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Internship Aws Machine Learning Jobs in Missouri

Jobtailor

Software Engineer, ML Platform

California, MO · On-site

$140 - $200/hr

Experience designing and developing machine learning lifecycle infrastructure and platform services ... AWS preferred but not required * Curiosity and experimentation with emerging AI frameworks

New

Jobtailor

Bioinformatics Scientist

California, MO · On-site

$120 - $180/hr

Machine Learning Model Development * Next-Generation Sequencing Data Analysis * Statistical Modeling And Predictive Algorithms * AWS Or GCP Expertise ATS Optimization KeywordsHard Skills * Machine ...

New

Jobtailor

Solutions Architect, AI and ML

California, MO · On-site

$130 - $180/hr

Deep Learning * Machine Learning * TensorFlow * PyTorch * CUDA * Scripting Language (Python) * Programming and Debugging Skills * Cloud Computing (AWS, GCP, Azure) * DevOps/ML Ops Technologies

New

Jobtailor

Distinguished Data Scientist

California, MO · On-site

$180 - $260/hr

Machine Learning Algorithms * Deep Learning with PyTorch * Natural Language Processing * AWS SageMaker and GCP * Python Programming ATS Optimization KeywordsHard Skills * XGBoost * CatBoost

New

Perficient

Lead Data Scientist

Saint Louis, MO · On-site

Experience working within client's machine learning and analytics ecosystem, including AWS and dbt, will significantly accelerate onboarding and enable rapid contribution to key initiatives.

Perficient

Lead Data Scientist

Saint Louis, MO · On-site

Experience working within client's machine learning and analytics ecosystem, including AWS and dbt, will significantly accelerate onboarding and enable rapid contribution to key initiatives.

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Senior Machine Learning Engineer - Foundational ML, AI for Biology, Translation

Jobtailor

California, MO • On-site

$150 - $210/hr

Other

Posted yesterday

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Job description

  • Build, scale, and productionize foundation models and AI agents that support target discovery, experimental design, and lab-in-the-loop pipelines.
  • Design and operate the AgentOps and MLOps backbone for these systems, including experiment tracking, model and agent evaluation, monitoring, and reproducible training and inference workflows.
  • Design, implement, and maintain scalable and reliable ML infrastructure on AWS, and optimize distributed training and inference on high-performance compute.
  • Manage and optimize CI/CD pipelines and Git repositories for ML projects, ensuring efficient version control to support collaboration and deployment.
  • Automate deployment, monitoring, and operational tasks using infrastructure-as-code and orchestration tooling (e.g., Terraform, Helm, Kubernetes).
  • Proactively identify issues and gaps, propose improvements, and champion engineering best practices and code quality across the team.
  • Collaborate closely with interdisciplinary and cross-functional teams across gRED and Roche, and help support research output where relevant, including publications.
Requirements
  • Educational background: BS/MS in Computer Science, Machine Learning, Engineering, or a related quantitative field.
  • Experience: 5+ years of industry experience building and delivering ML systems.
  • Technical skills: Excellent Python programming skills, and proficiency in scripting languages for automation.
  • Solid working knowledge of the theory and practice of deep learning, and hands-on experience with ML frameworks such as PyTorch or JAX.
  • Practical experience building, finetuning, deploying, and scaling foundation models, LLMs, and/or agentic systems in production.
  • Hands-on experience with the MLOps and/or AgentOps lifecycle, including experiment tracking, model and agent evaluation, monitoring, and reproducible training and inference workflows.
  • Proven experience designing, deploying, and managing ML infrastructure on Amazon Web Services (AWS), including services such as EC2, S3, EKS, and SageMaker, along with distributed training and inference on high-performance compute.
  • Strong software and data engineering fundamentals, with a proven track record of owning production CI/CD pipelines, Git-based workflows, automated testing, and documentation.
  • Demonstrated ability to lead technical projects from conception to completion and deliver high-quality, scalable, and reliable software.
  • Excellent problem-solving, communication, and collaboration skills, with the ability to thrive in a fast-paced, user-facing environment.
  • Preferred: Familiarity with agent orchestration frameworks (e.g., LangGraph, LangChain) and common patterns such as tool use, retrieval, and multi-step workflows.
  • Interest or experience in applying ML to scientific discovery (AI for science), such as biology, chemistry, or drug discovery, including working with domain-specific data and models.
Core Competencies

Demonstrates expertise in building and deploying scalable ML systems, with a strong focus on MLOps and AgentOps practices. Proficient in Python programming and AWS infrastructure management, with a solid understanding of deep learning frameworks and CI/CD processes.

Highest-signal resume keywords
  • Python Programming
  • MLOps Lifecycle Management
  • AWS Infrastructure Design
  • CI/CD Pipeline Management
  • Deep Learning Frameworks
ATS Optimization KeywordsHard Skills
  • Machine Learning
  • Deep Learning
  • Foundation Models
  • Model Evaluation
  • Experiment Tracking
  • Distributed Training
  • Automation Scripting
  • Data Engineering
  • Infrastructure-as-Code
  • High-Performance Compute
Soft Skills
  • Problem-Solving
  • Communication
  • Collaboration
  • Project Leadership
  • Adaptability
Industry Keywords
  • AI for Science
  • Biology
  • Chemistry
  • Drug Discovery
  • Quantitative Field
Tools & Technologies
  • AWS EC2
  • AWS S3
  • AWS EKS
  • AWS SageMaker
  • Terraform
  • Helm
  • Kubernetes
  • Git
  • CI/CD Tools
  • ML Frameworks
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