Senior Machine Learning Engineer

As a Senior Machine Learning Engineer at our company, you will play a crucial role in leading the design, development, and scaling of production-grade ML pipelines and infrastructure. Your responsibilities will include: - Leading complex ML engineering initiatives with a high degree of autonomy - Designing, developing, testing, documenting, and debugging software for customer-facing and internal applications - Deploying, scaling, monitoring, and maintaining ML models in production - Building scalable ML architectures, pipelines, and infrastructure - Partnering with data scientists to optimize, test, and evaluate ML models - Implementing and promoting engineering best practices and MLOps standards - Identifying and evaluating new tools, platforms, and technologies - Collaborating with architects and engineering managers to define roadmaps and prioritize features - Mentoring junior ML and software engineers - Working cross-functionally to understand system requirements and constraints - Acting as a technical expert for deployment and benchmarking of ML, LLM, and generative models - Sharing MLOps expertise and technical knowledge across teams - Developing technical documentation based on architectural and engineering requirements - Building domain expertise in at least one cybersecurity application area Qualifications required for this role include: - 7+ years of experience in engineering and/or data science, including 1+ year in a senior role - Strong academic background (MS or PhD in a technical field preferred) - Proven experience as an ML Engineer or in ML-focused engineering roles - Strong foundation in software engineering, system design, and data science methodologies - Hands-on experience with Python, Java, or C++ (Python preferred) - End-to-end experience in ML pipelines: data processing, model training, deployment, and monitoring - Strong MLOps experience, including model deployment as microservices - Experience working with cloud platforms, preferably AWS - Proven track record of deploying and benchmarking ML models in production - Familiarity with MLOps tools such as MLflow and Kubeflow (nice to have) - Experience with ML frameworks: PyTorch, TensorFlow, scikit-learn - Experience collaborating with architects, engineering managers, and cross-functional teams - Demonstrated leadership in delivering projects with small, agile teams - Mentorship experience with junior engineers - Excellent communication skills with the ability to explain complex technical concepts clearly - Strong problem-solving and critical-thinking abilities - Interest or experience in cybersecurity applications Join us to work on scalable, real-world ML and Generative AI systems, enjoy high ownership and technical leadership opportunities, be part of a fully remote, collaborative engineering culture, and have the opportunity to shape ML infrastructure and MLOps best practices. As a Senior Machine Learning Engineer at our company, you will play a crucial role in leading the design, development, and scaling of production-grade ML pipelines and infrastructure. Your responsibilities will include: - Leading complex ML engineering initiatives with a high degree of autonomy - Designing, developing, testing, documenting, and debugging software for customer-facing and internal applications - Deploying, scaling, monitoring, and maintaining ML models in production - Building scalable ML architectures, pipelines, and infrastructure - Partnering with data scientists to optimize, test, and evaluate ML models - Implementing and promoting engineering best practices and MLOps standards - Identifying and evaluating new tools, platforms, and technologies - Collaborating with architects and engineering managers to define roadmaps and prioritize features - Mentoring junior ML and software engineers - Working cross-functionally to understand system requirements and constraints - Acting as a technical expert for deployment and benchmarking of ML, LLM, and generative models - Sharing MLOps expertise and technical knowledge across teams - Developing technical documentation based on architectural and engineering requirements - Building domain expertise in at least one cybersecurity application area Qualifications required for this role include: - 7+ years of experience in engineering and/or data science, including 1+ year in a senior role - Strong academic background (MS or PhD in a technical field preferred) - Proven experience as an ML Engineer or in ML-focused engineering roles - Strong foundation in software engineering, system design, and data science methodologies - Hands-on experience with Python, Java, or C++ (Python preferred) - End-to-end experience in ML pipelines: data processing, model training, deployment, and monitoring - Strong MLOps experience, including model deployment as microservices - Experience working with cloud platforms, preferably AWS - Proven track record of deploying and benchmarking ML models in production - Familiarity