Role Description We are looking for an LLM Fine-Tuning Engineer to design, execute, and operationalize fine-tuning workflows for large language models across supervised, preference-based, and reinforcement learning approaches. The role requires deep practical experience with modern training stacks, careful dataset construction, rigorous evaluation methodology, and the engineering discipline to operate complex training pipelines reliably. The ideal candidate combines strong ML intuition with production-grade engineering practices, and is comfortable navigating the trade-offs between data quality, compute budget, evaluation rigor, and shipping velocity. In this role you will work closely with cross-functional partners — product, design, engineering, operations, and business stakeholders — to translate ambiguous requirements into well-engineered solutions, and will be expected to raise the bar through code review, design review, and mentorship of more junior engineers. The successful candidate brings strong engineering discipline, a clear communication style, and a track record of shipping meaningful work that holds up well in production. Qualifications - Master’s or PhD in Computer Science, Machine Learning, or a related field; or equivalent experience. - Six or more years of combined ML research and engineering experience, with significant LLM exposure. - Strong proficiency in Python and modern deep learning frameworks, especially PyTorch. - Hands-on experience fine-tuning transformer-based language models at non-trivial scale. - Familiarity with distributed training strategies including FSDP, ZeRO, and pipeline parallelism. - Experience with RLHF, DPO, or other preference optimization techniques. - Strong understanding of evaluation methodology, benchmarks, and human evaluation design. - Experience operating training jobs on GPU clusters and recovering from failures. - Strong written and verbal communication skills. - Track record of shipping or publishing impactful LLM work. Requirements - Design and execute fine-tuning experiments for large language models using supervised, DPO, RLHF, and related techniques. - Lead dataset construction, curation, and quality assurance processes for instruction tuning and preference data. - Build scalable training pipelines on top of modern distributed training frameworks. - Tune hyperparameters, optimizer configurations, and training stability strategies for large-model fine-tuning. - Implement parameter-efficient fine-tuning techniques such as LoRA, QLoRA, and adapter-based methods. - Design rigorous evaluation suites including automated benchmarks, human evaluation, and capability-specific probes. - Implement safety, refusal, and policy evaluations to track model behavior across releases. - Operate large-scale training jobs on GPU clusters, diagnosing failures and recovering training state reliably. - Optimize training throughput using mixed precision, sequence packing, and efficient attention implementations. - Manage model artifacts, lineage tracking, and reproducibility across many concurrent experiments. - Collaborate with product, research, and platform teams to align fine-tuning roadmaps with business needs. - Document training methodology, results, and decisions clearly for technical and non-technical audiences. - Mentor engineers on fine-tuning best practices, evaluation rigor, and responsible deployment. - Stay current with LLM research and translate advances into production-ready fine-tuning recipes. Benefits - Competitive base salary commensurate with experience, plus benefits.

• Develop and maintain web applications using modern technologies • Collaborate with cross-functional teams to deliver high-quality software • Participate in code reviews and contribute to technical architecture decisions • Mentor junior developers and share best practices

• Strategic Account Ownership: Own and grow strategic customer relationships, acting as the primary point of contact for executive stakeholders and technical teams alike. • Drive AI Infrastructure Revenue: Accelerate the adoption of Vultr’s AI cloud infrastructure solutions by identifying opportunities and guiding customers through the sales cycle—from initial engagement through solution architecture and scale-up. • Customer-Centric Engagement: Understand customer priorities, technical requirements, and business goals to position Vultr’s value proposition effectively and deliver tailored solutions. • Trusted Advisor: Provide thought leadership on AI/ML trends, infrastructure needs, and optimization strategies to senior leaders within your accounts. Help customers navigate the AI landscape and make informed architectural decisions. • Collaborate for Success: Work cross-functionally with Product Management, Solutions Engineering, and Customer Success to ensure alignment on product capabilities, roadmap feedback, and long-term success. • Sales Process Excellence & Operational Hygiene: Document and maintain accurate sales activity data in CRM tools to support MRR forecast accuracy. • AI Ecosystem Engagement: Understand and leverage the AI partner ecosystem to enhance Vultr’s value proposition for Founders and C-suite executives.

• Diseñar y desarrollar soluciones de Inteligencia Artificial listas para producción utilizando modelos tradicionales y Generative AI. • Liderar técnicamente la implementación de arquitecturas basadas en LLMs, RAG y agentes inteligentes. • Diseñar APIs, microservicios y componentes reutilizables para acelerar nuevas iniciativas de IA. • Integrar modelos de IA con sistemas corporativos, plataformas cloud y fuentes de datos estructurados y no estructurados. • Diseñar arquitecturas escalables sobre Azure, AWS o GCP considerando disponibilidad, seguridad y mantenibilidad. • Implementar prácticas de MLOps y LLMOps para automatizar despliegues, versionamiento, monitoreo y operación de modelos. • Diseñar e implementar pipelines de procesamiento de datos (ETL/ELT) para soluciones de IA. • Definir estrategias de evaluación, observabilidad y monitoreo del desempeño de modelos y agentes. • Colaborar con equipos de negocio para transformar requerimientos funcionales en soluciones técnicas de alto impacto. • Promover buenas prácticas de arquitectura, calidad, seguridad y gobernanza dentro de las iniciativas de Inteligencia Artificial. • Implementar soluciones de IA utilizadas en producción con impacto medible en el negocio. • Reducir el tiempo de desarrollo e implementación mediante arquitecturas reutilizables. • Diseñar componentes escalables que aceleren futuras iniciativas de IA. • Garantizar soluciones con alta disponibilidad, observabilidad y desempeño. • Mejorar continuamente la calidad y precisión de modelos y agentes desplegados. • Cumplir con estándares de seguridad, gobernanza y buenas prácticas de ingeniería.