Machine Learning Engineer
Job in
San Francisco, San Francisco County, California, 94199, USA
Listed on 2026-06-15
Listing for:
Scale.jobs
Full Time
position Listed on 2026-06-15
Job specializations:
-
Software Development
Machine Learning/ ML Engineer, AI Engineer (Applied/Software)
Job Description & How to Apply Below
About The Role
The role is responsible for designing, building, and scaling the core machine learning infrastructure and models that power real-time personalization and prediction engines. This position sits at the intersection of data science and systems engineering, focused on translating complex algorithms into reliable, low-latency production services. The team operates on the belief that a model is only as good as its deployment.
The engineer in this role will collaborate with cross‑functional partners to ensure models perform reliably under heavy production loads, directly influencing product capabilities and the overall user experience.
- Deploy, monitor, and maintain machine learning models in production environments, ensuring high availability and low‑latency inference
- Build and optimize robust data pipelines for feature engineering and real‑time model scoring using PySpark, Kafka, and SQL
- Implement automated retraining and continuous integration pipelines (CI/CD) specifically designed for ML assets using MLflow or Kubeflow
- Establish model monitoring, logging, and alerting systems to detect data drift, concept drift, and performance degradation
- Collaborate with backend engineering teams to design and integrate robust APIs that serve model predictions to client applications
- 3-6 years of experience as a Machine Learning Engineer or Software Engineer with a strong focus on production ML systems
- Proficiency in Python and deep familiarity with ML libraries such as PyTorch, scikit‑learn, and XGBoost
- Hands‑on experience with containerization technologies (Docker, Kubernetes) and cloud infrastructure (AWS or GCP)
- Solid understanding of software engineering best practices, including version control, unit testing, and design patterns
- BS or MS in Computer Science, Data Science, or a related quantitative technical field
- Bonus:
Experience with large language model (LLM) orchestration frameworks like Lang Chain, vector databases (Pinecone, Qdrant), or advanced fine‑tuning techniques
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