Roles
Deep Learning Engineer
Deep Learning Engineer

Deep Learning Engineer

Specialize in AI model development, optimization, and deployment across various platforms.

Overview
Tests
Advanced Analytics
Job Family
AU$150k
Salary
Average salary in Australia
17%
Job Growth
The number of positions relative to last year
32
Open Roles
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.

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

Deep Learning Engineer Levels

Intern Deep Learning Engineer

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

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

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)

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

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

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.

Common Deep Learning Engineer Required Skills

Activation FunctionsActivation FunctionsAdaptabilityAdaptabilityAirtableAirtableAmazon KinesisAmazon KinesisAnalytical ReasoningAnalytical ReasoningAnalytics ProgrammingAnalytics ProgrammingAnomaly DetectionAnomaly DetectionApache AirflowApache AirflowApache BeamApache BeamApache CassandraApache CassandraApache IcebergApache IcebergApache ImpalaApache ImpalaApache KafkaApache KafkaApache SparkApache SparkAPIsAPIsApplication Scaling StrategiesApplication Scaling StrategiesArtificial IntelligenceArtificial IntelligenceArtificial Intelligence EngineeringArtificial Intelligence EngineeringArtificial Neural NetworksArtificial Neural NetworksAutocorrelationAutocorrelationAutomated TestingAutomated TestingAutomationAutomationAutoMLAutoMLAzure Data LakeAzure Data LakeBackpropagationBackpropagationBaggingBaggingBalancing TreesBalancing TreesBashBashBatch NormalizationBatch NormalizationBayes TheoremBayes Theorem
BERT
BERTBiasBiasBig Data MiningBig Data MiningBinary SearchBinary SearchBlind-spot BiasBlind-spot BiasBonferroni CorrectionBonferroni CorrectionBoostingBoostingBranchingBranchingCachingCachingCaffeCaffeChatGPTChatGPTChi-Squared DistributionChi-Squared DistributionCI/CDCI/CDClassesClassesClassification Loss FunctionsClassification Loss FunctionsCloud ComputingCloud ComputingCloud MonitoringCloud MonitoringCloud PlatformsCloud PlatformsCode ReviewsCode ReviewsCognitive ComputingCognitive ComputingCollaborationCollaborationCollectorsCollectorsCommand Line ScriptingCommand Line ScriptingCommittingCommittingComplexityComplexityComputer ScienceComputer ScienceComputer VisionComputer VisionConcurrencyConcurrencyConcurrency ControlsConcurrency ControlsConditional OperatorsConditional OperatorsConditional ProbabilityConditional ProbabilityConfusion MatricesConfusion MatricesConscientiousnessConscientiousnessContent Management SystemsContent Management SystemsContinuous LearningContinuous LearningConversational AIsConversational AIsConvolutionConvolutionConvolution MatricesConvolution MatricesCost FunctionsCost FunctionsCQRSCQRSCreativityCreativitycroncronCross ValidationCross ValidationCustomer AnalyticsCustomer AnalyticsDaskDaskData CatalogingData CatalogingData FabricData FabricData FederationData FederationData IntegrationData IntegrationData LakeData LakeData LakehouseData LakehouseData LeakageData LeakageData MaskingData MaskingData MiningData MiningData Pipeline OrchestrationData Pipeline OrchestrationData ScienceData ScienceData ScrapingData ScrapingData StructuresData StructuresData TransferData TransferDatadogDatadogDataFramesDataFramesDAXDAXDebuggingDebuggingDecision TreesDecision TreesDeep LearningDeep LearningDeep Learning EngineeringDeep Learning EngineeringDenial of ServiceDenial of ServiceDependency InversionDependency InversionDesign PatternsDesign PatternsDevOpsDevOpsDistance MetricsDistance MetricsDistributed ComputingDistributed ComputingDistributionsDistributionsDockerDockerEdge AIEdge AIElixirElixirEncapsulationEncapsulationEnthusiasmEnthusiasmError of DecompositionError of DecompositionEvaluation MetricsEvaluation