Job Summary:
We are seeking a highly skilled Machine Learning (ML) Scientist with strong expertise in embeddings and deep learning. In this role, you will work on cutting-edge projects that involve developing and implementing machine learning models to solve real-world problems, particularly focusing on creating and leveraging embeddings for recommendation systems, NLP tasks, and other AI-driven solutions. You will collaborate with cross-functional teams, including data engineers, product managers, and software developers, to deploy scalable machine learning models.
Key Responsibilities:
● Design, develop, and optimize machine learning models with a focus on embedding-based techniques (e.g., word embeddings, sentence embeddings, product embeddings).
● Apply deep learning architectures (e.g., CNNs, RNNs, Transformers) to solve complex business problems in domains such as Natural Language Processing (NLP), computer vision, or recommendation systems.
● Experiment with state-of-the-art models such as BERT, GPT, Graph Neural Networks (GNNs), autoencoders, and other representation learning models.
● Develop and fine-tune embeddings for various applications (recommendation engines, personalization, similarity search).
● Perform data preprocessing, feature engineering, and exploratory data analysis to ensure high-quality input for models.
● Collaborate with cross-functional teams to integrate machine learning solutions into production environments.
● Use large-scale datasets to train, validate, and deploy models efficiently.
● Keep up to date with the latest research and trends in embedding techniques, representation learning, and deep learning.
● Document model development processes and share insights with the team.
● Optimize model performance, scalability, and robustness for real-world deployment.
Required Qualifications:
● Master’s or Ph.D. in Computer Science, Data Science, Statistics, Mathematics, or related field with a focus on machine learning or artificial intelligence.
● 7+ years of hands-on experience in designing and deploying machine learning models, with specific experience in embedding techniques and deep learning.
● Proficiency in Python and popular machine learning frameworks such as TensorFlow, PyTorch, Keras, or Hugging Face Transformers.
● Strong understanding of embedding techniques (e.g., Word2Vec, FastText, GloVe) and their applications in recommendation systems, NLP, or search algorithms.
● Experience with neural network architectures (e.g., CNNs, RNNs, LSTMs, Transformers) and deep learning methodologies.
● Experience in working with large-scale datasets and optimizing models for speed and performance.
● Solid understanding of natural language processing (NLP), computer vision, or recommendation systems.
● Familiarity with cloud platforms like AWS, GCP, or Azure for deploying machine learning models.
● Excellent problem-solving skills and ability to work independently as well as part of a team.
● Strong written and verbal communication skills to present findings to technical and non-technical stakeholders.
Preferred Qualifications:
● Experience with Graph Neural Networks (GNNs) or Graph-based recommendation systems.
● Familiarity with unsupervised learning techniques (autoencoders, self-supervised learning).
● Experience with distributed computing frameworks such as Spark or Dask.
● Understanding of MLOps practices and experience deploying models in production environments.
● Published papers or contributions in AI/ML conferences or journals.
Benefits:
● Competitive salary and performance bonuses
● Comprehensive health insurance (medical, dental, vision)
● Generous paid time off (PTO) and parental leave
● Learning and development opportunities (conferences, certifications, etc.)
● Flexible working hours and remote working options
● Collaborative and innovative work environment with opportunities for career growth
