AI/ML Engineer, Precision Oncology

Jobtailor; Tampa (FL); On-site; USD 120,000 - 180,000; Full time

Job summary

Jobtailor is hiring for an AI/ML systems engineer to design, develop, deploy, and maintain scalable AI platforms for precision oncology. You will integrate multimodal clinical data, build training and inference workflows, and implement MLOps across cloud/HPC environments.

The role requires strong Python expertise, experience with PyTorch/Hugging Face, and a track record of building production-grade AI software.

Qualifications

• Master’s degree in Computer Science, Computer Engineering, or related quantitative field;Bachelor’s considered with experience
• Three (3) years of professional experience in ML systems, data platforms, or AI software
• Strong Python software engineering and programming skills
• Experience with PyTorch or Hugging Face for ML/DL apps
• Experience building large-scale data processing pipelines in cloud/HPC/GPU environments
• Experience with MLOps, Git, containerization, and production-grade AI software
• Preferred: experience with foundation models, LLMs, and multimodal AI systems
• Preferred: experience integrating clinical, imaging, pathology, molecular, genomic data into AI
• Preferred: cloud-native AI platforms, CI/CD, workflow orchestration, and AI governance
• Preferred: vector databases, semantic search, RAG, retrieval systems, or agentic AI frameworks

Responsibilities

• Design and deploy scalable AI/ML systems for precision oncology
• Build and manage AI platforms integrating multimodal clinical and research data
• Develop scalable data pipelines, model-training workflows, and inference services
• Implement MLOps practices including versioning, experiment tracking, deployment, monitoring, governance
• Develop foundation models, generative AI, LLMs, and vision-language models
• Create APIs and services integrating AI into research, clinical, and operations
• Evaluate emerging AI technologies and frameworks for institutional use
• Collaborate with scientists and clinicians to translate AI into scalable solutions
• Perform related duties as assigned by leadership

Skills

ML System Development

MLOps Implementation

Python Programming

Data Pipeline Engineering

AI Application Development

Education

Master's degree in CS/related field

Tools

PyTorch

Hugging Face

Git

Containerization

Cloud Computing

HPC

GPU

CI/CD

Vector Databases

Workflow Orchestration

Job description

• Design, develop, deploy, and maintain scalable AI and machine learning systems for precision oncology applications
• Build and manage AI platforms integrating multimodal clinical and research datasets
• Develop scalable data pipelines, model-training workflows, inference services, and software infrastructure supporting AI initiatives
• Implement MLOps best practices, including model versioning, experiment tracking, deployment, monitoring, and governance
• Develop and optimize foundation models, generative AI solutions, large language models, vision-language models, and other AI applications
• Create APIs, software services, and user-facing applications integrating AI capabilities into research, clinical, and operational environments
• Evaluate and implement emerging AI technologies, engineering frameworks, and best practices supporting institution-wide AI innovation
• Collaborate with scientists, clinicians, and technical teams to translate novel AI methodologies into scalable solutions
• Perform other related duties as assigned by appropriate leadership

Requirements

• Master's degree in Computer Science, Computer Engineering, Electrical Engineering, Data Science, Artificial Intelligence, Biomedical Engineering, Informatics, or a related quantitative discipline; Bachelor's degree may be considered with additional relevant experience
• Three (3) years of professional experience developing and deploying machine learning systems, data platforms, or AI-enabled software solutions; relevant experience may be gained through master's degree research
• Strong software engineering and programming skills in Python
• Experience developing and deploying machine learning and deep learning applications using PyTorch, Hugging Face, or similar frameworks
• Experience building large-scale data processing pipelines and supporting AI systems in cloud, HPC, GPU, or distributed computing environments
• Experience with MLOps, Git, containerization technologies, and production-grade AI software development
• Preferred: experience with foundation models, LLMs, vision-language models (VLMs), multimodal AI systems, or generative AI applications
• Preferred: experience integrating clinical, imaging, pathology, molecular, genomic, and outcomes data into AI solutions
• Preferred: experience with cloud-native AI platforms and services
• Preferred: experience with CI/CD pipelines, workflow orchestration, infrastructure automation, and AI governance best practices
• Preferred: experience with vector databases, semantic search, retrieval-augmented generation (RAG), retrieval systems, or agentic AI frameworks

Core Competencies

Demonstrates expertise in designing and deploying scalable AI and machine learning systems, with a strong focus on MLOps best practices and integration of multimodal datasets. Proficient in developing data pipelines and AI applications that enhance precision oncology and clinical research.

Highest-signal resume keywords

• Machine Learning System Development
• MLOps Implementation
• Python Programming
• Data Pipeline Engineering
• AI Application Development

Hard Skills

• Machine Learning
• Deep Learning
• Data Processing Pipelines
• AI Software Development
• Model Training Workflows
• Generative AI Solutions
• Large Language Models
• Vision-Language Models
• APIs Development
• Experiment Tracking

Soft Skills

• Collaboration
• Communication

Industry Keywords

• Precision Oncology
• Multimodal AI Systems
• AI Governance
• Emerging AI TechnologiesClinical Data Integration

Tools & Technologies

• PyTorch
• Hugging Face
• Git
• Containerization Technologies
• Cloud Computing
• HPC
• GPU
• CI/CD Pipelines
• Vector Databases
• Workflow Orchestration