AI Engineer
Tasks
- Architect agentic workflows for IP research
- Build evals framework for model performance
- Collaborate with domain experts to encode expert judgment
- Design and iterate LLM pipelines for patent analysis
- Develop and deploy AI ML algorithms for IP applications
- Develop data classification and post training techniques
- Implement real-time and batch data pipelines
- Implement retrieval systems for patent corpora
- Improve ML methodologies through experiments and fine tuning
- Lead R and D of new technologies and data sources
- Manage production inference tradeoffs for cost latency quality
- Monitor data pipeline performance and model quality in production
- Perform unit testing profiling and parameter tuning
Perks/Benefits
- 401k enrollment
- Gym membership stipend
- Health coverage
- Hybrid work environment
- Paid Holidays
- Paid parental leave
- Professional development budget
- Remote work options
- Unlimited PTO
Skills/Tech-stack
API Design | AWS | Agentic Workflows | Asynchronous processing | BM25 | Batching | Context window | Context window management | Data Classification | Data Warehousing | Deep learning | Evaluation | Fine Tuning | GCP | Golden datasets | Inference Optimization | LLM Pipelines | Language Models | Large Language Models | Latency optimization | Machine Learning | Model migration | Multi-turn reasoning | Observability | Pandas | Prompt Caching | Prompt engineering | Python | Re-ranking | Regression testing | Retrieval-Augmented Generation | SQL | Scikit-learn | Spark | Structured Output | Tool use | Vector Search | Window management
Education
N/A
Roles
AI | AI Engineer | Engineer | Learning Engineer | Machine Learning Engineer
Regions
Countries
States
Cities
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