gem
Senior Machine Learning Engineer II
Fetch
Remote, USA
Full-time
Senior Level
Remote
USD 211353-248650 yearly
Discovered 1 weeks ago
Free
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- Design, build, and improve machine learning models that power ad ranking, relevance, and optimization across the Fetch platform.
- Implement and iterate on active learning strategies, including data sampling, error-driven retraining, and human-in-the-loop workflows to improve ranking quality.
- Leverage LLMs to reduce model development and annotation effort, including synthetic data generation, assisted labeling, weak supervision, and error analysis for ranking and relevance tasks.
- Own ML experimentation, offline and online evaluation, and production inference for assigned ad ranking components.
- Partner closely with product, data, and platform teams to translate advertiser and user experience gaps into measurable ML improvements.
- Maintain high standards for model performance, reliability, latency, and data quality in production ranking systems.
- Use AI-assisted tools to accelerate development, experimentation, debugging, and analysis while maintaining strong engineering judgment. Designing features and validating ideas with ChatGPT & Claude sandboxes. Leveraging AI for code generation and technical prototyping. Using AI assistants for systems architecture diagramming and design validation.
- Designing features and validating ideas with ChatGPT & Claude sandboxes.
- Leveraging AI for code generation and technical prototyping.
- Using AI assistants for systems architecture diagramming and design validation.
- 6+ years of software engineering experience with a strong track record of building and maintaining production ML or data-driven systems.
- Strong proficiency in Python for machine learning and data processing, with working knowledge of Go, and hands-on experience deploying low-latency models into production ranking or decisioning systems.
- Experience with AWS and distributed systems, including building or operating scalable training pipelines and online inference services.
- Practical experience applying LLMs to reduce model development and data labeling effort, including assisted labeling, synthetic data generation, weak supervision, or model error analysis.
- Strong engineering judgment and systems mindset, with an emphasis on reliability, performance, and long-term maintainability of ranking or optimization systems.
- Experience using AI-assisted development tools (e.g., GitHub Copilot, ChatGPT, or similar) to accelerate iteration while maintaining high code quality.
- Ability to critically evaluate AI-generated outputs, debug complex issues, and validate correctness in production ML workflows.
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