_{Machine Learning Engineer}

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_{Our client is a rapidly growing company operating at the intersection of AI and labor-market intelligence, and they are looking for a Machine Learning Engineer to join their core engineering team. The company partners with top AI organizations to provide the human expertise required to train and advance next-generation models.}

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_{Their network of domain specialists contributes real-world knowledge and context that AI systems can’t learn from code alone,  enabling faster model improvement and higher-quality training at scale. This is a new category of work, and achieving it requires an ambitious, fast-moving technical team working alongside researchers, operators, and AI companies shaping the future of the industry.}

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_{The company is a profitable late-stage startup valued in the multi-billion-dollar range, with a strong in-person culture at their San Francisco headquarters.}

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_{What You’ll Do}

_{- Research, train, and productionize ML models across engagement prediction, scoring, search, recommendation, and fraud detection}

_{- Build backend infrastructure and scalable APIs that reliably serve ML models in production}

_{- Design and run experiments, analyze results, and iterate quickly to improve both modeling performance and product outcomes}

_{- Collaborate with Product and Operations to translate business problems into model-driven, production-ready systems}

_{- Own projects end-to-end,  data pipelines, model training, serving, monitoring, and backend integration}

_{- Operate as a true generalist, shifting between backend engineering, applied ML, experimentation, and product-focused problem solving}

_{- Contribute to architectural decisions and establish best practices for ML development and deployment}

_{What We’re Looking For}

_{- 3+ years of experience building ML-driven products or backend systems in a production environment}

_{- Strong backend engineering skills (e.g., Python, Django, FastAPI) with a solid foundation in machine learning, statistics, and experimentation}

_{- Demonstrated experience shipping ML systems end-to-end,  from data to deployment}

_{- High ownership mentality and comfort navigating fast-changing, ambiguous environments}

_{- Generalist mindset,  comfortable working across modeling, data pipelines, backend systems, and product workflows}

_{- Clear and effective communicator who can translate complex technical concepts into practical business solutions}

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