At RBC Borealis, you'll be joining a team of leading researchers and software engineering ... Proficiency in Python and Deep Learning packages such as Tensorflow or PyTorch. What's in it for ...
At RBC Borealis, you'll be joining a team of leading researchers and software engineering ... Proficiency in Python and Deep Learning packages such as Tensorflow or PyTorch. What's in it for ...
Apply knowledge of statistics, machine learning, programming, data modeling, simulation, and ... Build Machine Learning, Deep Learning and statistical models to solve specific business problems.
Apply knowledge of statistics, machine learning, programming, data modeling, simulation, and ... Build Machine Learning, Deep Learning and statistical models to solve specific business problems.
As a Principal Research Developer on the AEC Solutions team, you will join a team of technologists ... Understanding of deep learning architectures (CNNs, Transformers) and familiarity with frameworks ...
As a Principal Research Developer on the AEC Solutions team, you will join a team of technologists ... Understanding of deep learning architectures (CNNs, Transformers) and familiarity with frameworks ...
Research Engineer
Toronto, ON · On-site +1
CA$122K - CA$215K/yr
You will work closely with our team of world-renowned scientists and engineers specializing in deep learning, computer vision, and self-driving technologies to develop cutting-edge solutions that ...
Research Engineer
Toronto, ON · On-site +1
CA$122K - CA$215K/yr
You will work closely with our team of world-renowned scientists and engineers specializing in deep learning, computer vision, and self-driving technologies to develop cutting-edge solutions that ...
ML/AI Research Engineer
CA$103K - CA$192K/yr
D. in Computer Science, Engineering, Mathematics, or a related quantitative field. * 5+ years of applied deep learning experience with end-to-end ownership of complex modelling initiatives. * Strong ...
ML/AI Research Engineer
CA$103K - CA$192K/yr
D. in Computer Science, Engineering, Mathematics, or a related quantitative field. * 5+ years of applied deep learning experience with end-to-end ownership of complex modelling initiatives. * Strong ...
Senior / Staff Perception Engineer
CA$158K - CA$269K/yr
You will work closely with our team of world-renowned scientists and engineers specializing in deep learning, computer vision, and self-driving technologies to develop cutting-edge solutions that ...
Senior / Staff Perception Engineer
CA$158K - CA$269K/yr
You will work closely with our team of world-renowned scientists and engineers specializing in deep learning, computer vision, and self-driving technologies to develop cutting-edge solutions that ...
Engineering Manager - Machine Learning
Toronto, ON · Remote
CA$210K - CA$282K/yr
You will work cross-functionally across ML engineering, data science, and research teams to ... With billions of compounds, 30+ petabytes of experimental data, and complex deep learning workloads ...
Engineering Manager - Machine Learning
Toronto, ON · Remote
CA$210K - CA$282K/yr
You will work cross-functionally across ML engineering, data science, and research teams to ... With billions of compounds, 30+ petabytes of experimental data, and complex deep learning workloads ...
This role is for someone who has specialized experience in machine learning/deep learning domains ... About the Role As a Senior Software Engineer, AI (C#, Cloud) , you will: * Optimize LLMs and ML ...
This role is for someone who has specialized experience in machine learning/deep learning domains ... About the Role As a Senior Software Engineer, AI (C#, Cloud) , you will: * Optimize LLMs and ML ...
Deep knowledge of retrieval augmented generation RAG agentic frameworks context and memory ... engineering, all fueled by its market leading capabilities in AI, generative AI, cloud and data ...
Deep knowledge of retrieval augmented generation RAG agentic frameworks context and memory ... engineering, all fueled by its market leading capabilities in AI, generative AI, cloud and data ...
Data Engineer
Toronto, ON · On-site
Data Engineer and Generative AI/ML Specialist Skill Cluster/Practice: Data Engineering / Emerging ... Apply and productionize Machine Learning and Deep Learning models, including building ...
Data Engineer
Toronto, ON · On-site
Data Engineer and Generative AI/ML Specialist Skill Cluster/Practice: Data Engineering / Emerging ... Apply and productionize Machine Learning and Deep Learning models, including building ...
Experience working with data science tooling and deep learning frameworks is a plus; * Experience with DevOps engineering (CI/CD pipelines, observability, containers etc) is a plus. What's in it for ...
Experience working with data science tooling and deep learning frameworks is a plus; * Experience with DevOps engineering (CI/CD pipelines, observability, containers etc) is a plus. What's in it for ...
Knowledge of machine learning and deep learning algorithms (e.g., supervised methods such as ... A bachelor's or master's degree or higher in computer science, engineering, statistics, or a ...
Knowledge of machine learning and deep learning algorithms (e.g., supervised methods such as ... A bachelor's or master's degree or higher in computer science, engineering, statistics, or a ...
Data Engineer
Toronto, ON · On-site
Data Engineer and Generative AI/ML Specialist Skill Cluster/Practice: Data Engineering / Emerging ... Apply and productionize Machine Learning and Deep Learning models, including building ...
Data Engineer
Toronto, ON · On-site
Data Engineer and Generative AI/ML Specialist Skill Cluster/Practice: Data Engineering / Emerging ... Apply and productionize Machine Learning and Deep Learning models, including building ...
Knowledge of machine learning and deep learning algorithms (e.g., supervised methods such as ... A bachelor's or master's degree or higher in computer science, engineering, statistics, or a ...
Knowledge of machine learning and deep learning algorithms (e.g., supervised methods such as ... A bachelor's or master's degree or higher in computer science, engineering, statistics, or a ...
