Clera
Founding Engineer - ML Research
About this role
Join a founding-stage AI evaluation company as an ML research engineer to build and scale the research infrastructure serving frontier AI labs. You'll design and train advanced models, develop scalable experimentation pipelines, and translate cutting-edge research into production systems.
What you'll do
- Design, train, and evaluate ML models including LLMs, diffusion models, and domain-specific architectures
- Develop scalable experimentation pipelines for data processing, model training, and evaluation workflows
- Optimize training throughput and data quality in collaboration with data and infrastructure teams
- Contribute to open research, internal benchmarks, and emerging techniques in multimodal and generative AI
- Rapidly prototype research insights and productionize them into usable tools and models
- Establish technical standards for research rigor, documentation, and reproducibility
What they're looking for
- PyTorch, JAX, or TensorFlow
- Transformer and diffusion model architectures
- Distributed training and optimization
- Data processing and evaluation metrics
- Research paper implementation and prototyping
- Production-grade Python engineering
- Multimodal and generative AI paradigms
- Systems thinking and scalability design
Benefits
- Equity compensation
- Founding-level influence and ownership
- Work directly with founding team
- On-site in Mountain View
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Clera
Clera builds an agentic operating system that automates complex workflows and processes through AI agents, with a platform designed to simplify distributed infrastructure management for developers. The company is hiring Founding Engineers, Customer Engineers, and Product Engineers to develop both backend systems and user-facing interfaces across their AI automation products.
View all jobs at CleraLikely interview questions
- Walk us through a recent ML research project where you translated a paper into a working prototype—what were the key challenges?
- How have you optimized distributed training pipelines, and what bottlenecks did you encounter?