MetricsEvent Data AnalysisEvent Data AnalysisEvent StreamingEvent StreamingExploratory Data AnalysisExploratory Data AnalysisFeature DependenciesFeature DependenciesFeature StoresFeature StoresFew-Shot PromptingFew-Shot PromptingFunnelsFunnelsGenerative Adversarial NetworksGenerative Adversarial NetworksGenerative AIGenerative AIGenerative ModelsGenerative ModelsGenetic AlgorithmsGenetic AlgorithmsGitHubGitHubGoGoGradient DescentGradient DescentGraph AnalyticsGraph AnalyticsHeat MapsHeat MapsHistogramsHistogramsHMMHMMHomoscedasticityHomoscedasticityHypothesis TestingHypothesis TestingImputationImputationIndexingIndexingInternet SecurityInternet SecurityJuliaJuliaJupyter NotebookJupyter NotebookKerasKerasKNNKNNKnowledge GraphsKnowledge GraphsKubeflowKubeflowKubernetesKubernetesLanguage ModelingLanguage ModelingLeadershipLeadershipLinear ExtrapolationLinear ExtrapolationLinked ListsLinked ListsLinuxLinuxLiskov Substitution PrincipleLiskov Substitution PrincipleLispLispLog CollectionLog CollectionLoss FunctionsLoss FunctionsLuaLuaMachine LearningMachine LearningMachine Learning EngineeringMachine Learning EngineeringMapReduceMapReduceMarket Basket AnalysisMarket Basket AnalysisMATLABMATLABMatrix DecompositionMatrix DecompositionMax PoolingMax PoolingMicroservicesMicroservicesMinimum Remaining ValuesMinimum Remaining ValuesMLflowMLflowModel EvaluationModel EvaluationModel ExplanationModel ExplanationModel InterpretabilityModel InterpretabilityModel MetricsModel MetricsModel Performance MetricsModel Performance MetricsModel TrainingModel TrainingModel ValidationModel ValidationModel VarianceModel VarianceMouseflowMouseflowMoving AveragesMoving AveragesMulti-threadingMulti-threadingMulticollinearityMulticollinearityMultilayer PerceptronMultilayer PerceptronMVCMVCNaive BayesNaive BayesNeural Network ArchitectureNeural Network ArchitectureNeural NetworksNeural NetworksNode.jsNode.jsNon-Functional RequirementsNon-Functional RequirementsNoSQL DatabasesNoSQL DatabasesNumPyNumPyObject-Oriented ProgrammingObject-Oriented ProgrammingOIDCOIDCOne-Hot EncodingOne-Hot EncodingOutlier TreatmentOutlier TreatmentOverconfidence BiasOverconfidence BiasOverfittingOverfittingOWASPOWASPP-ValueP-ValueParallel Computing FrameworkParallel Computing FrameworkParameter TuningParameter TuningPartitioningPartitioningPercentagesPercentagesPolymorphismPolymorphismPowerPointPowerPointPresentationsPresentationsProgrammingProgrammingProgramming ArchitecturesProgramming ArchitecturesPrompt EngineeringPrompt EngineeringPythonPythonPyTorchPyTorchQuboleQuboleQuery OptimisationQuery OptimisationQuickSightQuickSightRandom ForestRandom ForestRecurrent Neural NetworkRecurrent Neural NetworkRedisRedisRegressionsRegressionsRegularizationRegularizationRelational Data ModelsRelational Data ModelsRequirements TranslationRequirements TranslationReverting ChangesReverting ChangesRobustnessRobustnessROCROCRShinyRShinyRustRustS3S3SamplingSamplingScalaScalaScikit-learnScikit-learnSearch EnginesSearch EnginesSecure ProgrammingSecure ProgrammingSemi-supervised learningSemi-supervised learningSGDSGDSimilarity FunctionsSimilarity FunctionsSnowflake Data CloudSnowflake Data CloudSOAPSOAPSpeech RecognitionSpeech RecognitionStrategies for Missing DataStrategies for Missing DataString ManipulationString ManipulationStringsStringsSummary StatsSummary StatsSupervised LearningSupervised LearningSVMSVMSynthetic Data GenerationSynthetic Data GenerationTask ManagementTask ManagementTensorFlowTensorFlowTheanoTheanoThrottlingThrottlingTransfer LearningTransfer LearningTransport Layer SecurityTransport Layer SecurityUnixUnixUnsupervised AlgorithmsUnsupervised AlgorithmsUnsupervised LearningUnsupervised LearningUser Behaviour AnalyticsUser Behaviour AnalyticsUser RetentionUser RetentionWorkflow AutomationWorkflow AutomationWorkflow ManagementWorkflow ManagementWormsWormsXMLXMLYAMLYAMLYield AnalyticsYield Analytics

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