SunnyData
Machine Learning Engineer
- Location
- Argentina, AR
- Arrangement
- Remote
- Employment type
- Full-time
- Posted
- 28 August 2026 (about a month ago)
Checked about a month agoApplications go to the employer, never to RoleSprint
About this role
At SunnyData, our mission is to help customers build a highly scalable architecture, robust data engineering pipelines, easy data consumption layers and more importantly build ML and AI applications to power their business and drive outstanding business outcomes. As a Machine Learning Engineer you will lead complex data projects, develop predictive models, and deploy scalable machine learning solutions. You will work cross-functionally with engineering, product, and analytics teams to derive actionable insights and influence key business decisions.
The Impact You Will Have
- Lead the design, development, and deployment of machine learning models.
- Work with large, complex datasets to extract valuable insights and build predictive analytics pipelines.
- Collaborate with data engineers to architect and optimize cloud-based data solutions.
- Translate business challenges into data-driven solutions using statistical modeling and machine learning techniques.
- Automate data workflows and model deployment processes using cloud services and CI/CD tools.
- Mentor junior data scientists and contribute to best practices in model development and operationalization.
- Communicate findings and strategic recommendations to stakeholders and executive leadership.
What We Look For
- 4+ years of experience in data science or machine learning roles.
- Proficient in Python (pandas, scikit-learn, PyTorch or TensorFlow) and SQL.
- Strong background in statistics, A/B testing, and machine learning algorithms.
- Experience building and deploying models in production environments.
- Familiarity with MLOps practices and tools (e.g., MLflow, SageMaker Pipelines, Airflow).
- Excellent communication and leadership skills.
PREFERRED QUALIFICATIONS
- Experience with big data tools (Spark, EMR).
- Familiarity with containerization (Docker, ECS, EKS) and serverless architecture.
Education
- Bachelor’s or Master’s degree in Computer Science, Statistics, Mathematics, or a related field is preferred.
Our Commitment to Diversity and Inclusion:
SunnyData is dedicated to building a workforce that reflects the world around us. We are an equal opportunity employer committed to unbiased hiring practices. All qualified applicants will receive consideration for employment without regard to race, religion, gender identity, disability, veteran status, or any other protected characteristic.
Why Join SunnyData?
- Innovative Environment: Work with cutting-edge technologies and industry leaders in data engineering and AI.
- Customer Impact: Make a real difference in how businesses leverage data for strategic decision-making.
- Career Growth: Opportunities for professional development and career advancement.
- Collaborative Culture: Join a supportive team that values collaboration and knowledge sharing.
If you are passionate about data engineering and enjoy engaging with clients to solve their most challenging problems, we would love to hear from you. Apply today and become a key player in SunnyData's success story.
Work location
- AR
Related jobs
Ready to make a decision?
This role is either worth your time or it isn’t.
Analyze the posting against your experience, see the gaps clearly, and build the right materials only if the opportunity makes sense.
Nothing is submitted automatically. You choose what happens next.
About this listing
Advertised by Sunnydata and published on Ashby, the applicant tracking system they use.
RoleSprint is not the employer and not a recruiter. Applications are made on the employer’s own site and never reach us; what RoleSprint does is help you decide whether a role is worth your time and prepare for it if it is.
Published 28 August 2026, last checked about a month ago. A posting stops being advertised here 90 days after the employer published it, and one the employer takes down is marked closed rather than quietly removed.