Deep Learning Engineer

At Vintti, we specialize in providing US businesses with staffing solutions that feel local. By connecting companies with Latin American professionals operating in compatible time zones, we ensure that work schedules align naturally with US business hours. This temporal harmony facilitates immediate response times, efficient project management, and a cohesive team dynamic regardless of physical location.

A Deep Learning Engineer is a specialized professional focused on designing, developing, and implementing deep learning models and algorithms. They work with vast datasets to train neural networks, enabling machines to perform complex tasks such as image and speech recognition, natural language processing, and predictive analytics. Utilizing advanced frameworks and tools, Deep Learning Engineers contribute to the advancement of artificial intelligence by creating models that mimic human cognition. They collaborate closely with data scientists, machine learning engineers, and other tech specialists to improve model accuracy and efficiency, driving innovative solutions across various industries.

- Master’s or Ph.D. in Computer Science, Electrical Engineering, Statistics, or related field.
- Strong proficiency in deep learning frameworks such as TensorFlow, PyTorch, or Keras.
- Solid understanding of machine learning algorithms and principles.
- Hands-on experience with data preprocessing, augmentation, and pipeline management.
- Proficiency in programming languages such as Python, C++, or Java.
- Experience with GPU programming and optimization techniques.
- Ability to implement and deploy machine learning models in production environments.
- Strong analytical and problem-solving skills.
- Experience with cloud platforms like AWS, Google Cloud, or Azure.
- Familiarity with large-scale data storage and processing technologies (e.g., Hadoop, Spark).
- Excellent communication skills, both written and verbal.
- Strong collaboration and teamwork skills.
- Knowledge of software development lifecycle and agile methodologies.
- Experience with version control systems like Git.
- Proven ability to conduct hyperparameter tuning and model optimization.
- Expertise in model debugging, troubleshooting, and refinement.
- Strong mathematical background, especially in linear algebra, calculus, and probability.
- Ability to stay updated with the latest advancements in deep learning and AI research.
- Experience with visualization tools like TensorBoard or Matplotlib.
- Proven track record of converting research into practical applications.
- Knowledge of compliance, security, and scalability best practices.
- Prior experience mentoring junior team members or interns.
- Strong attention to detail and ability to maintain thorough documentation.
- Ability to develop custom software tools for deep learning projects.
- Strong organizational and time-management skills.

- Design, develop, and optimize deep learning models and algorithms.
- Conduct data preprocessing and augmentation.
- Perform model training, testing, and validation.
- Analyze model performance and conduct hyperparameter tuning.
- Debug, troubleshoot, and refine deep learning models.
- Implement and manage machine learning pipelines.
- Collaborate with cross-functional teams.
- Stay updated with the latest in deep learning and AI research.
- Convert research papers into practical solutions.
- Contribute to code reviews and maintain documentation.
- Integrate deep learning models into production.
- Use visualization tools to communicate model outcomes.
- Automate tasks to streamline model development.
- Optimize hardware and computational resources.
- Participate in brainstorming and solution development.
- Mentor junior engineers and interns.
- Develop custom software tools for data analysis.
- Ensure scalability, security, and compliance of models.

The ideal candidate for the Deep Learning Engineer role is a highly motivated and innovative individual holding a Master’s or Ph.D. in Computer Science, Electrical Engineering, Statistics, or a related field, with a robust understanding of and practical experience in deep learning frameworks such as TensorFlow, PyTorch, or Keras. They possess a strong foundational knowledge of machine learning algorithms, coupled with hands-on expertise in data preprocessing, augmentation, and managing machine learning pipelines. Proficiency in programming languages like Python, C++, or Java, and experience in GPU programming and optimization techniques are essential. This candidate excels in deploying machine learning models in production environments and has a deep analytical mindset, demonstrated by their strong problem-solving skills and ability to conduct hyperparameter tuning and model optimization. They are familiar with cloud platforms including AWS, Google Cloud, or Azure, and adept in large-scale data processing technologies such as Hadoop or Spark. Excellent communication and interpersonal skills are vital for collaborating with cross-functional teams and mentoring junior engineers. The candidate should be well-versed in using visualization tools like TensorBoard or Matplotlib to effectively convey model outcomes. They demonstrate a commitment to continuous learning, staying abreast of the latest advancements in deep learning and AI, and exhibit strong organizational and time-management skills. A proven track record of converting research into practical applications, meticulous attention to detail in maintaining thorough documentation, and a strong ethical standard in professional conduct set this candidate apart. They are resilient, adaptable in a fast-paced environment, and possess a deep passion for artificial intelligence and deep learning, making them an invaluable asset to the team.

- Design, develop, and optimize deep learning models and algorithms.
- Conduct data preprocessing and augmentation to prepare datasets for model training.
- Perform model training, testing, and validation using various deep learning frameworks.
- Analyze model performance and conduct hyperparameter tuning to improve accuracy and efficiency.
- Debug, troubleshoot, and refine models to enhance their functionality and performance.
- Implement and manage machine learning pipelines for efficient data processing and model deployment.
- Collaborate with cross-functional teams including data scientists, software engineers, and product managers.
- Stay updated with the latest research and advancements in deep learning and artificial intelligence.
- Convert research papers and theoretical models into practical, deployable solutions.
- Contribute to code reviews and maintain documentation for deep learning projects.
- Integrate deep learning models into production environments and monitor their performance.
- Use visualization tools to interpret and communicate model outcomes to stakeholders.
- Automate repetitive tasks and workflows to streamline model development processes.
- Optimize hardware and computational resources to accelerate model training and inference.
- Participate in brainstorming sessions and contribute innovative ideas to solve complex problems.
- Provide mentorship and support to junior engineers and interns.
- Develop and maintain custom software tools for data analysis and model evaluation.
- Ensure models adhere to scalability, security, and compliance requirements.

- Innovative thinking and creativity in problem-solving.
- Strong analytical mindset and attention to detail.
- Excellent communication and interpersonal skills.
- Deep passion for artificial intelligence and deep learning.
- High level of self-motivation and initiative.
- Collaborative and eager to work in a team-oriented environment.
- Adaptable and receptive to feedback.
- Strong organizational and time-management skills.
- A commitment to continuous learning and self-improvement.
- Ability to thrive in a fast-paced, dynamic work environment.
- Proven leadership and mentorship abilities.
- Resilience and perseverance in tackling challenging problems.
- Strong ethics and integrity in professional conduct.
- Ability to manage multiple projects simultaneously.
- Meticulousness in maintaining thorough documentation.
- Curiosity and openness to exploring new technological advancements.

- Competitive salary range based on experience
- Comprehensive health, dental, and vision insurance
- 401(k) retirement plan with company match
- Generous paid time off (PTO) and holiday schedule
- Flexible working hours and remote work options
- Professional development programs and continuous learning opportunities
- Tuition reimbursement for further education
- Access to latest tools and technologies
- Collaborative and inclusive work environment
- Opportunities for career advancement and promotion
- Regular team-building activities and events
- Wellness programs and gym membership discounts
- Paid parental leave and family care support
- Stock options and performance-based bonuses
- Commuter benefits or transportation reimbursement
- Employee assistance programs (EAP)
- Annual company retreats and off-site meetings
- Access to industry conferences and seminars
- Mentorship programs and leadership training
- High-impact projects with cutting-edge technologies
- Dedicated time for research and innovation projects