Zoetis is looking to operationalize AI/ML, scientific modeling, and digital twin workloads in its Veterinary Medicine Research and Development (VMRD) organization
Requirements
- Expert in Python
- Strong experience with a query language such as SQL, MapReduce, and/or Cypher
- Proficiency in one of: C++, Go, Rust, Java, or Scala
- Docker, Kubernetes, CI/CD (e.g., GitHub Actions), secure artifact/container registries
- Data pipeline orchestration (e.g., Databricks, Dagster, Kedro); streaming (Kafka or Redis); data modeling with SQL/NoSQL/graph
- MLOps: experiment tracking and model versioning (e.g., MLflow), model serving and monitoring
- Cloud (AWS/Azure/GCP) and on‑prem/HPC (e.g., Slurm) experience
Responsibilities
- Build end‑to‑end DevOps/MLOps foundations: CI/CD for code/data/models, containerization/orchestration, artifact/registry management, and secure configuration
- Design and operate data engineering pipelines (batch/streaming) with data quality checks, lineage, schema contracts, and governance across lake/warehouse environments
- Productionize scientific and digital twin workflows into services/APIs and lightweight UIs with reproducibility, versioning, auditability, and compliant deployment
- Implement scalable training/inference (batch/real‑time) with observability, SLIs/SLOs, runbooks, incident response, and automated rollback strategies
- Run distributed/HPC jobs (including GPU) and optimize storage, throughput, and cost across on‑prem and cloud; collaborate with scientists on experiment design, data/compute needs, and validation
- Build secure, scalable platforms and data pipelines across cloud and on‑prem/HPC
- Partner closely with biologists and data scientists to translate scientific questions into reliable production systems
Other
- PhD in a quantitative field (computer science, ML, computational biology, applied math) or MS/BS with equivalent senior engineer level experience working in a scientific domain
- 6+ years building production systems; strong software engineering fundamentals
- Experience on multidisciplinary projects and teams, including scientists and software engineers, with excellent communication with scientific stakeholders
- Full‑time, Remote (US)
- Work hours aligned to US Eastern through US Pacific time zones
