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# Founding Machine Learning Engineer

- **Company:** ONE STOP COLLECTIBLE CORP
- **Location:** Mountain View, CA, United States
- **Experience:** Experienced
- **Salary:** $220,000.0 - $300,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Microsoft Azure, Big Data, Data Cleansing, Distributed Systems, Python (Programming Language), Tensorflow, Feature Engineering, Data Ingestion, Pytorch, Machine Learning Operations, Software Version Control
- **Published:** September 4, 2026
- **Apply:** https://startup.jobs/founding-machine-learning-engineer-clera-9807800

## About the Role

* 3-10 years of experience as an ML Engineer, Applied Scientist, or Research Engineer.
 * Proficiency in Python and at least one major ML framework: PyTorch, TensorFlow, or JAX.
 * Strong grasp of ML fundamentals - data preprocessing, feature engineering, model training, and optimization.
 * Hands-on experience with distributed systems and cloud ML infrastructure (AWS, GCP, or Azure).
 * Familiarity with MLOps tooling such as Weights & Biases or MLflow.
 * Comfort working with large datasets and high-throughput systems.
 * A bias for action, ability to work autonomously, and genuine enthusiasm for building from scratch.

## Description

This is a founding-level ML engineering role at an early-stage AI data and services company, building core machine learning systems from the ground up alongside a small, high-ownership team. You'll bridge research and engineering to design, train, and ship production-grade models that directly serve frontier AI labs - and you'll help shape the technical culture and infrastructure from day one., * Implement and fine-tune LLMs, embeddings, and generative models for real-world applications.

 * Develop efficient training and inference systems leveraging distributed compute.
 * Partner with data and product teams to translate ideas into measurable ML impact.
 * Contribute to model monitoring, evaluation, and continual learning frameworks.
 * Establish best practices in model versioning, reproducibility, and scalability.


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