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Salary
$170k – $330k per year (Estimated)
Location
In office (San Francisco)
Seniority
Middle · 3+ years exp
Employment
Full-Time
Overview
Company
Impact
Profile match
Clera is a San Francisco company that runs an AI recruiting platform positioned as a talent agent rather than a job board, matching engineers and other technical candidates to roles at venture-backed startups. Its system reads a candidate's background and preferences, then represents them to hiring teams at companies funded by firms such as Andreessen Horowitz, Y Combinator, Index Ventures and General Catalyst, compressing the introduction step that traditional headhunting handles manually. The model is aimed at the segment where recruiter fees are highest and candidate supply is thinnest, and the platform handles screening, scheduling and pipeline tracking for both sides of the match.

About the Role

You will build and deploy ML systems that power a core product in a fast moving startup environment. You will work across the full ML lifecycle from problem definition through production monitoring, collaborating with product and engineering teams to ship models that drive business impact.

What You'll Do

  • Design, train, and evaluate machine learning models for production use cases.

  • Implement end to end ML pipelines from data preprocessing to model serving and monitoring.

  • Translate business requirements into ML solutions in collaboration with cross functional teams.

  • Debug and optimize model performance in production and iterate based on real world feedback.

  • Write clean, maintainable code and contribute to ML infrastructure and tooling.

  • Participate in code reviews and share knowledge with the broader team.

What We're Looking For

  • 3+ years of professional experience in machine learning or software engineering with hands on applied ML work in production systems.

  • Strong fundamentals in model selection, feature engineering, evaluation and validation.

  • Proficiency in Python for ML development and experience with ML frameworks such as TensorFlow, PyTorch, or scikit learn.

  • Experience building, deploying, and maintaining ML systems at scale including data pipelines, monitoring, A/B testing and MLOps tools (for example cloud ML platforms, Kubernetes, Docker).

  • Experience implementing end to end ML pipelines and deploying ML systems in production with experimentation frameworks.

  • Ability to work in a fast moving product environment with rapid iteration and ambiguity.

Compensation & Benefits

Compensation details are provided by the employer upon request.

Location

On site in San Francisco, California, United States

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