DescriptionAs an ML Software Developer at Verily you will be supporting our core mission to drive innovation in evidence generation for research and care decisions. We are building new types of longitudinal datasets that have foundations of RWD sources, such as EHRs (electronic health records) and claims data, and are augmented with prospective data collection. You will develop and deploy models that enable scalable curation of RWD. This will include multi-source integrations and reconciliations, creating derived features from the source data (e.g., abstraction of clinical concepts from unstructured data), and facilitating data quality assessments. You will work with a diverse cross-functional team to build reusable and scalable tools, systems, and products that unlock information from multi-modal structured and unstructured healthcare and life science data.
Our team is responsible for integrating and combining data from a variety of sources (e.g. EHR, ePROs, Digital Biomarkers, devices), building ML pipelines in order to provide curated data sets with ML insights that can contribute to clinical and research goals. In addition to research we are building AI tools for our care products to improve patient experience. Be at the forefront of innovation and tackle exciting challenges within a collaborative and dynamic work environment.
Responsibilities- Partner with Data Scientists: Collaborate closely with data scientists to translate research models and prototypes into scalable, production-grade systems.
- Build the Platform: Design, develop, and maintain the core platform components, libraries, and tools that enable our data scientists to efficiently build, train, and deploy models.
- Ensure Production Readiness: Architect and develop high-quality, reliable, and performant AI/ML software solutions that meet project requirements.
- Uphold Engineering Excellence: Participate in code reviews to maintain code quality, mentor junior engineers, and champion software engineering best practices within the team.
- Maintain Operational Stability: Investigate, troubleshoot, and resolve complex technical issues to ensure the reliability and stability of our machine learning systems.
QualificationsMinimum Qualifications
- Bachelor's degree in Computer Science or a related field.
- 2+ years of experience in software engineering, with a focus on cloud or machine learning systems.
- Proficiency in at least one major programming language (e.g., Python, Java, Go).
- Proven track record of delivering high-impact, production-quality software in a cloud environment.
- Direct experience with MLOps principles and tools for model training and deployment (e.g., VertexAI, GKE, notebooks).
Preferred Qualifications
- Experience with CI/CD and Infrastructure as Code tools like Terraform, GitHub.
- Proficiency in Go or Python, especially within the context of building distributed systems.
- Experience in a production environment with machine learning frameworks like TensorFlow or PyTorch.
- Familiarity with data processing technologies and concepts (e.g., Spark, Beam,).
- Experience with Database - Preferably FHIR . Exposure to other databases (such as Dynamo DB, Document DB, Mongo DB) are also desired.
- Data Warehouses - Such as Azure Data Lake, Redshift, Google Big Query.
The Canada base salary range for this full-time position is CAD $120,500-$136,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training.