AI Engineer
Toronto, ON · Hybrid
AI Engineer, AidenSales RBC Capital Markets is seeking an AI Engineer with deep expertise in Generative AI, neural networks, and transfer learning to support Sales teams with cutting-edge AI-powered ...
AI Engineer
Toronto, ON · Hybrid
AI Engineer, AidenSales RBC Capital Markets is seeking an AI Engineer with deep expertise in Generative AI, neural networks, and transfer learning to support Sales teams with cutting-edge AI-powered ...
AI Engineer Lead
Toronto, ON · Hybrid
AI Engineer, AidenSales RBC Capital Markets is seeking an AI Engineer with deep expertise in Generative AI, neural networks, and transfer learning to support Sales teams with cutting-edge AI-powered ...
AI Engineer Lead
Toronto, ON · Hybrid
AI Engineer, AidenSales RBC Capital Markets is seeking an AI Engineer with deep expertise in Generative AI, neural networks, and transfer learning to support Sales teams with cutting-edge AI-powered ...
... deep learning, scientific computation, and self-driving cars to graphics workloads for AAA game ... We need you as a key member of a talented team of compiler engineers that is working on a core ...
... deep learning, scientific computation, and self-driving cars to graphics workloads for AAA game ... We need you as a key member of a talented team of compiler engineers that is working on a core ...
Senior Applied AI/ML Scientist - Listing Quality
CA$180K - CA$247K/yr
LLM fine tuning, deep learning, or human-in-the-loop machine learning) * Strong programming skills * An excitement and willingness to learn new tools and techniques * The ability to design and ...
Senior Applied AI/ML Scientist - Listing Quality
CA$180K - CA$247K/yr
LLM fine tuning, deep learning, or human-in-the-loop machine learning) * Strong programming skills * An excitement and willingness to learn new tools and techniques * The ability to design and ...
... used to accelerate deep learning and GenAI workloads on Amazon's custom machine learning ... Working at the hardware-software boundary, our engineers craft high-performance kernels for ML ...
... used to accelerate deep learning and GenAI workloads on Amazon's custom machine learning ... Working at the hardware-software boundary, our engineers craft high-performance kernels for ML ...
Senior ML Engineer
Toronto, ON · On-site
Collaborating with cross functional teams (Applied Science, DevOps, Data Engineering, Cloud ... and deep learning, with a proven track record of building, hosting, and deploying ML models on ...
Senior ML Engineer
Toronto, ON · On-site
Collaborating with cross functional teams (Applied Science, DevOps, Data Engineering, Cloud ... and deep learning, with a proven track record of building, hosting, and deploying ML models on ...
Deep Learning Developer information
What are the key skills and qualifications needed to thrive as a Deep Learning Developer, and why are they important?
To thrive as a Deep Learning Developer, you need a strong background in computer science, mathematics, and proficiency in programming languages like Python, often supported by a degree in a related field. Familiarity with deep learning frameworks such as TensorFlow or PyTorch, and experience with cloud platforms or GPU acceleration, are commonly required technical skills. Analytical thinking, problem-solving abilities, and effective teamwork distinguish top performers in this role. These competencies are crucial for designing, training, and deploying advanced neural network models that address complex real-world problems.
What are Deep Learning Developers?
Deep Learning Developers are specialized software engineers or data scientists who design, build, and implement artificial intelligence systems using deep learning techniques. They work with neural networks, large datasets, and various frameworks like TensorFlow or PyTorch to develop models for tasks such as image recognition, natural language processing, and autonomous systems. Their responsibilities include data preprocessing, model training, optimization, and deployment to solve complex problems that require advanced pattern recognition. Deep Learning Developers often collaborate with AI researchers, data engineers, and product teams to integrate intelligent features into applications.
Which 3 jobs will survive AI?
Deep Learning Developers are likely to continue to be in demand as AI advances because they design and improve AI models, requiring specialized skills in programming, mathematics, and data analysis. Other resilient roles include AI ethicists, who address ethical considerations, and AI system trainers, who curate and annotate data to improve AI performance. These jobs involve complex problem-solving and human oversight that are less easily automated.
What is the difference between Deep Learning Developer vs Machine Learning Engineer?
| Aspect | Deep Learning Developer | Machine Learning Engineer |
|---|---|---|
| Required Credentials | Bachelor's or Master's in CS, AI, or related; experience with neural networks | Bachelor's or Master's in CS, Data Science, or related; knowledge of algorithms |
| Work Environment | Research labs, AI startups, tech companies focusing on neural networks | Data-driven companies, software firms, industries applying machine learning |
| Industry Usage | Primarily in AI research, neural network development, deep learning projects | Broader application including predictive modeling, data analysis, and ML systems |
Deep Learning Developers specialize in neural networks and deep learning models, often working on AI research and complex algorithms. Machine Learning Engineers have a broader focus on developing, deploying, and maintaining machine learning models across various applications. While both roles require similar educational backgrounds, their focus areas and industry applications differ.
What are some common challenges Deep Learning Developers face when deploying models to production environments?
Deep Learning Developers often encounter challenges such as optimizing model performance for real-time inference, managing resource constraints (like GPU/CPU availability), and ensuring model reproducibility across different environments. Additionally, integrating deep learning models into existing software systems and maintaining them over time can be complex, especially as data and requirements evolve. Collaborating closely with DevOps, data engineers, and QA teams is essential to address these challenges and ensure smooth deployment and ongoing reliability.
