Skills
2D computer graphics3D computer graphicsAlgorithmsAmazon Web ServicesArtificial intelligenceBudgetCNNCloudCommunication skillsComputational scienceComputer visionDICOMData engineeringData scienceDebuggingDeep learningEditingEngineeringEvaluationFluencyGitHPCJIRAKerasLeadershipLife sciencesLinuxMachine learningMathematicsMedical imagingMultitaskingNumPyOptimizationOrganizational skillsPhysicsProduct developmentProductivityPublicationsPyTorchPythonQARFResearchSOLIDSelf motivatedShellSoftwareSoftware developmentStatisticsStorageTrainingUnixVideoscikit-learnBiologyCCollaborationComputer scienceETLImage processingJavaJavaScriptNetworkingRevision controlScienceSemanticsShell scriptingStreamingUIData ScientistAIChemistryDNAGoogle Cloudchemistsconvolutional neural networksdrug discoverygeneticsgenomegenomicshuman geneticsmendelian inheritanceneural networks
Job Description
Immediate need for senior machine learning/AI engineer
- Prior experience in AI/ML preferably in life sciences/BioTech domain
- Work closely with chemists, biologists and computational scientists across various departments
- Design and develop machine learning methods for the analysis of graph and sequence data.
- ML and AI work on cloud platform (AWS or Google Cloud). Work with massive data sets. Design and build ML/AI algorithms
Qualifications
- PhD in Computer Science, Statistics, Mathematics or, in Chemistry, Biology, Bio Medical space. Industry experience having strong focus on AI methods for drug discovery.
- Expertise in Python, PyTocrh, TensorFlow, Keras
- Strong experience with analysis of single-cell genomics (e.g. scRNA-seq and scATAC-seq) datasets, graph and sequence based data.
- Prior work experience in problems across chemistry and biology as applied to the discovery and development of treatments for disease.
- Experience in data engineering concepts for Machine Learning and machine learning Ops
- Experience in NeurIPS, ICLR, ICML, Pyro. Experience in convolutional neural networks and their applications to DNA sequences in the context of regulatory genomics.
- Experience in Ornstein-Uhlenbeck process), human genetics, genome-wide association studies (GWAS) and online mendelian inheritance in man (OMIM)