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Staff Applied Machine Learning Engineer- Intelligent Data, Signals & Systems
Rol remoto de 10409 Engineering - AIDA con fit claro de ubicación del candidato.
PublicadoAgregado recientemente
Países elegibles27 países aceptados
Señal de seniorityLead
Modelo de trabajoRemoto
Ubicaciones aceptadas para candidatos
Resumen del rol
Staff Applied Machine Learning Engineer- Intelligent Data, Signals & Systems
Requisitos y responsabilidades
Contenido del rol extraído en secciones para revisar más rápido.
You Will
- Build and operate production ML systems that turn customer and product context into trusted signals, rankings, recommendations, and decision capabilities.
- Design production data and signal contracts that define intended use, freshness, provenance, confidence, eligibility, and calibration for downstream consumers.
- Own ranking, retrieval, recommendation, search, propensity, and next-best-action systems end to end, from feature and candidate generation through serving, experimentation, monitoring, and feedback loops.
- Evaluate customer and business impact beyond short-term conversion, including trust, fairness, access, risk, compliance, long-term engagement, and segment-level performance.
- Partner across product, growth, data, platform, modeling, risk, and compliance to translate ambiguous goals into measurable ML system designs.
- Use AI and agents to accelerate development, analysis, testing, documentation, and operations while exposing reusable capabilities to product services, internal tools, and AI-assisted workflows.
You Have
- 12+ years building and operating production software and ML systems for business-critical products.
- Deep expertise in intelligent systems such as ranking/retrieval, recommendations, search, personalization, growth and lifecycle ML, customer intelligence, propensity/churn/LTV, next-best-action, or model-derived risk signals.
- Strong production ML judgment across feature pipelines, model serving, experimentation, monitoring, feedback loops, online/offline consistency, and reliable signal interfaces.
- Ability to evaluate impact beyond short-term conversion, including trust, fairness, access, risk, compliance, and long-term engagement.
- Experience using AI-assisted engineering tools with appropriate verification, testing, and review for customer-impacting systems.
Nice to haves
- Experience with semantic retrieval, embeddings, two-tower models, graph features, LLM-powered retrieval or decision systems, entity resolution, or real-time personalization.
- Experience with experimentation, online evaluation, interleaving, counterfactual evaluation, multi-objective optimization, or long-term holdouts.
- Experience building reusable feature/signal platforms, decision services, customer intelligence layers, model-derived data products, or agent-assisted operations.
Technologies We Use and Teach
- Python, Java, Kotlin, SQL.
- TensorFlow, PyTorch, XGBoost/LightGBM, ranking/retrieval systems, embeddings, semantic search, recommendation frameworks.
- Event streams, batch pipelines, feature stores, model-serving infrastructure, workflow orchestration, experimentation systems, and data warehouses/lakehouses.
- Cloud infrastructure, Kubernetes, observability tooling, coding agents, evaluation harnesses, and agent-assisted operations tooling.
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