Clera
Founding Machine Learning Engineer
About this role
Join a Series A AI startup in Mountain View as a founding ML Engineer, building production-grade models and data systems for frontier AI labs. You'll own end-to-end ML pipelines, from LLM fine-tuning to distributed inference, shaping the company's technical foundation from the ground up.
What you'll do
- Design and optimize end-to-end ML pipelines including data ingestion, model training, and deployment
- Implement and fine-tune LLMs, embeddings, and generative models for production applications
- Develop distributed training and inference systems leveraging cloud infrastructure
- Collaborate with data and product teams to translate requirements into measurable ML outcomes
- Build model monitoring, evaluation, and continual learning frameworks
- Establish ML best practices around versioning, reproducibility, and scalability
What they're looking for
- Python programming
- PyTorch, TensorFlow, or JAX
- ML fundamentals (preprocessing, feature engineering, training, optimization)
- Distributed systems and cloud ML infrastructure (AWS, GCP, Azure)
- MLOps tools (Weights & Biases, MLflow)
- Large-scale data handling and high-throughput systems
- LLM fine-tuning and generative model deployment
- Autonomous problem-solving and bias toward action
Benefits
- Base salary $220,000–$300,000 USD annually
- Equity participation as founding team member
- Opportunity to establish technical direction and culture
- Remote-friendly with on-site presence in Mountain View
- High-ownership role with direct impact on company strategy
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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 end-to-end ML project you shipped to production—what were the key bottlenecks and how did you optimize for inference latency?
- Tell us about your experience fine-tuning or training large language models. What frameworks did you use, and how did you handle distributed training?