Machine Learning Engineer II

Glidewell Dental • Irvine, CA

95d • $117,000 - $140,000

About The Position

The position involves designing, developing, and deploying machine learning models for real-world applications. The candidate will build scalable pipelines for data ingestion, pre-processing, training, and inference. They will own the end-to-end development of machine learning algorithms, which includes data analysis, feature engineering, model development, training, validation, and performance evaluation. The role also requires designing, implementing, and optimizing retrieval-augmented generation (RAG) pipelines that combine large language models (LLMs) with vector search/retrieval systems. Additionally, the candidate will build data ingestion and embedding pipelines for efficient indexing and retrieval, fine-tune and adapt LLMs for domain-specific tasks, and engage in both engineering and research to explore the latest ML algorithms and solution architectures. The candidate will work with stakeholders to translate business requirements into robust technical solutions and identify new opportunities to apply ML technology to improve business workflows and processes.

Requirements

• Master's degree in Machine Learning, Deep Learning, or a computer science-related field; PhD preferred.
• Minimum three (3) years of relevant work experience in machine learning.
• Understanding of fundamental concepts, practices, and procedures of the machine learning field.
• Experience with data discovery, data aggregation, and feature engineering with SQL query writing skills.
• Experience in training, evaluating, optimizing, deploying, and maintaining machine learning models on production systems.
• Skills in logging, tracking, A/B testing, evaluating, and analyzing the performance of different machine learning algorithms and models in production.
• Strong development skills in Python programming language.
• Experience in developing data-driven, scalable, and reliable applications with Amazon Web Services (AWS).
• Ability to apply machine learning algorithms to solve optimization problems like customer sales prediction, recommendation engine, sentiment analysis, and object detection.
• Utilization of popular open-source machine learning/deep learning libraries like LangChain, HuggingFace, Tensorflow, scikit-learn, pandas, pyTorch, and Keras.
• Experience working with relational, non-relational, and high-scale data processing and storage frameworks like SQL, AWS RedShift, Aurora, S3, DynamoDB, MySQL, PostgreSQL.
• Experience with AWS Serverless architecture and AWS native services like BedRock, EC2, Lambda, Step Functions, SageMaker, Rekognition, Comprehend, Lex/Polly, and Transcribe.

Responsibilities

• Design, develop, and deploy machine learning models for real-world applications.
• Build scalable pipelines for data ingestion, pre-processing, training, and inference.
• Own end-to-end development of machine learning algorithms including data analysis, feature engineering, model development, training, validation, and performance evaluation.
• Design, implement, and optimize retrieval-augmented generation (RAG) pipelines that combine large language models (LLMs) with vector search/retrieval systems.
• Build data ingestion and embedding pipelines for efficient indexing and retrieval.
• Fine-tune and adapt LLMs for domain-specific tasks such as instruction tuning, prompt engineering, and low-rank adaptation (LoRA).
• Engage in both engineering and research, exploring latest ML algorithms and solution architectures.
• Work with stakeholders to translate business requirements into robust technical solutions.
• Identify new opportunities to apply ML technology to improve business workflows and processes.
• Build a deep understanding of the company's products, services, data, and customers to deliver impactful solutions.
• Perform other related duties and projects as business needs require.

What This Job Offers

• Education Level: Master's degree