Machine Learning Engineer II

Whether you’re an experienced professional or just getting started, your contributions matter. If you’re passionate about tackling meaningful challenges alongside talented team members committed to helping each other succeed, all while having lots of fun, we want to hear from you. We offer competitive benefits and salaries, personal and professional development opportunities, flexibility, and much more!

We’re breaking the attack chain. Ready to join us?

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, confidence in their ML engineering expertise, started to take initiative and works with less supervision, is a creative problem solver, and a team player.

WHAT YOU'LL DO

• Work on various ML engineering projects with minimal supervision.
• Collaborate with data scientists to optimize, test, and evaluate ML models.
• Help to design, develop, document, test, and debug software engineering solutions for both customer-facing applications and internal use.
• Help to deploy, scale, and maintain machine learning models in production environments.
• Follow and help enforce our development best practices and standards.
• Actively participate or sometimes take the lead in Agile ceremonies.
• Provide assistance to less experienced team members.
• Engage with data science and engineering teams to understand requirements and constraints for multiple products.
• Collaborate with other engineers and data scientists within a small team structure.
• Continually pursue independent exploration of related MLOps and data science topics and technologies.
• Acquire technical expertise on the development of ML models including LLMs and generative models.
• Acquire domain expertise in one cybersecurity application area.
• Write technical documentation of small-scale components based on architectural design and stated engineering requirements.

QUALIFICATIONS

• At least 4 years of experience in engineering and/or data science roles is required. At least one year of ML engineering experience 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.
• Solid experience developing system architecture and solving engineering problems in at least one major programming language such as Python, Java, or C++.
• Solid knowledge of software engineering and data science techniques and methodologies and demonstrated ability to apply these methodologies in active projects.
• Solid experience in integrating applications with cloud technologies such as AWS.
• Successfully deployed multiple ML models in production environments.
• Expertise in MLOps frameworks such as MLflow and Kubeflow is nice to have.
• Comfort with Python-based frameworks such as PyTorch, TensorFlow, and scikit-learn is a plus.
• Demonstrated ability to collaborate with other engineers and data scientists within a small team.
• Some experience assisting or providing mentorship to junior engineers.
• Good communication and presentation skills.
• Ability to convey complicated technical topics to non-technical people both verbally and in writing.
• Demonstrated problem solving ability and critical thinking skill with a variety of engineering challenges.
• Experience with the Agile/Scrum project development methodology.
• Interest in cybersecurity applications.