Genentech · 3 days ago

Senior Machine Learning Engineer

South San Francisco, CA

Full-time

Onsite

Senior Level

$188K/yr - $349K/yr

7+ years exp

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Comp. & Benefits

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Responsibilities

Design, enhance, scale, and maintain machine learning solutions from conception to deployment in a production environment.

Collaborate with data scientists, software engineers, and business stakeholders to translate business requirements into scalable ML systems.

Architect and uphold MLOps pipelines using job scheduling frameworks to streamline data preparation, training, deployment, and machine learning model lifecycles.

Ensure best practices in code quality, version control, and CI/CD for machine learning pipelines.

Design and implement robust monitoring systems for deployed models to track performance, data drift, and anomalies.

Pioneer and administer model retraining, A/B testing, and progressive deployment strategies for continuous model enhancement.

Contribute to the company's machine learning architecture to support scalable and repeatable model training and deployment.

Facilitate the creation of automated processes for model validation and testing.

Qualification

Find out how your skills align with this job's requirements. If anything seems off, you can easily click on the tags to select or unselect skills to reflect your actual expertise.

Machine LearningMLOpsML FrameworksProgramming LanguagesMLOps TechnologiesJob Scheduling FrameworksCloud ServicesContainerization TechnologiesAWSKubernetesSoftware EngineeringAgile DevelopmentCode ReviewsSCMBuild ProcessesTestingOperationsDistributed ComputingBig Data TechnologiesComputer ScienceBig DataData PipelinesData ArchitecturesStructured DataUnstructured DataOptimizationCommunicationCollaboration

Required

Bachelor’s Degree in Computer Science or related technical discipline

At least 7 years of experience working in a machine learning engineer role, with a minimum of 2 years in an MLOps-focused position

Expertise in ML frameworks (e.g., TensorFlow, PyTorch), programming languages (e.g., Python, Scala, Java), MLOps technologies (e.g., Kubeflow, MLflow, AWS Sagemaker), and job scheduling frameworks (e.g., Apache Airflow, AWS Step Functions)

Solid understanding and experience with cloud services and containerization technologies/platforms, particularly AWS and Kubernetes

Proficient with software engineering best practices, including agile development, code reviews, SCM, build processes, testing, and operations

Experience with distributed computing and big data technologies (e.g., Hadoop, Spark)