Senior Applied ML Engineer

Що робити

Partner with the Principal Data Scientist and Principal Data Engineers, senior technical leaders and data scientists to implement and evolve model-evaluation and productionisation standards; independently own assigned enrichment workflows from evaluation through production delivery.
Evaluate candidate models on representative content, using appropriate quality measures and threshold analysis to support selection and release decisions.
Develop tested, maintainable Python inference components that conform to shared engineering frameworks, metadata contracts and deployment standards.
Support ad-hoc metadata backfills by validating model outputs, estimating inference cost and throughput, and resolving model-specific processing issues.
Work with data engineers to move enrichment components from representative evaluation to reliable corpus-scale execution.

6+ years of experience in applied ML, ML engineering, data science or software engineering, including hands-on experience delivering production ML inference systems.
Experience evaluating pretrained or third-party ML models, defining appropriate quality metrics and operating thresholds, while balancing model quality against inference cost, throughput and operational complexity.
Strong Python software engineering skills, including testing, packaging and dependency management.
Hands-on proficiency with ML libraries and frameworks such as PyTorch, TensorFlow, scikit-learn or Hugging Face.
Experience developing, deploying and maintaining high-performing, resilient and scalable ML inference systems across CPU and GPU environments in both AWS and GCP

Model tracking and lifecycle tooling such as MLflow.
Managed ML and batch-inference platforms such as SageMaker, Vertex AI or Databricks.
Experience evaluating models for image, video or audio content.
Experience with Snowflake, DBT or Clickhouse

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