sonarsource - Forward Deployed Engineer, ML
Requirements
• Education: Bachelor's degree in Computer Science or a related field. • Experience: 2+ years of experience in a technical, customer facing role such as Forward Deployed Engineer, or as a Software/ML Engineer with consulting experience. • ML Engineering & Training Expertise: Experience in the Machine Learning lifecycle (training, optimization, deployment), with a proven ability to lead and execute complex model deployments in production environments. • Forward Deployed/Consulting Background: Proven track record working within or closely alongside client engineering teams to successfully deploy and integrate complex, high-performance software, involving cloud or on-premise ML workloads. • Technical & MLOps Knowledge: Understanding of modern ML frameworks, programming languages including Python, and deployment technologies (Docker, Kubernetes, cloud services like SageMaker/Vertex AI/Azure AI). • Value-Driven Influence: Demonstrated ability to influence senior technical leaders and lead engineers, translating complex model performance and system architectures into clear, tangible business value and deployment assurance. • We value diversity, equity, and inclusion
Responsibilities
• End-to-End Ownership: Proactively engage with client or partner teams in Research, Engineering, Data Science, MLOps, Infrastructure to understand their business and technical requirements. With our internal R&D team in the loop, design specific implementations that you will integrate, optimize, and productionize within the client’s existing or greenfield systems as well as transferring technical knowledge to client teams when applicable. • Subject Expert: Stay up-to-date with the latest LLM capabilities and implementation patterns, you are learning driven. You will need to explain complex technical details and concepts to both technical and non-technical audiences. • Influence Model Training & Tuning: Represent our core R&D team on-site, leading technical engagement with modern techniques covering all stages of model training using complex, proprietary client data. Ensure architecture is aligned with and optimized for specific constraints (e.g. GPU types, air-gapping). • Develop Deployment Strategy: Define and execute a global technical strategy for integrating our ML solutions into diverse client environments, ensuring compliance with sector-specific data security standards and performance SLAs. Based on your implementations, build reusable playbooks and libraries that will accelerate yourself and others. • Building Relationships: Operate autonomously and with agency to build strong relationships with clients, create strategic technical partnerships and drive high-value, referenceable production deployments. • Serve as Internal Expert: Act as the primary internal consultant, advising product, research, and sales on real-world client infrastructure limitations, performance bottlenecks, and emerging technical standards necessary for product success.
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