Das ist der Job
Optimization & Performance: Profile and optimize model latency and throughput for production environments.
Darum lohnt es sich
Responsibilities Model Implementation: Design, train, and fine‑tune state‑of‑the‑art ML models (Deep Learning, Transformers, Gradient Boosting, etc.) specifically optimized for our internal datasets.
End‑to‑End Pipeline Development: Build and maintain robust data pipelines and training workflows to ensure reproducible and scalable model development. Data Centricity: Perform deep exploratory data analysis (EDA) to identify biases, signal‑to‑noise ratios, and feature engineering opportunities within our unique data silos.
Collaboration: Work closely with Data Engineers to streamline data ingestion and Backend Engineers to integrate model APIs into our user‑facing products. Requirements 5+ years of professional experience in Machine Learning or Software Engineering, with at least 3 years focused on deploying models to production.
Expert‑level Python (and ideally C++ or Go for performance‑critical components). Deep fluency in PyTorch, TensorFlow, or JAX. Experience with SQL, Spark, and vector databases (e.g., Pinecone, Milvus). Familiarity with Weights & Biases, MLflow, Kubeflow, or similar orchestration tools.
Strong understanding of linear algebra, calculus, and statistics as applied to ML optimization. MS or PhD in Computer Science, Mathematics, or a related field (or equivalent 'battle‑tested' industry experience). #J-18808-Ljbffr
