Adaption Labs
Research Scientist / Research Engineer
About this role
Join a research-focused team building efficient AI systems that adapt in real-time to user feedback and world interactions. You'll innovate on algorithms and co-design solutions across software, hardware, and ML domains, with emphasis on synthetic data optimization and measurable real-world impact.
What you'll do
- Develop and innovate algorithms that react in real-time to product signals and user feedback
- Design feedback mechanisms that drive algorithmic improvements
- Optimize across the full ML stack—software, hardware, and algorithms—for system-wide efficiency
- Measure and validate real-world impact of research contributions
- Collaborate cross-functionally on model efficiency and algorithmic optimization
What they're looking for
- Deep learning frameworks (PyTorch, JAX, or TensorFlow)
- Python programming
- Model optimization techniques (RLHF, fine-tuning)
- Systems thinking and ML stack optimization
- Expertise in model efficiency, real-time alignment, or algorithmic optimization
- Large-scale distributed computing experience
- Research experience in computer science
Benefits
- Global remote work with Bay Area in-person collaboration options
- Annual travel stipend (Adaption Passport) to explore new countries
- Weekly meal allowance for groceries or takeout
- Comprehensive medical benefits
- Generous paid time off
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Adaption Labs
Adaption Labs builds machine learning systems and infrastructure that solve real-world customer problems at scale, from efficient AI inference to production ML deployments. The company is hiring Applied ML Engineers, distributed systems engineers, and research-focused technologists to develop adaptive AI solutions and bridge the gap between experimental research and reliable, deployed systems.
View all jobs at Adaption LabsLikely interview questions
- Can you walk us through a specific project where you optimized model efficiency across the full ML stack—what were the bottlenecks and how did you approach cross-domain collaboration?
- Tell us about your experience with synthetic data generation and optimization. How have you used it to improve model performance or coverage in areas with limited real-world data?