Senior Machine Learning Engineer

Position Summary:
The Senior Machine Learning Engineer builds Machine Learning models, particularly NLP and LLM, and executes large NLP/LLM models on a cloud environment at scale. This role is essential in experienced learning, advancing machine learning skillset, and sharing knowledge with others. As a senior member of the team, the Sr. ML Engineer works well individually and in a team. You have a passion for Machine Learning and enjoy researching state-of-the-art NLP and LLM techniques and applying them to the education domain. You have initiative and follow through with your tasks while communicating with peers on status. The Senior Machine Learning Engineer applies direct ML knowledge and skills to make a direct impact on improving student learning experiences. This role has the courage to challenge the current state and propose innovative ideas. As a great communicator, the Senior Machine Learning Engineer works with cross-functional teams.

Job Details:

• Work from Home
• Monday to Friday | 9 AM to 6 PM MST
• 12 AM to 9 AM Philippine Time

Responsibilities:

• Works closely with the MLE manager to define NLP initiatives, roadmaps, and strategies.
• Collaborates with the product team and project stakeholders to convert business requirements into requisite NLP capabilities.
• Develops, deploys, and optimizes state-of-the-art LLM models for diverse NLP applications.
• Utilizes NLP/LLM techniques to discover valuable insights from unstructured data sources such as call transcripts, emails, mentor notes, etc.
• Utilizes generative AI to build the next-generation learning experience.
• Executes the entire ML development lifecycle including model research, data processing, model training and fine-tuning, model experimenting and evaluation, model improvement, as well as model deployment.
• Collaborates with the Data Engineer team to develop and implement the data processing pipeline to ensure high-quality input for model training and inference.
• Collaborates with the MLOps team to deploy ML models to the production environment, ensuring scalability, reliability, and performance.
• Collaborates with the Software, Infrastructure, and Security teams to integrate ML solutions seamlessly into the ecosystem.
• Stays up to date with state-of-the-art technologies of LLM, NLP, and Deep Learning, and proactively applies them to use cases to drive innovation.
• Works with other team members to create standards and guidelines for ML.
• Follows Agile, ML best practices, and company processes.
• Communicates status and updates with leadership, team members, and other teams.
• Mentors and provides guidance to junior team members.
• Investigates trends and needs for ML models.
• Performs other related duties as assigned.

Knowledge, Skills, and Abilities:

• Experience operating high-availability, fault-tolerant, scalable, distributed software/infrastructure in production utilizing GitOps practices (Terraform preferred).
• Experience with existing MLOps frameworks (Databricks, Seldon, Sagemaker, DVC, etc.).
• Strong background with either Scala (Java), Go, or Python programming experience.
• Substantial experience operating big data infrastructure in a cloud-based ecosystem (AWS preferred).
• Solid understanding of traffic management and networking concepts.
• Experience with stream-processing systems (ksqlDB, Spark Streaming, Apache Beam/Flink, etc.).
• Experience with software engineering standard methodologies (unit testing, code reviews, design documents, continuous delivery).
• Develop and deploy production-grade services, SDKs, and data infrastructure emphasizing performance, scalability, and self-service.
• Ability to conceptualize and articulate ideas clearly and concisely.
• Entrepreneurial or intrapreneurial experience leading the creation of a new product and organization.

Job Qualifications:

Minimum Qualifications:

• M.S. degree or higher in Computer Science, Software Engineering, Data Science, Machine Learning/Deep Learning, Math, Physics, or any related field.
• 5+ years of industry experience in Software Development within a cloud environment.
• 3+ years of industry experience in building large-scale Machine Learning or Deep Learning models, carrying out the entire ML development lifecycle from POC to production release.
• Deep understanding of NLP and LLM concepts, including language modeling, text classification, sentiment analysis, token embeddings, etc.
• Proficient programming skills such as Python, R, SQL, etc.
• Hands-on experience with one or more deep learning frameworks like PyTorch, TensorFlow, Hugging Face, etc.
• Experience with leading cloud and data platforms such as AWS, Azure, Sagemaker, Databricks, etc.
• Experience with data ETL, feature engineering, and visualization techniques.
• Experience with open-source ML tools and APIs such as MLFlow, Streamlit, etc.
• Excellent problem-solving abilities to analyze complex data and requirements towards practical solutions.
• Excellent creative thinking skills to come up with new solutions and approaches.
• Strong communication and collaboration capabilities, being able to work seamlessly with business stakeholders and cross-functional teams.
• Comfortable working in a fast-paced, highly collaborative, dynamic work environment.

Preferred Qualifications:

• PhD preferred.
• Experience with Databricks preferred.
• AWS cloud platform experience preferred.