Senior Machine Learning Engineer

Level AI was founded in 2019 and is a Series C startup headquartered in Mountain View, California. Level AI revolutionizes customer engagement by transforming contact centers into strategic assets. Our AI-native platform leverages advanced technologies such as Large Language Models to extract deep insights from customer interactions. By providing actionable intelligence, Level AI empowers organizations to enhance customer experience and drive growth. Consistently updated with the latest AI innovations, Level AI stands as the most adaptive and forward-thinking solution in the industry.

We'll love to explore more about you if you have

• Qualification: B.E/B.Tech/M.E/M.Tech/PhD from tier 1 engineering institutes with relevant work experience with a top technology company in computer science or mathematics-related fields with 3-5 years of experience in machine learning and NLP.
• Knowledge and practical experience in solving NLP problems in areas such as text classification, entity tagging, information retrieval, question-answering, natural language generation, clustering, etc.
• 3+ years of experience working with LLMs in large-scale environments.
• Expert knowledge of machine learning concepts and methods, especially those related to NLP, Generative AI, and working with LLMs.
• Knowledge and hands-on experience with Transformer-based Language Models like BERT, DeBERTa, Flan-T5, Mistral, Llama, etc.
• Deep familiarity with internals of at least a few Machine Learning algorithms and concepts.
• Experience with Deep Learning frameworks like Pytorch and common machine learning libraries like scikit-learn, numpy, pandas, NLTK, etc.
• Experience with ML model deployments using REST API, Docker, Kubernetes, etc.
• Knowledge of cloud platforms (AWS/Azure/GCP) and their machine learning services is desirable.
• Knowledge of basic data structures and algorithms.
• Knowledge of real-time streaming tools/architectures like Kafka, Pub/Sub is a plus.

Your role at Level AI includes but is not limited to

• Big picture: Understand customers’ needs, innovate and use cutting edge Deep  Learning techniques to build data-driven solutions.
• Work on NLP problems across areas such as text classification, entity extraction, summarization, generative AI, and others.
• Collaborate with cross-functional teams to integrate/upgrade AI solutions into the company’s products and services.
• Optimize existing deep learning models for performance, scalability, and efficiency.
• Build, deploy, and own scalable production NLP pipelines.
• Build post-deployment monitoring and continual learning capabilities. Propose suitable evaluation metrics and establish benchmarks.
• Keep abreast with SOTA techniques in your area and exchange knowledge with colleagues.
• Desire to learn, implement and work with latest emerging model architectures, training and inference techniques, data curation pipelines, etc.