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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Cloud Solution Engineer (Genomics & AI) - **Company:** Oracle - **Location:** Atlanta, GA, United States - **Experience:** Expert - **Salary:** $118,700.0 - $194,400.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Audit Trail, Automation of Tests, Bioinformatics, Cloud Computing, Cloud Engineering, Computational Biology, Computer Programming, Information Leak Prevention, Linux, Distributed Systems, General-Purpose Computing on Graphics Processing Units, Python (Programming Language), Laboratory Information Management Systems, Machine Learning, Oracle (Applications), Performance Tuning, Tensorflow, Scientific Computating, Software Engineering, Workflow Management Systems, Cloud Platform System, High Performance Computing, Pytorch, Deep Learning, Model Validation, Git, Scikit Learn, Kubernetes, Infrastructure Automation Frameworks, Information Technology, Data Management, Machine Learning Operations, Oracle Cloud Infrastructure, Software Version Control - **Published:** October 26, 2026 - **Apply:** https://www.atlantacareerpath.com/job.asp?id=3468437552&tx=TT4542THT&pt=1&aff=0B19D771-A501-4A5E-8338-2A822B784D54&utm_source=Job%20Feed&utm_medium=textkernel&utm_campaign=DE&utm_term=0B19D771-A501-4A5E-8338-2A822B784D54 ## About the Role * Eight or more years of relevant technical experience, including hands-on work, in bioinformatics, computational genomics, scientific computing, cloud engineering, or closely related areas. * Demonstrated ability to build and troubleshoot genomic analysis workflows, with a strong understanding of sequencing data, genomic variation, statistical analysis, and the biological interpretation of results. * Strong programming skills in Python and working proficiency in R or an equivalent scientific computing language. Experience with Linux, Git, containers, and workflow tools such as Nextflow, WDL/Cromwell, or Snakemake. * Practical experience developing, evaluating, or deploying machine learning models for biological or other complex scientific datasets, using frameworks such as PyTorch, TensorFlow, or scikit-learn. * Experience designing cloud or high-performance computing solutions, including compute and storage sizing, workflow scheduling, security, performance optimization, and cost management. * Demonstrated executive presence, strong interpersonal skills, and clear verbal and written communication. Able to translate complex scientific and technical requirements into actionable recommendations, build trusted relationships with researchers and senior decision-makers, and connect technical tradeoffs to business priorities, costs, and measurable outcomes. * Ability to travel up to 50 percent domestically and internationally., * Bachelor's degree in bioinformatics, computational biology, computer science, genetics, engineering, mathematics, or a related field, or equivalent relevant experience. An advanced degree in a relevant field is highly desirable. * Depth in one or more areas: population or translational genomics; oncology and liquid biopsy; rare disease analysis; single-cell or spatial omics; epigenomics and 3D genome biology; or CRISPR and perturbation-based functional genomics. * Experience with tools such as GATK, DeepVariant, NVIDIA Parabricks, or comparable genomics software; GPU acceleration; distributed computing; and large-scale genomic datasets. * Experience adapting genomic or biological foundation models, integrating multimodal biological data, or building evidence-grounded AI assistants for scientific workflows. * Familiarity with OCI or another major cloud platform, Kubernetes, infrastructure as code, experiment tracking, and model deployment and monitoring. * Experience supporting sensitive genomic data with access controls, encryption, audit trails, consent-aware data use, and customer validation requirements. * Customer-facing solution architecture, technical consulting, or presales experience, including proofs of concept and successful transitions to production. ## Description The opportunity Oracle is seeking a senior pre-sales engineer with expertise in genomics, software engineering, and applied AI/ML. You will help biotechnology and pharmaceutical companies, research institutions, and genomics laboratories evaluate and adopt Oracle Cloud Infrastructure (OCI) for genomic discovery, precision medicine, and therapeutic research. Partnering with sales, you will translate scientific and business requirements into cloud architectures, demonstrations, and proofs of concept that guide customer decisions and establish a path to production. What you will do * Design and demonstrate genomic analysis workflows, including WGS/WES germline and somatic variant calling, bulk RNA-seq, and single-cell analysis. Apply appropriate quality control, processing, and interpretation methods for each data type. * Design computational solutions for functional genomics and genome engineering research, including CRISPR screen analysis, gene regulation, perturbation response, and therapeutic target identification and prioritization. Partner with experimental scientists to connect model predictions with biological validation. * Develop and evaluate AI/ML approaches for variant prioritization, multi-omics integration, biomarker discovery, and genotype-phenotype modeling. Assess genomic foundation models and sequence-based deep learning where they address a defined scientific question. * Build reproducible pipelines using Python or R, workflow orchestration, containers, version control, and automated testing. Integrate sequencing platforms, laboratory information management systems, sample metadata, and downstream analytical applications. * Architect CPU and GPU computing, storage, networking, and data platforms on OCI. Benchmark accelerated genomics and model training or inference against customer baselines for accuracy, throughput, turnaround time, and cost per sample. * Establish sound model evaluation with appropriate cohort separation, controls for data leakage and batch effects, subgroup analysis, and uncertainty assessment. Maintain data provenance, pipeline and model versioning, and reproducibility. * Lead customer workshops, demonstrations, and proofs of concept with agreed success criteria. Communicate findings, architecture tradeoffs, costs, and recommendations through presentations and written proposals that support customer decisions and production planning. * Partner with sales, product, and engineering teams; advise scientific and executive stakeholders; mentor colleagues; and publish reusable architectures, solution code, and technical content. Bring customer requirements into product roadmap discussions., * Design and demonstrate genomic analysis workflows, including WGS/WES germline and somatic variant calling, bulk RNA-seq, and single-cell analysis. Apply appropriate quality control, processing, and interpretation methods for each data type. * Design computational solutions for functional genomics and genome engineering research, including CRISPR screen analysis, gene regulation, perturbation response, and therapeutic target identification and prioritization. Partner with experimental scientists to connect model predictions with biological validation. * Develop and evaluate AI/ML approaches for variant prioritization, multi-omics integration, biomarker discovery, and genotype-phenotype modeling. Assess genomic foundation models and sequence-based deep learning where they address a defined scientific question. * Build reproducible pipelines using Python or R, workflow orchestration, containers, version control, and automated testing. Integrate sequencing platforms, laboratory information management systems, sample metadata, and downstream analytical applications. * Architect CPU and GPU computing, storage, networking, and data platforms on OCI. Benchmark accelerated genomics and model training or inference against customer baselines for accuracy, throughput, turnaround time, and cost per sample. * Establish sound model evaluation with appropriate cohort separation, controls for data leakage and batch effects, subgroup analysis, and uncertainty assessment. Maintain data provenance, pipeline and model versioning, and reproducibility. * Lead customer workshops, demonstrations, and proofs of concept with agreed success criteria. Communicate findings, architecture tradeoffs, costs, and recommendations through presentations and written proposals that support customer decisions and production planning. * Partner with sales, product, and engineering teams; advise scientific and executive stakeholders; mentor colleagues; and publish reusable architectures, solution code, and technical content. Bring customer requirements into product roadmap discussions.