Sr. Machine Learning Engineer

This position is responsible for developing, enhancing, and maintaining our Machine Learning pipelines and data science solutions including data pipelines, infrastructure, and deployment of machine learning models. The scope of the role includes both existing software products and new products in development. The ideal candidate has a strong commitment to excellence, brings a wealth of expertise in ML Engineering, is a creative problem solver, and a team player.

WHAT YOU'LL DO

• Lead highly complex ML engineering projects with extensive latitude for independent judgment.
• Design, develop, document, test, and debug software engineering solutions for both customer-facing applications and internal use.
• Deploy, scale, and maintain machine learning models in production environments.
• Develop scalable ML architecture and pipelines.
• Collaborate with data scientists to optimize, test, and evaluate ML models.
• Identify and evaluate new platforms and technologies to enhance our existing systems.
• Follow and help define and enforce our development best practices and standards.
• Provide mentorship and assistance to less experienced team members.
• Collaborate with architects and engineering managers to maintain development roadmaps and prioritize new features.
• Engage with data science and engineering teams to understand requirements and constraints for multiple products.
• Share your technical knowledge and MLOps framework expertise with team members.
• Maintain up-to-date knowledge of related MLOps and data science topics and technologies.
• Serve as the technical expert on the deployment and benchmarking of ML models including LLMs, and generative models.
• Acquire domain expertise in at least one cybersecurity application area.
• Write technical documentation based on architectural design and stated engineering requirements.

QUALIFICATIONS

• At least 7 years of experience in engineering and/or data science roles is required with at least 1 year of senior role experience included. Multi-year experience as an ML engineer is desirable.
• Strong academic background (M.S or PhD program in a technical field) is an adequate substitute for a few years of industry experience.
• Thorough knowledge of software engineering and data science techniques and methodologies, and experience leading projects applying these methodologies.
• Extensive experience developing system architecture and solving engineering problems in at least one major programming language such as Python, Java, or C++.
• Comprehensive MLOps background and expertise in data processing, model training, and deployment of models as microservices.
• Substantial experience integrating applications with cloud technologies such as AWS.
• Proven track record of deploying and benchmarking ML models in production environments.
• Expertise in MLOps frameworks such as MLflow and Kubeflow is nice to have.
• Solid experience with Python-based frameworks such as PyTorch, TensorFlow, and scikit-learn is a plus.
• Demonstrated ability to collaborate with engineering leads, software architects, and other stakeholders to advance engineering and data science projects.
• Led multiple projects to completion in a small team.
• Proven experience assisting or providing mentorship to junior engineers.
• Excellent communication and presentation skills. Ability to convey complicated technical topics to non-technical people both verbally and in writing.
• Demonstrated excellent problem solving ability and critical thinking skill with a variety of engineering challenges.
• Comfortable and enthusiastic about sharing technical knowledge with team members.
• Interest in cybersecurity applications.