Scientific AI & ML Engineer designing and deploying innovative AI - driven solutions. Collaborating with teams to solve complex scientific challenges through advanced machine learning techniques.
About the role
- Machine Learning Engineer role at Level AI, innovating in speech AI and NLP space. Collaborate with experienced technologists to solve complex problems.
Responsibilities
- Big picture: Understand customers’ needs and innovate and use cutting-edge Machine Learning techniques to build data-driven solutions.
- Work on NLP problems across areas such as text classification, entity extraction, summarisation, generative NLP, and others.
- Collaborate with cross-functional teams to integrate/upgrade AI solutions into company’s products and services Optimise existing machine 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 of SOTA techniques in your area and exchange knowledge with colleagues.
- Desire to learn, implement and apply latest emerging model architectures (like LLMs), inference optimizations, distributed training, using open-source models, etc.
Requirements
- Bachelors in Computer Science or mathematics-related fields with 2+ years of experience in Machine Learning and NLP.
- Proficient in Python, 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.
- Knowledge and experience with data engineering, basic machine learning concepts, data mining, feature extraction, pattern recognition, etc.
- Knowledge and hands-on experience with Transformer-based Language Models like BERT, DeBERTa, Flan-T5, GPT, Llama, Gemma, DeepSeek, etc.
- Deep familiarity with Model Training concepts, model inference optimisations, GPUs, etc.
- Experience with Deep Learning frameworks like Pytorch and common machine learning libraries like scikit-learn, numpy, pandas, NLTK, transformers, 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 Multimodal models is a plus
- Knowledge of real-time streaming tools/architectures like Kafka, Pub/Sub is a plus.
Job title
Job type
Experience level
Salary
Not specified
Degree requirement
Tech skills
Location requirements
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