Machine Learning Engineer

Vintti is a cutting-edge staffing agency revolutionizing the way US companies build their teams. Leveraging advanced technology and embracing the power of remote work, we connect SMBs, startups, and firms across the United States with top-tier talent from Latin America. Our platform seamlessly integrates professionals into US business ecosystems, regardless of physical borders. Vintti operates on the principle of a borderless future of work, where skills and expertise trump geographical constraints.

A Machine Learning Engineer specializes in designing, building, and deploying machine learning models and algorithms to solve complex problems and enhance operational efficiencies. This role typically involves working with large datasets to train models, coding in languages such as Python or R, and using frameworks like TensorFlow or PyTorch. The engineer collaborates closely with data scientists, software developers, and business analysts to integrate these models into production environments, ensuring they deliver actionable insights and drive data-driven decision-making across the organization. Proficiency in statistics, data analysis, and deep learning techniques is essential for success in this role.

- Bachelor's or Master's degree in Computer Science, Data Science, Mathematics, Statistics, or a related field.
- Proven experience as a Machine Learning Engineer or similar role.
- Strong programming skills in languages such as Python, R, or Java.
- Proficiency with machine learning libraries and frameworks like TensorFlow, PyTorch, Scikit-learn, or Keras.
- Solid understanding of machine learning techniques and algorithms, including supervised and unsupervised learning methods.
- Experience with data preprocessing, feature engineering, and data visualization.
- Familiarity with big data tools and platforms such as Hadoop, Spark, or similar.
- Knowledge of SQL and experience with relational databases.
- Experience with cloud platforms like AWS, Azure, or Google Cloud for deploying machine learning models.
- Strong analytical and problem-solving skills.
- Ability to work effectively in a collaborative, cross-functional team environment.
- Excellent communication skills, with the ability to convey complex technical concepts to non-technical stakeholders.
- Experience with version control systems such as Git.
- Strong attention to detail and commitment to delivering high-quality work.
- Ability to manage multiple tasks and projects simultaneously.
- Familiarity with MLOps practices and tools for continuous deployment and integration of machine learning models.
- Up-to-date knowledge of the latest machine learning research and trends.
- Ability to debug and troubleshoot issues in data pipelines and model performance.

- Design and develop innovative machine learning models and algorithms.
- Preprocess large datasets to extract meaningful insights.
- Analyze data to identify patterns and trends.
- Implement and maintain data pipelines for continuous data ingestion.
- Optimize models using techniques such as parameter tuning and selection.
- Evaluate models using accuracy, precision, recall, and other metrics.
- Collaborate with cross-functional teams for requirement gathering and solution delivery.
- Deploy machine learning models into scalable and reliable production environments.
- Monitor and retrain models based on performance metrics.
- Write and maintain efficient, clear, and robust code.
- Stay updated with the latest advancements in machine learning.
- Participate in code reviews to ensure high-quality standards.
- Document models, algorithms, and processes comprehensively.
- Debug and troubleshoot data pipeline and model performance issues.
- Communicate machine learning concepts to non-technical stakeholders.
- Contribute to the continuous improvement of machine learning practices.
- Prototype new machine learning approaches and technologies.

The ideal candidate for the role of Machine Learning Engineer will possess a Bachelor's or Master's degree in a relevant field such as Computer Science, Data Science, Mathematics, or Statistics, coupled with proven experience in designing and developing sophisticated machine learning models and algorithms. They will have strong programming skills in languages like Python, R, or Java, and will be proficient in using well-known machine learning libraries and frameworks such as TensorFlow, PyTorch, Scikit-learn, or Keras. This candidate will excel in data preprocessing, feature engineering, and data visualization, and will be adept at working with big data tools and platforms like Hadoop or Spark. Their analytical acumen and problem-solving skills will be complemented by an ability to work collaboratively in a cross-functional team environment, gathering requirements and delivering scalable solutions. They will have hands-on experience deploying machine learning models on cloud platforms such as AWS, Azure, or Google Cloud, and will be familiar with MLOps practices for continuous model integration and deployment. Proficiency in SQL, version control systems like Git, and an up-to-date knowledge of the latest machine learning research and trends will be imperative. The candidate will demonstrate excellent communication skills, effectively conveying complex technical concepts to non-technical stakeholders. Personal attributes such as a proactive and innovative mindset, high attention to detail, strong sense of ownership and accountability, and a commitment to continuous improvement and ethical data practices will set them apart. They will be adaptable, eager to learn new technologies, and resilient in troubleshooting and debugging intricate issues, ensuring high-quality work and successful project outcomes.

- Design and develop machine learning models and algorithms to address business problems.
- Preprocess and analyze large datasets to extract meaningful insights and features.
- Implement data pipelines and workflows for continuous data ingestion and model training.
- Optimize model performance through parameter tuning and model selection.
- Conduct thorough evaluations of model accuracy, precision, recall, and other relevant metrics.
- Collaborate with data scientists, software engineers, and domain experts to understand requirements and deliver solutions.
- Deploy machine learning models into production environments and ensure their scalability and reliability.
- Monitor deployed models for performance degradation, retraining as necessary to maintain or improve accuracy.
- Write and maintain clear, robust, and efficient code for machine learning applications.
- Stay updated with the latest research and advancements in machine learning, and apply relevant trends and techniques.
- Participate in code reviews to ensure code quality and adherence to best practices.
- Document models, algorithms, processes, and results comprehensively for future reference and reproducibility.
- Debug and troubleshoot issues in data pipelines and model performance.
- Communicate complex machine learning concepts and results to non-technical stakeholders effectively.
- Contribute to the continuous improvement of the team's machine learning practices, tools, and methodologies.
- Prototype new approaches and technologies to continually advance the organization's machine learning capabilities.

- Innovative and proactive mindset
- Strong problem-solving and analytical abilities
- Excellent collaboration and teamwork skills
- High attention to detail and quality
- Effective communication skills, both technical and non-technical
- Adaptable and eager to learn new technologies
- Strong sense of ownership and accountability
- Time management and multitasking skills
- Commitment to continuous improvement and staying updated with industry trends
- Resilience and persistence in troubleshooting and debugging
- Logical and methodical approach to designing solutions
- Ethical mindset with a focus on data privacy and security

- Competitive salary based on experience and expertise
- Comprehensive health, dental, and vision insurance plans
- Generous paid time off and holiday schedules
- Flexible working hours and remote work options
- 401(k) retirement plan with company matching
- Professional development opportunities, including conferences, workshops, and certifications
- Tuition reimbursement for relevant education and training courses
- Employee wellness programs, including gym memberships and mental health support
- Stock options or equity participation
- Paid parental leave and family care support
- Relocation assistance for qualified candidates
- Cutting-edge technology and tools to support daily work
- Collaborative and inclusive work environment
- Regular team-building activities and social events
- Opportunities for rapid advancement and career growth within the organization