88-50100046 Machine Learning Software Engineer

The Position

Genentech, Inc. seeks a Machine Learning Software Engineer at its South San Francisco, CA location.

Duties:

• Lead the design, development, testing, and delivery of software workflows to support machine learning and artificial intelligence efforts focused on drug discovery:

• Consult with stakeholders and other technical teams to identify requirements and coordinates development of solutions.
• Lead efforts to define technical standards for machine learning operational systems.

• Develop and code software programs to deploy machine learning models developed by scientists.

• Research and recommend frameworks and infrastructure for training, maintaining, and deploying machine learning and artificial intelligence models.
• Collaborate with scientists to understand their model requirements and features, writes code to integrate these models with established infrastructure. Update infrastructure as needed to support new modeling modalities and architectures.
• Implement and maintain on-premise and cloud-based infrastructure for training and serving machine learning, artificial intelligence, and physics-based models:

Develop data workflows such as training data curation to support machine learning objectives.

• Contribute to general small molecule informatics software engineering environment by making recommendations and reviewing architectural and code changes.
• Document informatics systems and their relationships to other systems.
• Position allows telecommuting up to 2 days per week.

Education and experience required: Bachelor's degree in Computer Science, Electrical Engineering, Computer Engineering, Applied Math, Statistics or related field, and five (5) years of experience in the job offered or a related position.

Alternative Requirements to the education/experience requirements above: Seven (7) years of experience in the job offered or a related position.

Special Requirements: Experience and/or education must include:

• Python programming to develop and deploy new systems that operationalize machine learning and other models.
• Infrastructure design, development and maintenance for serving machine learning models, ensuring scalable and reliable infrastructure for AI-driven projects.
• Data management and workflow design for machine learning objectives, including training and data curation.
• Artificial intelligence knowledge to design and develop software workflows for drug discovery projects, ensuring the integration and efficient operation of machine learning models within a technical ecosystem.
• Machine Learning orchestration framework (e.g. Kubeflow, Sagemaker);
• Weights & Biases;
• AWS cloud;
• Kubernetes;
• Helm and Kustomize frameworks;
• Docker;
• Java;
• Postgres SQL database;
• Bash programming;
• Slurm;
• GitlabCI;
• ArgoCD;
• Pytorch Lightning;
• Pandas;
• FastAPI for web services creation;
• Git code versioning technology
• Position allows telecommuting up to 2 days per week.

Worksite: 1 DNA Way, South San Francisco, CA 94080

The expected salary range for this position based on the primary location for this position of South San Francisco CA is $218,016 to $283,800. Actual pay within the range will be determined based on experience, qualifications, geographic location, and other job-related factors permitted by law. A discretionary annual bonus may be available based on individual and Company performance. This position also qualifies for the benefits detailed at the link provided below.

Benefits (https://roche.ehr.com/default.ashx?CLASSNAME=splash )

Genentech is an equal opportunity employer. It is our policy and practice to employ, promote, and otherwise treat any and all employees and applicants on the basis of merit, qualifications, and competence. The company's policy prohibits unlawful discrimination, including but not limited to, discrimination on the basis of Protected Veteran status, individuals with disabilities status, and consistent with all federal, state, or local laws.

If you have a disability and need an accommodation in relation to the online application process, please contact us by completing this form Accommodations for Applicants.