Founding Engineer – Machine Learning

Clera

Founding Engineer building and scaling core ML systems for a well-funded AI/ML platform startup. Collaborating with founders to shape the architecture and direction of ML infrastructure.

Posted 7/10/2026full-timeMountain View • California • 🇺🇸 United StatesMid-LevelSenior💰 $220,000 - $300,000 per yearWebsite

Tech Stack

Tools & technologies

AWSAzureCloudGoogle Cloud PlatformPythonPyTorchTensorflow

About the role

Key responsibilities & impact

Build and optimize end-to-end ML pipelines from data ingestion through deployment.
Implement and fine-tune LLMs, embeddings, and generative models for real-world use cases.
Develop efficient training and inference systems leveraging distributed compute.
Partner with data and product teams to translate ideas into measurable ML outcomes.
Contribute to model monitoring, evaluation, and continual learning frameworks.
Establish best practices for model versioning, reproducibility, and scalability.
Move quickly between experimentation and production deployments, balancing research and engineering rigor.

Requirements

What you’ll need

3–10 years of experience as an ML Engineer, Applied Scientist, or Research Engineer.
Strong ML fundamentals: data preprocessing, feature engineering, model training, and optimization.
Proficiency in Python and at least one deep learning framework: PyTorch, TensorFlow, or JAX.
Experience with distributed training/inference and cloud ML infrastructure (AWS, GCP, or Azure).
Hands-on experience building end-to-end ML pipelines from data ingestion to production deployment.
Familiarity with MLOps tooling (e.g., Weights & Biases, MLflow) and model monitoring approaches.
Comfortable working with large datasets and high-throughput systems.
Bias for action, ability to work autonomously, and eagerness to build systems from scratch.
Willingness to work onsite in Mountain View, CA.

Benefits

Comp & perks

Early-stage equity commensurate with a founding engineer role

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

Data PreprocessingFeature EngineeringModel TrainingModel OptimizationModel MonitoringModel VersioningReproducibilityScalabilityCloud ML InfrastructureHigh-Throughput Systems

Soft Skills

Bias for ActionAutonomous WorkEagerness to Build Systems