Description
We're seeking a Staff Machine Learning Engineer to join our Content Understanding team. As a key member of this team, you will lead modeling strategy for content understanding, including architecture selection, training approach, and evaluation methodology. You will design and ship production models that generate content signals such as embeddings and classifications used across multiple product surfaces. The ideal candidate will have significant industry experience building software and ML pipelines/systems, including technical leadership. They will have strong proficiency in Python and at least one ML stack such as PyTorch / TensorFlow, plus solid software engineering fundamentals. The role requires proven experience training and deploying ML models to production, including model versioning, rollouts, monitoring, and retraining strategies. The successful candidate will have deep hands-on experience in content understanding domains, such as computer vision, NLP, and multimodal/embedding models. They will also have experience working with large-scale datasets and distributed compute. The ideal candidate will be able to influence across teams and drive ambiguous problem areas to measurable outcomes. They will have strong applied skills in evaluation and experimentation, including defining metrics, offline/online alignment, A/B testing, debugging regressions, and model quality analysis.
The role is ideal for a senior modeler who also enjoys developing, productionizing models and leading technical direction across teams. The successful candidate will be able to provide technical leadership through design reviews, mentoring, and raising the quality bar for modeling and ML engineering practices.
In addition to the above responsibilities, the successful candidate will be expected to:
- Collaborate with infra/platform teams to ensure scalable, reliable training/serving (latency, cost, observability, rollout safety).
- Partner with signal-consuming teams (ranking, retrieval, integrity, ads) to define signal contracts, adoption patterns, and success metrics.
- Own the full ML lifecycle: data/labeling strategy (human labels + weak supervision), training pipelines, offline evaluation, online experimentation, deployment, and monitoring/retraining.
- Provide technical leadership through design reviews, mentoring, and raising the quality bar for modeling and ML engineering practices.
Nice to have: experience with Cursor, Copilot, Codex, or similar AI coding assistants for development, debugging, testing, and refactoring; familiarity with LLM-powered productivity tools for documentation search, experiment analysis, SQL/data exploration, and engineering workflow acceleration.