Logo for Clera Inc.

Founding Engineer - Machine Learning

Role overview

Qualifications

  • 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)

Responsibilities

  • 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

About the company

Clera Inc. logo

Clera Inc.

Art

Company details

IndustryArt

Your match analysis

See how your profile stacks up against this role.

We compared the job requirements to your profile to show where you're strong and where you fall short.

Job description

About the Role

A well-funded AI/ML platform startup (Series A) based in Mountain View, CA is looking for a Founding Engineer – Machine Learning to build and scale core ML systems from the ground up. You'll join a small, high-velocity team working closely with the founders to establish technical culture, reliable ML pipelines, and measurable product impact. This is a rare opportunity to shape the architecture and direction of ML infrastructure at an early stage.

What You'll Do

  • 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.

What We're Looking For

Required:

  • 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.

Nice to Have:

  • Experience with vector databases and retrieval-augmented generation (RAG) workflows.

  • Experience with continual learning, model monitoring, and evaluation frameworks.

Compensation & Benefits

  • Salary: $220,000 – $300,000 USD annually

  • Early-stage equity commensurate with a founding engineer role

  • Note: Visa sponsorship is not available for this position

Location

Onsite in Mountain View, CA. This is not a remote role.

Apply once. Then go straight to the hiring manager.

After you apply, unlock the direct contact details of the people who actually make the call. A quick follow-up makes you 5x more likely to land an interview.

MR

Marcus Rivera

Chief Revenue Officer

m.rivera@company.com
linkedin.com/in/marcusrivera
Unlocked after you apply
·

Machine Learning Engineer Related jobs

Other jobs at Clera Inc.

Premium

Reach out to the hiring manager directly.

Gain access to the contact details of the hiring managers who actually decide, and reach out to network with them directly. That, plus more when you upgrade:

  • Full match report with fit score and gaps
  • Career diagnostics on how recruiters read you
  • Curated company matches and warm intros
  • 48h early access to new roles

Cancel anytime.