Machine Learning Engineer

Machine Learning Engineer

• Title of Role: Machine Learning Engineer


• Location: San Francisco, CA, onsite


• Company Stage of Funding: Series C — Software Development


• Office Type: Onsite


• Salary: [To be confirmed with final candidates]

Company Description

We're representing a dynamic AI-powered hiring platform that is redefining how talent is matched with opportunity. By leveraging machine learning at the core of their product, our client connects candidates with roles tailored to their skills, making recruitment faster, smarter, and more equitable. They operate at the intersection of AI and human potential, building the infrastructure to scale their mission globally.

What You Will Do

• Take ownership of the full machine learning lifecycle, from research and experimentation to production deployment.


• Research, train, and productionize ML models focused on engagement prediction, scoring, and search functionalities.


• Build robust backend infrastructure and APIs to reliably serve ML models at scale.


• Run experiments, analyze results, and iterate quickly to enhance model quality and overall product performance.


• Collaborate cross-functionally with Operations and Product teams to translate business needs into model-driven solutions.


• Adapt to a broad range of responsibilities, including backend engineering and applied ML, as priorities evolve.

Ideal Candidate Background

• Demonstrated experience shipping production ML systems that real users depend on.


• Strong backend engineering skills, particularly with Python and Django or similar frameworks.


• Proven ability to thrive in fast-paced, ambiguous environments with a high degree of ownership and initiative.


• Solid foundation in machine learning, statistics, and classification, with experience in building backend infrastructure and APIs.

Preferred

• Experience with large language models (LLMs) and their application in production environments.


• Background in search or recommendation systems, learning-to-rank (LTR), or classification models.


• Prior internships or 1–3 years of experience at top-tier tech companies, fast-scaling startups, or quantitative finance environments.

Compensation and Benefits

Visa sponsorship is available for this role. Our client is committed to offering a competitive compensation package that reflects the experience and impact of the candidate.