- Company Name
- Doppel
- Job Title
- Machine Learning Engineer, Detection
- Job Description
-
Job Title: Machine Learning Engineer, Detection
Role Summary
Design, train, and deploy machine‑learning models that detect malicious or infringing content in real‑time and batch pipelines across massive web and social‑media datasets. Work closely with detection, infrastructure, and client teams to transform security threats into scalable production systems.
Expectations
- Build and maintain end‑to‑end ML pipelines handling billions of data points daily.
- Ensure production models meet performance, latency, and reliability targets.
- Continuously evaluate and improve models in response to evolving adversarial tactics.
Key Responsibilities
- Develop supervised, semi‑supervised, and anomaly‑detection models for binary and multi‑class classification of threat indicators.
- Create and refine text embeddings, similarity search, and content‑matching algorithms for large‑scale search.
- Implement feature engineering, feature store management, and feature drift monitoring.
- Deploy models using containerized services, scalable compute (e.g., Kubernetes, GPU clusters) and serve via low‑latency APIs.
- Collaborate with data engineering to ingest, clean, and partition terabyte‑scale datasets.
- Partner with infrastructure to optimize compute, storage, and networking for real‑time inference.
- Conduct model validation, A/B testing, and post‑deployment monitoring.
- Translate stakeholder requirements into technical specifications and documented results.
Required Skills
- Strong background in statistical ML (classification, clustering, anomaly detection).
- Expertise in NLP: tokenization, embeddings (BERT, Sentence‑Transformers), BOW, and sequence models.
- Proficiency with Python, PyTorch/TensorFlow, and ML libraries (scikit‑learn, HuggingFace).
- Experience with distributed data processing (Apache Spark, Dask) and large‑scale data pipelines.
- Knowledge of model serving frameworks (TensorFlow Serving, TorchServe, OnnxRuntime).
- Familiarity with cloud ML services (AWS Sagemaker, GCP Vertex AI, Azure ML) or self‑hosted infra.
- Strong debugging, version control (Git), and CI/CD fundamentals.
- Excellent communication skills for cross‑functional collaboration.
Required Education & Certifications
- Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, Applied Mathematics, or related quantitative field.
- Formal certifications in ML/AI (e.g., TensorFlow Developer, AWS Certified Machine Learning) are optional but a plus.