Staff ML Engineer

Accountabilities:

• Partner with ML researchers working on generative AI teams to identify bottlenecks and improve the speed, scalability, and reliability of research iteration.
• Design, build, and maintain reusable research infrastructure, workflows, models, interfaces, and automation supporting experimentation, training, evaluation, data processing, and model packaging.
• Enable reproducible experiments through consistent environments, dependency management, artifact and model versioning, configuration management, observability, and CI/CD practices.
• Support scalable ML workloads involving large datasets, GPU clusters, distributed computing, and multiple interconnected models, services, and algorithmic components.
• Deliver pragmatic research-enablement capabilities for immediate needs while keeping solutions aligned with the broader ML platform architecture and roadmap.
• Act as a technical bridge between researchers and ML platform teams by translating research pain points into clear platform requirements, validating new capabilities, and supporting adoption of shared infrastructure.
• Improve the path from research to production by making research outcomes easier to reproduce, integrate, test, and operationalize.
• Contribute to shared ML engineering standards and architecture while promoting strong engineering practices through hands-on collaboration, technical guidance, and knowledge sharing.
• Evaluate and introduce technologies that can materially improve research velocity, reliability, scalability, and cost efficiency.

Requirements:

• Proven experience building reusable infrastructure, developer tools, or platforms that enable multiple engineers or researchers rather than supporting only a single predefined model or pipeline.
• Strong proficiency in Python and Linux, including the ability to build sustainable software, automation, and services.
• Hands-on experience with Docker, Kubernetes, CI/CD pipelines, cloud environments such as AWS, and Infrastructure as Code tools such as Terraform.
• Practical understanding of the end-to-end ML lifecycle, including data preparation, experimentation, training, evaluation, model and artifact management, packaging, deployment, and monitoring.
• Experience supporting compute-intensive or distributed workloads and diagnosing reliability, performance, resource, and cost bottlenecks.
• Practical knowledge of modern ML frameworks such as PyTorch, with sufficient understanding of model behavior and constraints to collaborate effectively with ML researchers; this is an engineering and infrastructure role rather than a research scientist position.
• Ability to work with ambiguous and evolving requirements, uncover underlying needs, and translate them into simple, reusable engineering capabilities.
• Strong communication and collaboration skills across research, engineering, and platform teams.
• Advanced English proficiency is required across conversational communication, writing, and reading.
• Strong interest in diversity, inclusion, and accessibility, combined with curiosity, collaboration, an action-oriented approach, and comfort working in ambiguous, early-stage environments.
• Impact-oriented mindset, with a focus on meaningful outcomes rather than output alone.
• Experience supporting ML research in areas such as language modeling, machine translation, computer vision, multimodal or generative AI, robotics, or autonomous systems is desirable.
• Experience scaling GPU clusters, distributed training, and large-scale data processing is a plus.
• Experience building researcher-focused ML platforms, self-service experimentation environments, or tools supporting multiple evolving research workflows is advantageous.
• Experience helping research teams migrate to or adopt shared ML platforms is desirable.
• Knowledge of inference optimization techniques, such as custom GPU kernels, is a plus but secondary to strong research-enablement and ML infrastructure experience.

Benefits:

• CLT employment contract, providing statutory employment rights and protections.
• Caju Benefits Card with R$1,160 monthly allowance, with flexibility across meal, food, home office, culture, and mobility expenses.
• Fully remote work from anywhere in Brazil.
• SulAmérica medical insurance.
• SulAmérica dental insurance.
• SulAmérica life insurance.
• Conexa Saúde online consultations and telemedicine.
• Wellhub membership.
• Extended year-end holiday break.
• Birthday day off.
• Extended parental leave.
• Continuous professional development through LinkedIn Learning and an annual allowance for courses and training.
• University partnerships offering discounts and additional learning opportunities.
• Libras training.
• English Pass language-learning program.
• Work equipment provided as part of the onboarding kit.