- Company Name
- Nextdoor
- Job Title
- Machine Learning Engineer - Ads
- Job Description
-
**Job title:** Machine Learning Engineer – Ads
**Role Summary:**
Design, develop, and deploy scalable machine learning models that improve advertising relevance and performance across a large, data‑intensive platform. Work closely with product, data science, and engineering teams to build real‑time decision systems, run live experiments, and iterate on model quality to impact key business metrics.
**Expectations (Core Objectives):**
- Deliver low‑latency predictive models that support real‑time ad decisioning.
- Drive measurable improvements in ad relevance, click‑through, and revenue.
- Contribute foundational ML engineering practices that scale across the platform.
**Key Responsibilities:**
1. **Data Engineering** – Collect, clean, and transform large datasets for feature extraction and model training.
2. **Model Development** – Build, tune, and evaluate supervised/unsupervised algorithms for ad relevance and targeting.
3. **Production Deployment** – Deploy models to production environments, ensuring reliability, monitoring, and continuous integration.
4. **Experimentation & Analysis** – Design and run A/B or multi‑armed bandit experiments; analyze statistical significance and business impact.
5. **Collaboration** – Partner daily with product managers, data scientists, and other ML engineers to align on objectives, share insights, and iterate on features.
6. **Operational Excellence** – Establish best practices for versioning, testing, and documenting ML pipelines and components.
**Required Skills:**
- Strong programming expertise (Python, C++, or Java) and experience with ML libraries (scikit‑learn, TensorFlow, PyTorch, XGBoost).
- Proficiency in SQL and big‑data processing frameworks (Spark, Flink, Hive).
- Experience building and scaling recommendation or advertising models (ranking, CTR, conversion prediction).
- Comfortable with MLOps tools (MLflow, Kubeflow, Airflow) and cloud platforms (AWS, GCP, Azure).
- Ability to interpret model performance, debug issues, and iterate rapidly in a dynamic environment.
**Required Education & Certifications:**
- Bachelor’s degree in Computer Science, Applied Mathematics, Statistics, Computational Biology, or a related field.
- 2+ years of industry or academic experience applying machine learning at scale.
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San francisco, United states
Hybrid
Junior
19-10-2025