SerenityForWork
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Deep Learning
Amazon Web Services (AWS)A stealth startup, born from research at Stanford's Human-Centred AI Lab, is building an AI Chief of Staff, designed to revolutionize how we work.
Most companies building AI agents are automating routine work. But we want to hold AI to a higher bar – an AI that “reasons” and helps humans deal with cognitive overload.
Come work at the intersection of cutting-edge NLP, generative AI and graph learning ML techniques. Help build technology that empowers, not exhausts!
Team: Stanford founder with background in data science, investing, and startups; Staff engineer at Amazon; and top-tier AI practitioners as Advisors (ex-Head of AI at Spotify, Senior Eng Director at Pinterest, AI 2030 Global Leader among others). Harvard and Stanford engineers contributed to early product development.
Role: Founding ML Engineer, full-time
Location: India. We can sponsor visas to relocate to the US based on fit.
Compensation: Around INR 25 lakh based on experience + equity
Required Qualifications
- Bachelor’s degree or master’s (preferred) in computer science, engineering, mathematics, or a related technical field from a top-tier institution
- 3+ years of experience building and deploying ML models in production, ideally with “0 to 1” ML work
Technical
- Expertise in NLP: Hugging Face Transformers, BERT, RoBERTa, and techniques like text classification and semantic clustering.
- Experience with LLMs (e.g., GPT-4, Gemini) for prompt engineering, fine-tuning for specific use cases, content generation, and cost-optimized ML infrastructure
- Deep Learning & Graph-Based ML: Proficiency in TensorFlow, PyTorch, XGBoost, GNNs, and dynamic graph databases like Neo4j for modelling relationships
- AI-powered chatbots, integrating LLMs for natural language interaction, feedback processing, and real-time user support
- Real-Time Data Processing & Integration: Skilled in using Apache Kafka and integrating ML models into scalable, reliable back-end systems
- Cloud Deployment: Experience deploying and managing ML services on AWS, GCP, or Azure with a focus on scalability and performance optimization
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