Machine Learning Developer

Vintti stands at the forefront of economically advantageous staffing solutions for US businesses. By facilitating partnerships between American companies and Latin American professionals, we offer a pathway to reduced operational costs without sacrificing quality. Our approach enables businesses to reinvest savings into core areas, fostering growth and enhancing overall market competitiveness.

A Machine Learning Developer focuses on designing, building, and deploying machine learning models and algorithms to solve complex problems and improve business processes. By utilizing data science techniques and leveraging large datasets, they create predictive models and recommendations that enhance product functionality and user experiences. A key part of their role involves collaborating with data scientists, software engineers, and domain experts to integrate these models into scalable solutions, ensuring they operate efficiently and accurately in production environments. Continuous iteration and optimization are central to their responsibilities, driving innovation and improvements over time.

- Bachelor's or Master's degree in Computer Science, Data Science, Mathematics, or related field
- Proven experience as a Machine Learning Developer or similar role
- Strong programming skills in Python, R, or equivalent languages
- Proficiency with machine learning frameworks like TensorFlow, PyTorch, Scikit-learn, or Keras
- Experience with data preprocessing, data wrangling, and data visualization
- Solid understanding of machine learning algorithms, statistical models, and neural networks
- Hands-on experience with supervised, unsupervised, and reinforcement learning techniques
- Proficient in conducting exploratory data analysis (EDA)
- Strong problem-solving skills and the ability to troubleshoot issues with machine learning models
- Experience with version control systems like Git
- Knowledge of SQL and NoSQL databases for data storage and retrieval
- Familiarity with cloud platforms such as AWS, Azure, or Google Cloud
- Ability to write efficient, well-documented, and maintainable code
- Strong analytical skills and attention to detail
- Excellent communication skills, both verbal and written
- Ability to work collaboratively in a cross-functional team environment
- Familiarity with continuous integration and deployment (CI/CD) processes
- Understanding of model evaluation metrics and validation techniques
- Knowledge of software engineering practices and principles
- Flexibility and eagerness to learn about new technologies and approaches in machine learning and AI

- Research and identify suitable datasets for training models
- Clean and preprocess data for accurate model training
- Develop and fine-tune ML algorithms using frameworks like TensorFlow, PyTorch, Scikit-learn
- Conduct exploratory data analysis for trend and pattern identification
- Design experiments to validate model performance
- Optimize algorithms for scalability and speed
- Collaborate with data engineers, data scientists, and software developers
- Integrate ML models into existing systems and products
- Monitor and update models in production environments
- Document code and methodologies for reproducibility
- Participate in code reviews
- Stay updated with the latest in ML and AI research
- Troubleshoot and debug model and pipeline issues
- Communicate findings to technical and non-technical stakeholders
- Conduct workshops on ML best practices
- Develop and maintain automated pipelines for model operations

The ideal candidate for the Machine Learning Developer role is a highly analytical and detail-oriented individual with a Bachelor's or Master's degree in Computer Science, Data Science, Mathematics, or a related field, coupled with proven experience in developing machine learning models. They possess strong programming skills in Python or R and are proficient in utilizing leading machine learning frameworks such as TensorFlow, PyTorch, Scikit-learn, or Keras. Demonstrating exceptional capabilities in data preprocessing, wrangling, and exploratory data analysis, they have solid knowledge of various machine learning algorithms, statistical models, and neural networks, along with hands-on experience in supervised, unsupervised, and reinforcement learning techniques. The candidate is adept at troubleshooting and debugging, and brings a strong understanding of SQL and NoSQL databases, cloud platforms like AWS, Azure, or Google Cloud, and version control systems such as Git. They are familiar with best practices in software engineering, continuous integration, and deployment (CI/CD) processes, and can write clean, efficient, and well-documented code. With excellent communication skills, both verbal and written, they can effectively collaborate in a cross-functional team environment, articulating complex technical information to both technical and non-technical stakeholders. The ideal candidate is committed to continuous learning, staying abreast of the latest advancements in machine learning and artificial intelligence, and is willing to share their knowledge through workshops and training sessions. Possessing strong organizational skills, they are capable of managing multiple tasks and projects simultaneously with high accuracy and attention to detail.

- Research and identify suitable datasets for training machine learning models
- Preprocess and clean data to facilitate accurate and efficient model training
- Develop and fine-tune machine learning algorithms using frameworks such as TensorFlow, PyTorch, or Scikit-learn
- Conduct exploratory data analysis to understand trends, patterns, and insights
- Design and implement experiments to validate model performance and accuracy
- Optimize algorithms for scalability, speed, and overall performance
- Collaborate with cross-functional teams including data engineers, data scientists, and software developers
- Integrate machine learning models into existing systems and products
- Monitor model performance in a production environment and perform necessary updates or retraining
- Document code, processes, and methodologies for reproducibility and knowledge sharing
- Participate in code reviews to ensure high-quality and maintainable code
- Stay updated with the latest research and developments in the field of machine learning and artificial intelligence
- Troubleshoot and debug issues related to machine learning models and data pipelines
- Communicate findings and project status to both technical and non-technical stakeholders
- Conduct workshops or training sessions to educate team members on machine learning best practices
- Develop and maintain automated pipelines for model training, validation, and deployment

- Strong analytical and problem-solving skills
- High proficiency in programming languages such as Python or R
- Expertise with machine learning frameworks like TensorFlow, PyTorch, Scikit-learn, or Keras
- Excellent data preprocessing and data wrangling capabilities
- Solid understanding of machine learning algorithms and statistical models
- Hands-on experience with supervised, unsupervised, and reinforcement learning techniques
- Proficient in exploratory data analysis (EDA)
- Ability to troubleshoot and debug machine learning models and data pipelines
- Strong knowledge of SQL and NoSQL databases
- Familiarity with cloud platforms such as AWS, Azure, or Google Cloud
- Experience with version control systems like Git
- Understanding of software engineering practices and principles
- Ability to write efficient, maintainable, and well-documented code
- Strong attention to detail and high accuracy in work
- Excellent communication skills, both verbal and written
- Ability to work effectively in a collaborative, cross-functional team environment
- Commitment to continuous learning and keeping up-to-date with the latest advancements in machine learning and AI
- Capability to conduct and design experiments to validate model performance
- Familiarity with continuous integration and deployment (CI/CD) processes
- Strong organizational skills and ability to manage multiple tasks and projects simultaneously
- Willingness to conduct workshops or training sessions for team members

- Competitive salary range based on experience and qualifications
- Comprehensive health benefits including medical, dental, and vision coverage
- Flexible working hours and remote work options
- Generous paid time off (PTO) and holiday schedule
- Retirement savings plan with company match
- Professional development opportunities and access to industry conferences
- Tuition reimbursement for continued education and certifications
- Employee wellness programs including gym memberships and mental health support
- Opportunity to work on cutting-edge technologies and challenging projects
- Collaborative and inclusive work environment
- Stock options or equity in the company
- Performance-based bonuses and incentives
- Company-sponsored social events and team outings
- Access to the latest tools and resources for development and research
- Opportunities for career advancement and leadership roles
- Support for work-life balance, including parental leave and childcare assistance
- Relocation assistance for qualified candidates
- Access to a global network of industry professionals and experts