Senior Machine Learning Engineer

Key Skills

AWS, Data science techniques, Docker, Google Cloud Platform (GCP), HuggingFace, Kubernetes-K8s, LangChain, Large Language Models - LLMs, Machine learning techniques, Natural Language Processing (NLP), PyTorch, Scikit-learn, spaCy

Job Description

About the Role

Dow Jones is seeking a skilled Senior Machine Learning Engineer to join our AI Engineering Team. You will be responsible for designing, building, and maintaining machine learning pipelines and infrastructure, supporting both conventional and GenAI models. You will collaborate with our data scientists to seamlessly integrate various machine learning models, including large language models (LLMs).

As a key team member, you will play a crucial role in operationalizing machine learning solutions to meet our organization's needs and deliver tangible value. You will leverage your strong software engineering skills to develop robust, secure, and scalable production systems, utilizing your expertise in machine learning algorithms and techniques.

Responsibilities

• Collaborate with data scientists and engineers to integrate ML models into various AI/ML pipelines, covering pre-processing, fine-tuning, and deployment.

• Implement optimized data storage and indexing systems for NLP, utilizing advanced database technologies.

• Develop tools and frameworks for model training, tuning, and evaluation, ensuring seamless infrastructure integration.

• Partner with data scientists and engineers to integrate models into production, leveraging cloud infrastructure as needed.

• Monitor and improve model performance for accuracy and efficiency.

• Provide ongoing support, troubleshoot issues, and implement updates for ML models.

• Build and maintain data processing pipelines for high volumes of structured and unstructured data.

• Develop and maintain documentation for all ML infrastructure and support processes.

• Stay updated on GenAI, NLP, ML, and IR technologies, incorporating best practices and leveraging cloud infrastructure for efficiency.

Qualifications

• Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related STEM field.

• At least 5 years of industrial experience in a machine learning engineering, data science or data engineering role.

• Strong programming skills in Python and/or another high-level language commonly used in machine learning.

• Experience with NLP and Machine Learning frameworks and libraries (e.g., PyTorch, HuggingFace, LangChain, spaCy, NLTK, scikit-learn, etc.).

• Experience in developing and managing NLP-focused data infrastructure, including storage, indexing, and retrieval systems.

• Proficiency in vector storage, indexing, and graph database technologies for scalable operations in cloud environments.

• Familiarity with cloud-based infrastructure and services (e.g., AWS, GCP, etc.).

• Experience with containerization and orchestration technologies, such as Docker and Kubernetes, on cloud-based infrastructure.

• Experience with version control systems such as Git.

• Strong communication and collaboration skills, and a passion for working as part of a team.

Reasonable accommodation: Dow Jones, Making Careers Newsworthy - We are an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity or expression, pregnancy, age, national origin, disability status, genetic information, protected veteran status, or any other characteristic protected by law.EEO/AA/M/F/Disabled/Vets. Dow Jones is committed to providing reasonable accommodation for qualified individuals with disabilities, in our job application and/or interview process. If you need assistance or accommodation in completing your application, due to a disability, email us at talentresourceteam@dowjones.com. Please put Reasonable Accommodation in the subject line and provide a brief description of the type of assistance you need. This inbox will not be monitored for application status updates.