Deep Learning Engineer

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Job Family

AU$150k

Salary

Average salary in Australia

17%

Job Growth

The number of positions relative to last year

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Job openings on Alooba Jobs

Deep Learning Engineers’ role centers on the development and optimization of AI models, leveraging deep learning techniques. They are involved in designing and implementing algorithms, deploying models on various platforms, and contributing to cutting-edge research. This role requires a blend of technical expertise in Python, PyTorch or TensorFlow, and a deep understanding of neural network architectures.

Typical Tasks and Responsibilities of a Deep Learning Engineer

Develop and optimize deep learning models for diverse applications.
Create and implement algorithms and evaluation metrics for deep learning models.
Produce technical reports and contribute to research in the field.
Optimize Deep Neural Networks for deployment on various platforms.
Conduct statistical analysis and model fine-tuning based on test results.
Extend and enhance existing machine learning libraries and frameworks.
Stay updated with the latest advancements in deep learning.
Participate in team brainstorming sessions for innovative solutions.
Develop, optimize, and deploy deep learning architectures on GPUs.
Collaborate in a distributed computing environment for scalable deep learning systems.
Lead project teams and mentor junior data scientists.
Construct and maintain MLOps pipelines for model training and inference.
Manage large and varied datasets for deep learning applications.

Typical Role Requirements of a Deep Learning Engineer

2-10 years of experience in deep learning or a PhD in a STEM field.
Proficiency in Python, with experience in PyTorch or TensorFlow.
Solid understanding of deep neural networks and their applications.
Experience in deploying machine learning solutions in cloud environments.
Strong skills in software development, data structures, and algorithms.
Familiarity with the software development lifecycle, including agile methodologies and software testing.
Knowledge of Docker, Linux, and cloud development.
Bachelor's or Master's degree in Engineering, Computer Science, or related field.
Excellent analytical, development, debugging, and communication skills.
Experience with large-scale deep learning frameworks and datasets.
Understanding of GPU computing (e.g., CUDA, OpenCL).
Advanced degree (PhD, MS) in a quantitative field is preferred.
Minimum of 5 years of experience in training deep learning models.
Proficiency in Python and SQL, with cloud computing experience.
Experience working in a software production environment or a relevant research background.

Core Deep Learning Engineer Required Skills

Analytics Programming

Application Scaling Strategies

Artificial Intelligence

Artificial Intelligence Engineering

Artificial Neural Networks

Automation AutoML

Backpropagation Bagging

Balancing Trees Bash

Bayes Theorem BERT

Bonferroni Correction

Chi-Squared Distribution

CI/CD

Classes

Classification Loss Functions

Command Line Scripting

Conditional Operators

Conditional Probability

Confusion Matrices

Conscientiousness

Content Management Systems

Cost Functions CQRS

Creativity cron

Data Pipeline Orchestration

Data Science

Data Scraping

Data Structures

Data Transfer Datadog

Datadog

DataFrames DAX

DAX

Debugging

Decision Trees

Deep Learning

Deep Learning Engineering

Distributed Computing

Distributions Docker

Error of Decomposition

Evaluation Metrics

Event Data Analysis

Event Streaming

Exploratory Data Analysis

Feature Dependencies

Feature Stores

Few-Shot Prompting Funnels

Funnels

Generative Adversarial Networks

Generative AI

Generative Models

Genetic Algorithms GitHub

Histograms HMM

HMM

Internet Security Julia

Julia

Jupyter Notebook Keras

KNN

Linked Lists Linux

Liskov Substitution Principle Lisp

Lisp

Log Collection

Loss Functions Lua

Lua

Machine Learning

Machine Learning Engineering

MapReduce

Market Basket Analysis MATLAB

Minimum Remaining Values MLflow

MLflow

Model Evaluation

Model Explanation

Model Interpretability

Model Metrics

Model Performance Metrics

Multilayer Perceptron MVC

MVC

Naive Bayes

Neural Network Architecture

Neural Networks

Node.js

Non-Functional Requirements

NoSQL Databases NumPy

NumPy

Object-Oriented Programming

Overfitting OWASP

Parallel Computing Framework

Programming Architectures

Prompt Engineering Python

Recurrent Neural Network Redis

Redis

Regressions

Regularization

Relational Data Models

Requirements Translation

Reverting Changes

Robustness ROC

ROC

Semi-supervised learning SGD

SGD

Strategies for Missing Data

String Manipulation Strings

Strings

Summary Stats

Supervised Learning SVM

SVM

Synthetic Data Generation

TensorFlow Theano

Transport Layer Security Unix

Unix

Unsupervised Algorithms

Unsupervised Learning

User Behaviour Analytics

User Retention

Workflow Automation

Workflow Management Worms

Worms

XML

YAML

Yield Analytics

Discover how Alooba can help identify the best Deep Learning Engineers for your team

Deep Learning Engineer Levels

Intern Deep Learning Engineer

An Intern Deep Learning Engineer is an aspiring professional who supports the development and implementation of deep learning models. They work under the mentorship of experienced engineers and scientists, contributing to projects and gaining hands-on experience in the application of deep learning technologies.

Graduate Deep Learning Engineer

A Graduate Deep Learning Engineer is an emerging talent in the field of artificial intelligence, leveraging foundational skills in machine learning, neural networks, and programming to develop robust deep learning models. They are innovative, tech-savvy, and ready to contribute to the development of cutting-edge AI solutions.

Junior Deep Learning Engineer

A Junior Deep Learning Engineer is a budding professional in the field of artificial intelligence, with a focus on implementing deep learning models. They work under the guidance of senior engineers to develop and optimize neural networks, contributing to innovative AI solutions that drive business growth and technological advancement.

Deep Learning Engineer (Mid-Level)

A Mid-Level Deep Learning Engineer is a specialized professional who designs, develops, and deploys deep learning models to solve complex problems. They apply their expertise in machine learning, neural networks, and programming to create innovative solutions and advance the organization's AI capabilities.

Senior Deep Learning Engineer

A Senior Deep Learning Engineer is an experienced professional skilled in designing and implementing deep learning models. They leverage complex machine learning algorithms and neural networks to solve challenging problems and contribute to the development of AI-powered products and solutions. Their expertise is pivotal in driving innovation and enhancing business performance.

Lead Deep Learning Engineer

A Lead Deep Learning Engineer is a seasoned professional who leverages their extensive knowledge of artificial intelligence and machine learning to develop sophisticated models and algorithms. They lead a team of engineers, oversee project development, and ensure the delivery of high-quality AI solutions.

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