lab with computing at scale.
What we offer
- Competitive salary + meaningful equity.
- Learning and development budget to support your growth.
- Comprehensive medical benefits and generous PTO.
- Annual travel stipend to explore somewhere new.
- Mission-driven team shaping the future of intelligence, with high ownership and career-defining impact.
Efficiency Research Engineer employer: Adaption
Contact Detail:
Adaption Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Efficiency Research Engineer
✨Tip Number 1
Network like a pro! Reach out to current employees at Adaption on LinkedIn or other platforms. Ask them about their experiences and any tips they might have for landing the Efficiency Research Engineer role. Personal connections can make a huge difference!
✨Tip Number 2
Prepare for the interview by diving deep into the company’s projects and values. Understand their focus on efficiency and adaptability in AI. This will help you tailor your responses and show that you're genuinely interested in contributing to their mission.
✨Tip Number 3
Showcase your skills through practical examples. Be ready to discuss specific projects where you've optimised algorithms or improved system efficiency. Real-world impact is what they’re after, so highlight your achievements!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, it shows you’re serious about joining the team and are familiar with their processes.
We think you need these skills to ace Efficiency Research Engineer
Some tips for your application 🫡
Show Your Passion for Efficiency: When writing your application, let your enthusiasm for efficiency shine through! We want to see how your background and interests align with our mission of building adaptable intelligence. Share specific examples of your work that demonstrate your commitment to innovation and optimisation.
Tailor Your Application: Make sure to customise your application for the Efficiency Research Engineer role. Highlight your expertise in model efficiency or distributed systems, and connect your skills to the responsibilities outlined in the job description. This shows us you’ve done your homework and are genuinely interested in joining our team.
Be Clear and Concise: We appreciate clarity in applications! Keep your writing straightforward and to the point. Use bullet points where appropriate to make it easy for us to digest your qualifications and experiences. Remember, we’re looking for impactful communication just as much as technical skills.
Apply Through Our Website: Don’t forget to submit your application through our website! It’s the best way for us to receive your details and ensures you’re considered among the first 25 applicants. Plus, it helps us keep everything organised on our end, making the process smoother for everyone involved.
How to prepare for a job interview at Adaption
✨Know Your Algorithms
Make sure you brush up on your knowledge of algorithmic optimisation and model efficiency. Be ready to discuss specific techniques you've used in the past, especially those related to real-time adaptation and efficiency gains.
✨Showcase Your Systems Thinking
Demonstrate your ability to think across the full machine learning stack. Prepare examples that highlight how you've optimised systems in previous roles, particularly in collaboration with software and hardware teams.
✨Get Hands-On with Python
Since strong programming skills in Python are a must, be prepared to talk about your experience with deep learning frameworks like PyTorch or TensorFlow. You might even want to bring along a project or two to discuss!
✨Emphasise Real-World Impact
Remember, this role is all about building adaptable intelligence with real-world applications. Be ready to share insights from your past work that demonstrate how your research has led to tangible results or improvements in efficiency.
