Staff Applied Scientist

Accountabilities:

Knowledge Graph Development: Design, build, and maintain production-grade knowledge graphs that transform unstructured content into structured, reliable, and accessible knowledge. Lead ontology design, information extraction, and entity resolution to improve data quality and usability.
AI Agents and Intelligent Retrieval: Develop and deploy LLM-powered agents and agentic retrieval systems capable of finding, validating, and using relevant information. Implement planning, tool integration, and orchestration mechanisms to deliver dependable, context-aware results.
Model Evaluation and Continuous Improvement: Establish robust evaluation frameworks and quality metrics to measure model performance and determine production readiness. Incorporate real-world user feedback to improve knowledge accuracy, retrieval relevance, and overall system effectiveness.
Applied Machine Learning and NLP: Apply advanced machine learning, natural language processing, retrieval-augmented generation (RAG), and LLM fine-tuning techniques to solve complex product challenges. Combine modern language models with classical machine learning approaches to improve reliability and performance.
Technical Strategy and Architecture: Set technical direction for applied science initiatives, evaluate emerging technologies, and make informed build-versus-buy decisions. Establish scalable approaches to experimentation, model deployment, and production engineering.
Cross-Functional Product Collaboration: Partner with Product, Engineering, and AI teams to translate research findings into practical product capabilities. Balance technical feasibility, system performance, and customer value when prioritizing solutions.
Technical Leadership and Mentorship: Mentor engineers, data scientists, and senior technical contributors. Establish best practices for experimentation, model evaluation, software quality, and production deployment while fostering a culture of scientific rigor and continuous learning.
Responsible and Reliable AI: Improve agent reliability through systematic testing, guardrails, human review, and ongoing performance monitoring. Ensure AI systems meet established quality standards before their release to customers.
Requirements:
Applied Machine Learning Experience: At least 8 years of experience applying machine learning techniques in production environments, with a demonstrated ability to develop and deploy solutions that deliver measurable business or customer outcomes.
NLP, LLMs, and RAG Expertise: Strong knowledge of natural language processing, large language models, and retrieval-augmented generation. Hands-on experience fine-tuning LLMs and applying classical machine learning techniques is required.
Knowledge Graph Experience: At least 3 years of experience building and deploying production knowledge graphs, including ontology design, information extraction, and entity resolution.
Graph Technologies: Practical experience with graph databases such as Neo4j and graph query languages such as Cypher. Familiarity with knowledge representation and graph-based information retrieval is highly valuable.
AI Agent Development: At least 3 years of experience building production-grade LLM-based agents, including planning, tool use, and orchestration. Ability to improve agent reliability through evaluation frameworks, guardrails, and human-in-the-loop review is essential.
Model Evaluation and Experimentation: Proven ability to design meaningful evaluation methodologies, define performance metrics, and use empirical results to guide model development and production release decisions.
Software Engineering and Deployment: Strong Python programming and software engineering skills, with experience developing, deploying, and maintaining machine learning services in production environments.
Technical Leadership: Demonstrated ability to establish technical direction, mentor senior engineers and data scientists, and influence architecture and implementation decisions across teams.
Communication and Collaboration: Ability to explain complex technical concepts and trade-offs in terms of customer needs, product outcomes, and business impact. Comfortable collaborating with distributed teams across multiple regions and time zones.
Additional Qualifications: Experience with graph neural networks or knowledge graph embeddings is an advantage. Research publications or open-source contributions in knowledge graphs, information extraction, or AI agents are also desirable.
Working Hours: Willingness to work from 11:00 a.m. to 7:30 p.m. India Standard Time (IST) to support collaboration with international teams.
Mindset and Values: A strong sense of ownership, intellectual curiosity, commitment to continuous learning, and enthusiasm for experimentation and responsible AI development. Candidates are encouraged to apply even if they do not meet every listed qualification.
Benefits:
Remote Work Flexibility: Work remotely from anywhere in India, with opportunities to collaborate with international colleagues in a remote-first environment.
Work-Life and Collaboration Structure: A combination of shared collaboration hours and dedicated focus time, supported by asynchronous communication practices across global teams.
Professional Development: Access to a dedicated professional development allowance to support continued learning, skill enhancement, and technical mastery.
Manager Support and Career Growth: Regular one-on-one meetings, ongoing feedback, and structured performance and development conversations.
Remote Work Equipment: A company-provided MacBook to support your day-to-day work.
Home Office Support: Monthly phone and internet subsidies, along with a work-from-home budget to help establish a productive remote workspace.
AI Productivity Tools: Access to Claude to explore AI-assisted workflows, automate routine tasks, and develop tools that improve productivity.
Collaborative Culture: Opportunities to participate in company-wide town halls, Ask-Me-Anything sessions, celebrations, and virtual team-building activities.
Employee Resource Groups: Access to four employee resource groups that support community building, connection, and learning throughout the year.
Meaningful Professional Impact: The opportunity to influence AI product strategy, advance applied science practices, and contribute to the development of intelligent software solutions.
Inclusive Workplace: A commitment to equitable hiring, diversity, accessibility, and an environment where people from different backgrounds are encouraged to contribute and grow.
Compensation and Healthcare: Salary, healthcare coverage, paid leave, and other country-specific benefits were not detailed in the provided job description and should be confirmed during the recruitment process.
Recruitment Accommodations: Support is available for candidates who require reasonable accommodations during the virtual interview process.