Senior ML Engineer I
USD 156K-210K (estimate) Senior-level Full Time
Tasks
- Build agentic multi agent orchestration
- Build vector embeddings for retrieval
- Create feedback loops for model improvement
- Curate and mask sensitive training data
- Deploy language models on GCP
- Design ML data pipelines
- Develop RAG systems
- Implement MLOps CI/CD
- Implement document information extraction
- Implement fine-tuning strategies
- Integrate structured tool calling
- Monitor model performance and latency
- Select and fine tune language models
Perks/Benefits
- 401 k company match
- Education assistance
- Employee resource groups
- Flexible time off
- Free LinkedIn Learning access
- Free mental health programs
- Paid parental leave
- Paid time off
- Pet insurance
Skills/Tech-stack
Agent systems | CI/CD | Cloud Functions | Cloud Run | Cloud platform | Continuous Delivery | Data Privacy | Document AI | Document Layout Analysis | Document layout | Entity recognition | Fine Tuning | Full Fine-tuning | GKE | Google Cloud | Google Cloud Platform | Google Kubernetes | Google Kubernetes Engine | Hugging Face | Hugging Face Transformers | Knowledge Distillation | Kubernetes Engine | Language Modeling | Language Processing | Latency monitoring | Layout analysis | LoRA | MLOps | Machine Learning | Model Monitoring | Model versioning | Multi-Agent | Multi-Agent Systems | Named Entity Recognition | Natural Language | Natural Language Processing | OCR | Optical character recognition | PGVector | PHI PII Masking | PHI/PII | PII masking | Pinecone | PyTorch | Python | QLoRA | Qdrant | RAG | Relationship Extraction | Retrieval-Augmented Generation | TensorFlow | Throughput Monitoring | Transformers | Vector Databases | Vector Search | Vector embeddings | Vertex AI | Weaviate
Education
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