Senior ML Engineer designing and developing machine learning models for national security. Collaborating with cross - functional teams to deliver scalable solutions in defense applications.
About the role
- Staff ML Engineer developing GenAI infrastructure at Zendesk. Leading design and optimization of ML platforms while fostering technical excellence and collaboration.
Responsibilities
- Lead the design, rollout, and optimization of ML Platform components including model serving, feature management, training pipelines, and evaluation frameworks.
- Own the design, rollout, and optimization of Zendesk’s LLM Proxy, enabling safe, observable, and cost-efficient access to multiple foundation models.
- Champion best practices around service resilience, observability, cost efficiency, and performance optimization.
- Partner with stakeholders to ensure platform investments align with Zendesk’s AI/ML roadmap and business priorities.
- Foster a culture of technical excellence, inclusion, and mentorship within the engineering team.
Requirements
- 7+ years of hands-on experience developing and deploying ML models or Generative AI.
- Proven success in production deployments with a focus on scalability, reliability, and availability.
- Familiarity with MLOps best practices (CI/CD for ML, model monitoring, automated retraining pipelines).
- Deep understanding of LLM systems, GenAI applications, or ML/AI platform components such as vector databases, serving layers, and orchestration tools.
- Experience with provisioning and deploying services with a cloud provider (GCP, AWS, Azure).
- Fluency in any server-side programming language and the testing frameworks (Python, Java, Scala, Golang, Ruby).
- Sound understanding of architecture and software design patterns for server-side domains.
- Experience owning the full lifecycle of ML/AI platform components from early design to production deployment.
Benefits
- Full ownership of the projects you work on.
- Team of passionate people who love what they do.
- Exciting projects, ability to implement your own ideas and improvements.
- Opportunity to learn and grow.
- Flexible working hours.
- Professional development funds.
- Comfortable office and a remote setup.
- Choice of your laptop and other equipment.
- Premium Medical Insurance as well as Private Life Assurance.
Job title
Job type
Experience level
Salary
Not specified
Degree requirement
Tech skills
Location requirements
Machine Learning Engineer developing and deploying ML planning algorithms for autonomous trucks. Join Plus, a leader in AI - based virtual driver software for autonomous trucking.
Intern for Servo Engineering at Seagate, integrating AI/ML into precision servo design. Collaborating on research and optimization of control algorithms for hard disk systems.
Intern role focused on Machine Learning and Generative AI projects for Seagate's innovative data solutions. Contributing to precision - engineered storage initiatives in Singapore.
Senior ML Platform Engineer at GEICO focusing on building scalable machine learning infrastructure and managing AI applications. Responsible for design, implementation, and mentoring within the ML team.
Senior Staff Machine Learning Engineer developing and integrating ML systems for GEICO’s Claims organization. Collaborating on AI - powered capabilities to enhance decision - making and user experience.
Principal Machine Learning Engineer optimizing video recommendation systems for Snap. Collaborating with cross - functional teams to advance machine learning strategies and improve tech stack.
Machine Learning Engineering Manager at Snap Inc. leading engineering teams to develop models for value creation. Responsible for technical evaluations, product scalability, and engineering excellence.
Intern working on servo controller design and AI technologies for hard disk drives. Collaborating on projects involving cutting - edge control systems and presenting findings to engineering teams.
Senior/Principal Machine Learning Engineer designing ML systems for Workday’s AI agents. Overseeing full lifecycle from problem framing to deployment while collaborating with cross - functional teams.