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Deep Learning Engineer Job Description Template

Need a skilled Deep Learning Engineer fast? Use our job description template to attract top talent and streamline your hiring for high-performance web apps.

Written by David G. — Lead Software Engineer - AI, Cloud and Generative AI Platforms · Softaims Deep Learning Engineer network

Deep learning is a subset of machine learning that employs neural networks with multiple layers to analyze various factors of data. It has revolutionized fields such as computer vision, natural language processing, and autonomous systems.

With the explosion of big data and increasing computational power, deep learning has become a critical technology driving innovation across industries. Companies are leveraging deep learning to gain insights, automate processes, and enhance decision-making.

The demand for skilled deep learning developers is at an all-time high, as organizations seek to harness the power of artificial intelligence to maintain competitive advantages and drive growth.

Deep learning developers are at the forefront of technological advancements, building models that can learn and make predictions with unprecedented accuracy and efficiency.

Joining the field of deep learning not only offers the opportunity to work on cutting-edge technology but also to contribute to transformative projects that can shape the future.

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Deep Learning Engineer Job Description Template

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Job Description:

As a Mid-Level Deep Learning Engineer, you will be responsible for owning medium-sized features end-to-end, ensuring they are designed, implemented, and deployed efficiently within our production environment. You will collaborate closely with cross-functional teams, including product managers, designers, and backend engineers, to deliver impactful deep learning solutions that enhance our product offerings. Success in this role means you can independently manage feature development, from initial conception through to deployment, while maintaining high standards of code quality and performance.

In this role, you will be expected to integrate APIs, tune performance, conduct thorough testing, and debug production issues effectively. You will also contribute to the technical roadmap, ensuring our deep learning models are robust, scalable, and aligned with business goals. Your ability to communicate complex technical concepts to non-technical stakeholders will be crucial in driving the adoption of deep learning technologies across the organization.

Tech Stack:

Framework/UI: TensorFlow and PyTorch for building and deploying deep learning models, with Keras as a high-level API for rapid prototyping.
Language: Python as the primary programming language, leveraging its rich ecosystem of libraries for data manipulation and machine learning.
State and data: NumPy and Pandas for data manipulation and preprocessing, ensuring efficient data flow throughout the model lifecycle.
Styling/design system: Matplotlib and Seaborn for data visualization, aiding in model interpretability and result presentation.
API integration: RESTful APIs for seamless integration with backend services, facilitating data exchange and model deployment.
Testing: PyTest and Unittest for ensuring code reliability and model accuracy through rigorous testing practices.
Build and deployment: Docker for containerization, enabling consistent and scalable deployment of deep learning models across environments.
Observability: TensorBoard for monitoring model training and performance metrics, providing insights into model behavior.
Collaboration/platform: GitHub for version control and collaborative development, ensuring seamless code integration and review processes.

How We Work:

Squad delivery: Work in agile squads where each member contributes to the delivery of deep learning features, with clear ownership and accountability.
Product/design/backend collaboration: Engage in regular syncs with product managers, designers, and backend engineers to align on feature requirements and technical implementation.
Code reviews: Participate in peer code reviews to maintain high code quality and share knowledge across the team.
Release cadence: Follow a bi-weekly release cycle, ensuring timely delivery of new features and improvements.
Documentation: Maintain comprehensive documentation of deep learning models and processes, facilitating knowledge transfer and onboarding.
QA partnership: Collaborate with QA engineers to define test cases and ensure deep learning models meet quality standards before deployment.
Production monitoring: Utilize monitoring tools to track model performance and proactively address any issues that arise post-deployment.
Technical decision-making: Contribute to technical discussions and decisions, leveraging your expertise to guide the adoption of best practices in deep learning.

Job Responsibilities:

Design and implement deep learning models to address specific business needs, ensuring they are scalable and maintainable.
Integrate APIs to facilitate data exchange between deep learning models and other systems, ensuring seamless interoperability.
Manage state and data flow within models, employing best practices for efficient data handling and processing.
Conduct testing to validate model accuracy and reliability, utilizing unit and integration tests to ensure code robustness.
Optimize performance of deep learning models, identifying bottlenecks and implementing solutions to enhance speed and efficiency.
Ensure accessibility of deep learning solutions, adhering to industry standards and guidelines to make models usable for all users.
Debug production issues related to deep learning models, employing systematic approaches to identify and resolve problems.
Document model architectures, workflows, and processes to facilitate knowledge sharing and future development.
Review code submitted by peers, providing constructive feedback to maintain high standards of code quality and collaboration.
Collaborate with cross-functional teams to align on project goals and deliverables, ensuring deep learning solutions meet business objectives.
Contribute to the continuous improvement of our deep learning practices, staying updated with the latest industry trends and technologies.
Mentor junior engineers by sharing knowledge and best practices, fostering a culture of learning and development within the team.

