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Machine Learning Manager Jobs in Missouri (NOW HIRING)

Jobtailor

Principal Applied Scientist

California, MO · On-site

$180 - $280/hr

Experience with Python, machine learning frameworks, cloud-based artificial intelligence, or machine learning platforms, MLOps, model deployment, monitoring, lifecycle management, and structured and ...

Jobtailor

Platform Engineering Manager

California, MO · On-site

$180 - $240/hr

Manage both senior IC work and people management. Requirements * 7+ years of professional ... Experience with machine learning concepts and technology is a plus. * You are a lifelong learner ...

New

Jobtailor

Bioinformatics Scientist

California, MO · On-site

$120 - $180/hr

Develop and refine machine learning models for cell-free circulating tumor DNA fraction estimation ... Expertise with AWS or GCP, Docker, and workflow management tools such as Nextflow or Snakemake

New

Jobgether

$225K - $250K/yr

This position is listed on behalf of a partner company, who manages all applications and next steps ... The focus is on interpretable machine learning, statistical modeling, optimization, uncertainty ...

Kohl's

$95K - $131K/yr

Manage and communicate cloud infrastructure costs and budgets to project stakeholders * Stay current with GCP services and evolving best practices in Machine Learning Engineering and MLOps

Third-Party Job Posts

Ability to apply statistical and ML methods to optimize revenue management and pricing strategies. What Sets You Up for Success: * 5+ years of experience in a machine learning role, with demonstrated ...

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Machine Learning Manager information

See Missouri salary details

$47.8K

$76.6K

$110.7K

How much do machine learning manager jobs pay per year?

As of Aug 20, 2026, the average yearly pay for machine learning manager in Missouri is $76,643.00, according to ZipRecruiter salary data. Most workers in this role earn between $61,900.00 and $86,800.00 per year, depending on experience, location, and employer.

What is a machine learning manager?

Machine Learning Managers are professionals responsible for leading teams that develop, implement, and maintain machine learning models and systems. They oversee data scientists, engineers, and other specialists, ensuring projects align with business goals and are delivered on time. Their role often involves coordinating cross-functional teams, managing project timelines, and staying current with the latest advancements in artificial intelligence and machine learning. Additionally, they may be involved in hiring, mentoring, and providing technical guidance to their team.

What are the key skills and qualifications needed to thrive as a machine learning manager?

To thrive as a Machine Learning Manager, you need a robust background in machine learning algorithms, statistical analysis, and software engineering, typically supported by an advanced degree in computer science or a related field. Familiarity with tools such as Python, TensorFlow, PyTorch, and project management platforms, along with experience in deploying ML systems, is essential. Strong leadership, communication, and strategic thinking skills set exceptional managers apart, enabling them to guide teams and align projects with business objectives. These skills are crucial to successfully leading technical teams, ensuring project delivery, and translating complex ML solutions into organizational value.

What are some of the main challenges a machine learning manager faces when leading a team?

A Machine Learning Manager often navigates challenges such as balancing project deadlines with the need for thorough experimentation and research, ensuring clear communication between technical and non-technical stakeholders, and fostering collaboration among data scientists, engineers, and product teams. Additionally, managers must keep their team's skills current with rapidly evolving technologies while also addressing issues like data quality and model deployment in production environments. Successfully overcoming these challenges requires strong leadership, adaptability, and a deep understanding of both business objectives and technical intricacies.

Is machine learning a high paying job?

Machine Learning Managers typically earn high salaries due to their specialized skills in data analysis, programming, and model development. Compensation varies based on experience, location, and industry, but it is generally considered a well-paying role within the tech sector.

What are the most commonly searched types of Machine Learning jobs in Missouri?

The most popular types of Machine Learning jobs in Missouri are:

What are popular job titles related to Machine Learning Manager jobs in Missouri?

For Machine Learning Manager jobs in Missouri, the most frequently searched job titles are:

What job categories do people searching Machine Learning Manager jobs in Missouri look for?

The top searched job categories for Machine Learning Manager jobs in Missouri are:

What cities in Missouri are hiring for Machine Learning Manager jobs?

Cities in Missouri with the most Machine Learning Manager job openings:

Machine Learning Manager jobs near you
Infographic showing various Machine Learning Manager job openings in Missouri as of August 2026, with employment types broken down into 90% Full Time, 9% Part Time, and 1% Contract. Highlights an 84% Physical, 3% Hybrid, and 13% Remote job distribution, with an average salary of $76,643 per year, or $36.8 per hour.

Machine Learning Engineering Director

Jobtailor

California, MO • On-site

$180 - $270/hr

Other

Posted 14 days ago

Job description

Responsibilities
  • Lead the vision, architecture, roadmap, and provide technical leadership across multiple engineering teams; develop engineering managers and senior engineers while establishing standards, guardrails, and governance models.
  • Own cloud‑native platform design and delivery across Azure and AWS, ensuring scalability, reliability, performance, and cost optimization.
  • Ensure alignment with enterprise requirements for security, data privacy, compliance, and Responsible AI practices.
  • Drive cross‑functional collaboration with product, risk, security, and business stakeholders to enable secure and scalable AI adoption.
  • Maintain strong awareness of team delivery, technical issues, and platform risks; step in to review designs, code, and architectural decisions as needed.
  • Ensure Agile practices are consistently followed, including sprint planning, backlog refinement, and delivery tracking.
  • Enforce operational rigor across teams, including keeping Jira artifacts current, transparent, and aligned to priorities.
  • Provide ongoing feedback on team performance, resource allocation, and organizational effectiveness to optimize delivery outcomes.
  • Manage core people leadership responsibilities, including addressing HR‑related matters, supporting employee development, and ensuring team engagement.
  • Ensure continuity of delivery through appropriate backup coverage, resource planning, and support during team member absences.
  • Oversee administrative responsibilities such as time tracking and compliance with enterprise processes (e.g., timesheets, reporting).
Qualifications
  • Bachelor's degree, or equivalent work experience.
  • 10 or more years of relevant software engineering experience.
  • Six or more years of experience leading multiple software engineering teams.
  • Minimum 5 years of experience managing engineering teams, including direct oversight of engineering managers and senior technical staff.
  • 10+ years of experience in software engineering, platform engineering, or related disciplines.
  • Proven experience designing and delivering large‑scale, distributed systems and cloud‑native platforms (Azure and/or AWS).
  • Working knowledge of AI/ML and GenAI concepts, including familiarity with LLMs, RAG architectures, and AI application integration (deep specialization not required).
  • Familiarity with MLOps practices, including CI/CD concepts for models, monitoring, and lifecycle considerations.
  • Experience implementing enterprise‑grade security, compliance, and data governance frameworks.
  • Demonstrated ability to influence and partner with senior technology and business leadership.
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