Clera
Founding Machine Learning Engineer
About this role
Join an early-stage AI company as a founding ML engineer to build production machine learning systems serving frontier labs and enterprises. You'll own the complete ML pipeline from data ingestion to deployment, working autonomously to establish technical infrastructure and best practices.
What you'll do
- Design and optimize end-to-end ML pipelines including data ingestion, training, and deployment
- Implement and fine-tune LLMs, embeddings, and generative models for production applications
- Build distributed training and inference systems on cloud infrastructure
- Establish model monitoring, evaluation, and continual learning frameworks
- Collaborate with data and product teams to translate requirements into measurable ML outcomes
- Create best practices for model versioning, reproducibility, and scalability
What they're looking for
- Python programming
- PyTorch, TensorFlow, or JAX
- Distributed systems and cloud ML infrastructure (AWS, GCP, Azure)
- MLOps tools (Weights & Biases, MLflow)
- Data preprocessing and feature engineering
- LLM and generative model fine-tuning
- Model optimization and inference
- Large-scale data handling
Benefits
- Founding team role with significant technical influence
- Build ML systems from scratch
- Work on frontier AI applications
- Autonomous ownership of full ML lifecycle
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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
- Describe a complex ML pipeline you've built end-to-end—what were the bottlenecks and how did you optimize for production?
- Walk us through your experience fine-tuning large language models or generative models. What trade-offs did you make between performance and inference cost?