Required Skills:

Production deep learning experience: At least 3 years of professional experience, with 2 years focused on deploying deep learning models in production environments.
Python proficiency: Strong command of Python, with experience in using libraries like TensorFlow, PyTorch, and Keras for deep learning applications.
Debugging expertise: Ability to troubleshoot and resolve issues in deep learning models, employing systematic approaches to problem-solving.
Testing knowledge: Experience with unit and integration testing frameworks to ensure model reliability and accuracy.
Git proficiency: Competence in using Git for version control, enabling collaborative development and code management.
API integration skills: Experience in integrating deep learning models with RESTful APIs, ensuring seamless data flow and interaction.
Performance awareness: Understanding of performance optimization techniques for deep learning models, ensuring efficiency and scalability.
Estimation capability: Ability to estimate the time and resources required for deep learning projects, ensuring timely delivery.
Communication skills: Strong ability to articulate complex technical concepts to non-technical stakeholders, fostering collaboration and understanding.
Collaboration mindset: Experience working in cross-functional teams, contributing to shared goals and project success.

Nice to Have:

Advanced framework experience: Familiarity with advanced features of TensorFlow or PyTorch, such as distributed training or model optimization techniques.
Design systems knowledge: Understanding of design systems and their application in creating consistent and reusable components.
Visual regression testing: Experience with tools for visual regression testing, ensuring UI consistency and accuracy.
Accessibility expertise: Knowledge of accessibility standards and best practices, ensuring deep learning solutions are usable by all users.
Internationalization (i18n): Experience with internationalization practices, enabling models to support multiple languages and regions.
Cloud/CDN exposure: Familiarity with cloud platforms and content delivery networks, supporting scalable deployment of deep learning models.
Regulated industry experience: Experience working in regulated industries, understanding compliance requirements and standards.
Analytics/experimentation: Experience with analytics tools and experimentation frameworks, supporting data-driven decision-making.

Interview Process:

Initial Phone Screen (30 minutes): A conversation with HR to discuss your background, experience, and interest in the Mid-Level Deep Learning Engineer role.
Technical Assessment (60 minutes): A coding exercise focused on deep learning concepts, evaluating your problem-solving skills and technical knowledge.
Technical Interview (90 minutes): A practical session with an engineering manager, where you'll work through a real-world deep learning problem and discuss your approach.
Team Interview (60 minutes): Meet with potential team members to assess cultural fit, collaboration skills, and alignment with our working style.
Final Interview (60 minutes): A discussion with senior leadership to explore your career goals, vision for the role, and how you can contribute to the company's success.

Perks and Benefits:

Competitive compensation: A salary package that reflects your skills and experience, with opportunities for performance-based bonuses.
Comprehensive healthcare: Medical, dental, and vision insurance plans to support your health and well-being.
Generous PTO: Paid time off to recharge and maintain a healthy work-life balance.
Learning budget: An annual budget for professional development, including courses, conferences, and certifications.
Remote/hybrid support: Flexibility to work remotely or in a hybrid setup, with necessary equipment provided for your home office.
Retirement plan: A 401(k) plan with company matching to help you save for the future.
Career progression: Opportunities for growth and advancement within the company, with support for your career goals.
Engineering culture: A collaborative and innovative environment where engineers are empowered to make an impact.
Team events: Regular team-building activities and social events to foster a strong sense of community.
Wellness programs: Access to wellness resources and programs to support your physical and mental health.

Salary Expectations:

The salary for a Mid-Level Deep Learning Engineer in 2026 is expected to range from $110,000 to $150,000 USD annually. This range may vary based on factors such as location, company size, industry, and level of specialization. Additionally, compensation packages may include bonuses, equity options, and other financial incentives, particularly for roles in high-demand areas or with specialized skill sets. Remote or hybrid work arrangements may also influence salary expectations, with some companies offering location-based adjustments.

About the Author

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By Osama E.
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11 years of experience

My name is Osama E. and I have over 11 years of experience in the tech industry. I specialize in the following technologies: Classification, Model Optimization, Logistic Regression, Model Tuning, Deep Learning, etc.. I hold a degree in Bachelor of Science (BS), Master of Science (MS), Doctor of Philosophy (PhD). Some of the notable projects I’ve worked on include: AI MedBox, AFFECT-CX, SitejetAi, Avatar.e Applied, Recur Network, etc.. I am based in Kingston, Canada. I've successfully completed 6 projects while developing at Softaims.

Information integrity and application security are my highest priorities in development. I implement robust validation, encryption, and authorization mechanisms to protect sensitive data and ensure compliance. I am experienced in identifying and mitigating common security vulnerabilities in both new and existing applications.

My work methodology involves rigorous testing—at the unit, integration, and security levels—to guarantee the stability and trustworthiness of the solutions I build. At Softaims, this dedication to security forms the basis for client trust and platform reliability.

I consistently monitor and improve system performance, utilizing metrics to drive optimization efforts. I’m motivated by the challenge of creating ultra-reliable systems that safeguard client assets and user data.

Skills:

Previously worked at:IBM Canada

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Deep Learning Engineer Job Description Template

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Deep Learning Engineer Job Description Template

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