Machine Learning Specialist

We are seeking a Machine Learning Engineer Specialist- AI Trainer to join one of our clients. You'll play a critical role in advancing AI models by reviewing and refining outputs generated by cutting-edge large language models (LLMs). By applying your expertise in machine learning, data science, and technical domains, you'll ensure that model outputs maintain high standards of technical accuracy, relevance, and consistency. This role combines analytical thinking with hands-on data quality work, offering research-grade insights into model evaluation and improvement.

- Use internal tools to evaluate and critique AI-generated outputs, focusing primarily on technical and scientific domains.

- Review complex model responses and suggest improvements with an emphasis on clarity, correctness, and domain relevance.

- Contribute to the curation and refinement of datasets used to train and fine-tune AI/ML models.

- Collaborate closely with cross-functional AI teams to identify data patterns, edge cases, and model blind spots.

- Stay updated on model behaviors and guidelines as they evolve, applying sound judgment to nuanced annotation tasks.

- MS or PhD in Computer Science, Machine Learning, Data Science, or a related technical field.

- Alternatively, 3+ years of professional experience as a Machine Learning Engineer or Data Scientist at a top-tier company (e.g., FAANG, leading startups, AI labs).

- Strong grasp of core machine learning concepts, model training workflows, and evaluation strategies.

- Experience with LLMs, NLP systems, or applied ML in production settings is highly desirable.

- Ability to assess complex technical information and provide constructive, detail-oriented feedback.

- Excellent written communication skills, both for technical and explanatory writing.

- Ability to operate independently with sound judgment under ambiguous conditions.

- Passion for AI development, data quality, and technological advancement.

- Publications in machine learning, AI, or computer science journals/conferences.

- Experience working with human feedback loops in ML systems (e.g., RLHF, data annotation, model alignment).

- Teaching, mentoring, or technical writing experience in ML or related technical domains.

- Exposure to generative AI applications or prompt engineering.

RYZ Labs is a startup studio built in 2021 by two lifelong entrepreneurs. The founders of RYZ have worked at some of the world's largest tech companies and some of the most iconic consumer brands. They have lived and worked in Argentina for many years and have decades of experience in Latam. What brought them together is the passion for the early phases of company creation and the idea of attracting the brightest talents in order to build industry-defining companies in a post-pandemic world.

Our teams are remote and distributed throughout the US and Latam. They use the latest cutting-edge technologies in cloud computing to create applications that are scalable and resilient. We aim to provide diverse product solutions for different industries, planning to build a large number of startups in the upcoming years.

At RYZ, you will find yourself working with autonomy and efficiency, owning every step of your development. We provide an environment of opportunities, learning, growth, expansion, and challenging projects. You will deepen your experience while sharing and learning from a team of great professionals and specialists.

- Customer First Mentality - every decision we make should be made through the lens of the customer.

- Bias for Action - urgency is critical, expect that the timeline to get something done is accelerated.

- Ownership -  step up if you see an opportunity to help, even if not your core responsibility. 

- Humility and Respect - be willing to learn, be vulnerable, and treat everyone who interacts with RYZ with respect.

- Frugality - being frugal and cost-conscious helps us do more with less

- Deliver Impact - get things done in the most efficient way. 

- Raise our Standards - always be looking to improve our processes, our team, and our expectations. The status quo is not good enough and never should be.