Deep Learning Engineer

Job Title: Deep Learning Engineer

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Location:

[Location] (Opportunities for remote/hybrid/flexible work available)

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Reports to:

Head of Engineering​

 

Role Purpose

We are seeking a highly skilled and innovative Deep Learning Engineer to join our team at [Company Name]. Based in [Location] with options for remote or flexible work, this role will involve designing, implementing, and optimizing cutting-edge deep learning models to elevate our products and services, helping us push the boundaries in [Industry]. You’ll play a pivotal role in developing high-impact solutions, working alongside talented data scientists and engineers committed to shaping the future through advanced AI technologies.

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Company Overview

Summarise your company and it’s culture.[e.g. [Company Name] is a leading [sector] company specializing in [e.g. developing advanced artificial intelligence solutions for specific industry or application]. Our mission is to [company mission]. We foster a collaborative and inclusive work environment that encourages creativity and professional growth.] Include USPs and technical innovation or focus that will appeal to the ideal candidate [e.g. Recognized as one of the "Best Places to Work" by [Awarding Body], we are committed to excellence in AI technology.]

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Key Responsibilities

 

• Design and develop deep learning models tailored for complex tasks, such as computer vision, natural language processing, and speech recognition, to address real-world challenges and meet specific product requirements.

• Implement neural networks and optimize algorithms, ensuring they perform efficiently within production systems.

• Conduct extensive data analysis, including data preprocessing and feature engineering, to prepare large, complex datasets for model training and validation.

• Optimize model performance and scalability, utilizing techniques like distributed training, hyperparameter tuning, and model compression to improve efficiency and deployment feasibility.

• Work closely with cross-functional teams, including data scientists, software engineers, and product managers, to integrate deep learning solutions seamlessly into the overall architecture and ensure they align with business goals.

• Stay at the forefront of advancements in deep learning and AI, incorporating the latest research findings into the development pipeline to enhance model performance and explore novel approaches.

• Identify and resolve any issues related to model performance, deployment, and data quality to ensure reliable, high-performing models.

• Document model architectures, processes, and best practices for future reference and contribute to internal knowledge sharing.

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Required Skills and Qualifications

• Proven experience in developing, deploying, and maintaining deep learning models in a production environment, with a portfolio or project examples demonstrating applied deep learning expertise.

• Strong programming skills in Python, with proficiency in libraries and frameworks such as TensorFlow, PyTorch, and Keras.

• Solid understanding of neural network architectures, including CNNs, RNNs, and attention mechanisms, and their applications to various domains.

• Familiarity with GPU acceleration, distributed training, and cloud-based AI services.

• Proficiency with data manipulation and analysis using libraries like NumPy, Pandas, and Scikit-learn.

• Strong problem-solving skills with a keen attention to detail and an ability to tackle complex technical challenges.

• Excellent interpersonal skills, with the ability to collaborate effectively within a multidisciplinary team environment.

• Knowledge of MLOps tools and practices, including Docker, Kubernetes, and model version control.

• Experience with version control systems such as Git and CI/CD pipelines for AI model deployment.

• Familiarity with additional programming languages (e.g., C++, Java) or cloud platforms (e.g., AWS, Azure, Google Cloud) is a plus.

• Bachelor’s or Master’s degree in Computer Science, Machine Learning, Data Science, or a related field. A Ph.D. in a relevant area is a plus.

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Perks and Benefits:

Clearly outline the benefits and perks of the role.​

 

How to Apply:

End with a strong call to action encouraging candidates to apply. Include a direct link to the application page and provide contact information for further queries.

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Please ensure each job description includes all relevant information in compliance with local, state, and national laws. This includes:
 

 

• Salary Information: Provide a clear salary range to maintain transparency and meet legal requirements.

• Privacy Policies: Protect candidate privacy by following all applicable data protection and privacy laws.

• Equality & Non-Discrimination: Include an equal opportunity statement to uphold our commitment to a diverse, inclusive workplace that does not discriminate based on race, gender, age, disability, or any other protected characteristic.

• Accessibility: Make reasonable accommodations available for candidates with disabilities and include information on how they can request assistance throughout the hiring process.

• Environmental and Social Responsibility: If your company has sustainability initiatives or community engagement programs, mentioning them briefly can attract candidates who prioritize working for socially responsible employers.

• Transparent Hiring Process: Briefly explain the hiring process (e.g., “Our interview process typically includes three stages: an initial screening, a technical interview, and a final interview”) to help candidates know what to expect.