Adaption Labs
Research Engineer
About this role
Research Engineer role focused on building efficient, adaptive ML systems that continuously learn from production data. You'll own the design and implementation of data pipelines for model retraining and fine-tuning, working closely with the founding team at the intersection of applied research and product development.
What you'll do
- Lead development and deployment of efficient, adaptive ML systems in production environments
- Own implementation of data products and closed-loop feedback systems from production to model retraining
- Design and build data pipelines for continuous model fine-tuning and online learning
- Address novel technical challenges related to data strategy, evaluation, and model adaptation
- Collaborate with founding team to align technical implementation with research direction and product vision
- Ensure observability and monitoring of ML systems in production
What they're looking for
- Machine learning systems and applied research
- Online learning and reinforcement learning
- Model retraining and fine-tuning pipelines
- Python and strong software engineering practices
- Observability tools (OpenTelemetry, Docker, Grafana)
- Data pipeline design and implementation
- Efficient ML architectures
- Technical communication and cross-team alignment
Benefits
- Flexible work with Bay Area collaboration and global-first distributed option
- Annual travel stipend (Adaption Passport) for international exploration
- Weekly meal allowance for food delivery or groceries
- 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 describe a production ML system where you closed the loop from live signals back into model retraining? What challenges did you face?
- How would you design a data pipeline to efficiently retrain or fine-tune a model on production data at scale?