דרושים Knowledge%20manager ב-United States

Key job responsibilities
* Use deep learning, ML and NLP techniques to create scalable solutions for creation and development of language model centric solutions for building personalized assistant systems based on a rich set of structured and unstructured contextual signals
* Innovate new methods for contextual knowledge extraction and information retrieval, using language models in combination with other learning techniques, that allows effective grounding in context providers when considering memory, compute, latency and quality
* Research in advanced customer understanding and behavior modeling techniques
* Collaborate with cross-functional teams of engineers, product managers, and scientists to identify and solve complex problems in personal knowledge aggregation, processing, modeling, and verification
* Design and execute experiments to evaluate the performance of state-of-the-art algorithms and models, and iterate quickly to improve results
* Think Big about the arc of development of conversational assistant system personalization over a multi-year horizon, and identify new opportunities to apply these technologies to solve real-world problems
* Communicate results and insights to both technical and non-technical audiences, including through presentations and written reports
* Mentor and guide junior scientists and engineers, and contribute to the overall growth and development of the teamA day in the life
About the team
Diverse Experiences
AWS values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying.Mentorship & Career Growth
We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional.Work/Life Balance

- PhD, or Master's degree and 6+ years of applied research experience
- 3+ years of building machine learning models for business application experience
- Experience programming in Java, C++, Python or related language
- Experience with neural deep learning methods and machine learning

- Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.
- Experience with large scale distributed systems such as Hadoop, Spark etc.

We're looking for candidates who combine strong software engineering fundamentals with practical system development experience. You'll need to demonstrate expertise in building scalable, fault-tolerant distributed systems, with a track record of shipping production services that handle large-scale workloads. We prioritize candidates who understand professional software engineering practices across the full development lifecycle - from system design and coding standards to testing, deployment, and operational excellence.
Key job responsibilities
- Develop efficient data processing pipelines to handle large-scale training and inference data
- Support experimentation and A/B testing infrastructure to evaluate model improvements
- Participate in code reviews, technical design discussions, and sprint planning to ensure high quality software delivery
- Develop and optimize LLM-assisted tools that revolutionize knowledge graph creation, from automated ontology generation to real-time fact extraction and verification- Release and maintain ML model infrastructure to enable high-throughput, low-latency inference in production environments

- 3+ years of non-internship professional software development experience
- Bachelor's degree in computer science or equivalent
- 2+ years of non-internship design or architecture (design patterns, reliability and scaling) of new and existing systems experience
- Knowledge of professional software engineering & best practices for full software development life cycle, including coding standards, software architectures, code reviews, source control management, continuous deployments, testing, and operational excellence
- Experience with data processing and ETL pipelines at scale (Spark, GlueJob, Kafka)