wagey.ggwagey.ggv1.0-68eec7a-3-May
Browse Tech JobsCompaniesFeaturesPricingFAQs
Log InGet Started Free
Jobs/Machine Learning Engineer Role/Veeva Systems - Algorithm Engineer
Veeva Systems

Veeva Systems - Algorithm Engineer

Singapore2mo ago
In OfficeMidAPACCloud ComputingArtificial IntelligenceMachine Learning EngineerAI EngineerSolutions ArchitectPythonMLOpsMLflowAWSKubeflow

Upload My Resume

Drop here or click to browse · Tap to choose · PDF, DOCX, DOC, RTF, TXT

Apply in One Click
Apply in One Click

Requirements

• 2+ years of hands-on experience in a Machine Learning Engineer, Algorithm Engineer, or similar role • Expert-level proficiency in Python, with strong experience in building production-ready ML code • Solid foundation in machine learning concepts, including model training, evaluation, and optimization • Practical experience with deep learning or ML frameworks, such as PyTorch, • TensorFlow, or related libraries (e.g., TRL for reinforcement learning or fine-tuning workflows) • Familiarity with modern MLOps practices, including experiment tracking, model versioning, and deployment, using at least one platform such as MLflow, • Kubeflow, or AWS SageMaker • Strong problem-solving ability and the capacity to work both independently and collaboratively • Strong communication skills, with the ability to explain tech • Experience with cutting-edge AI techniques, such as: Agentic AI / Autonomous Agents, Retrieval-Augmented Generation (RAG), Large Language Models (LLMs) and fine-tuning approaches • Exposure to end-to-end ML systems, including data ingestion, model serving, monitoring, and automated retraining. • Experience working in cloud environments (AWS, GCP, or Azure). • Veeva’s headquarters is located in the San Francisco Bay Area with offices in more than 15 countries around the world.

Responsibilities

• Work within a cross-functional data team to build scalable NLP and ML models • Work from end-to-end on live production pipelines. Not just modeling, not theoretical • Define the best approach to solve problems with ML. Build data and model pipelines • Test, validate, deploy, and monitor solutions for impact • Optimize models for production throughput and uptime requirements • Automate deployments, testing, and monitoring (MLOps)

Get Started Free

No credit card. Takes 10 seconds.

Privacy·Terms··Contact·FAQ·Wagey on X