ML Engineer — Talent Matching AI (Hybrid, SF)

Clera; San Francisco (CA); On-site; USD 140,000 - 210,000; Full time

Job summary

Clera is seeking a mid-level Machine Learning Engineer to design, build, and iterate on ML models powering candidate-to-role matching and personalization. You will work with the founding team to translate product goals into scalable ML solutions and develop data pipelines to support training, evaluation, and deployment.

You will monitor model performance in production and drive continuous improvements, while contributing to research on AI techniques for talent matching and NLP, in a fast-moving

Qualifications

• 3–7 years hands-on experience in machine learning engineering or closely related role.
• Strong fundamentals in computer science, statistics or related field.
• Proven experience building and deploying ML models in production.
• Proficiency with ML frameworks (PyTorch or TensorFlow) and NLP familiarity.
• Comfort with full ML lifecycle from data prep to monitoring.

Responsibilities

• Design, build, and iterate on ML models powering candidate-to-role matching.
• Collaborate with founding team to translate product goals into scalable ML solutions.
• Develop and maintain data pipelines to support model training, evaluation, and deployment.
• Monitor production model performance and drive continuous improvements.
• Contribute to research and experimentation on AI techniques relevant to talent matching and NLP.

Skills

ML systems

Production ML

NLP techniques

Data pipelines

Ownership

Model evaluation

CS fundamentals

Education

Bachelor's in CS/Math/Stats

Tools

PyTorch

TensorFlow

Job description

Clera is seeking a mid-level Machine Learning Engineer to design, build, and iterate on ML models powering candidate-to-role matching and personalization. You will work with the founding team to translate product goals into scalable ML solutions and develop data pipelines to support training, evaluation, and deployment.

You will monitor model performance in production and drive continuous improvements, while contributing to research on AI techniques for talent matching and NLP, in a fast-moving