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Associate Machine Learning Jobs in Meta, MO (NOW HIRING)

Walmart

Stocking Team Associate

Eldon, MO · On-site

$15 - $28/hr

Ability to operate heavy machinery such as forklifts may also be necessary. Benefits & perks At ... Learning and short-form certificates. Tuition, books, and fees are completely paid for by Walmart.

Walmart

Stocking Team Associate

Osage Beach, MO · On-site

$16 - $29/hr

Ability to operate heavy machinery such as forklifts may also be necessary. Benefits & perks At ... Learning and short-form certificates. Tuition, books, and fees are completely paid for by Walmart.

Walmart

Stocking Team Associate

Jefferson City, MO · On-site

$15 - $28/hr

Ability to operate heavy machinery such as forklifts may also be necessary. Benefits & perks At ... Learning and short-form certificates. Tuition, books, and fees are completely paid for by Walmart.

Walmart

Stocking Team Associate

Jefferson City, MO · On-site

$15 - $28/hr

Ability to operate heavy machinery such as forklifts may also be necessary. Benefits & perks At ... Learning and short-form certificates. Tuition, books, and fees are completely paid for by Walmart.

All JobsAssociate Machine Learning JobsAssociate Machine Learning Jobs in Meta, MO

Associate Machine Learning information

See Meta, MO salary details

$29.8K

$126K

$297.9K

How much do associate machine learning jobs pay per year?

As of Sep 11, 2026, the average yearly pay for associate machine learning in Meta, MO is $126,049.00, according to ZipRecruiter salary data. Most workers in this role earn between $43,600.00 and $191,400.00 per year, depending on experience, location, and employer.

What does an associate machine learning engineer do?

An Associate Machine Learning Engineer assists in designing, developing, and deploying machine learning models under the supervision of senior engineers. They handle tasks such as data preprocessing, model evaluation, and maintaining machine learning pipelines. Associates often collaborate with data scientists, software engineers, and business teams to ensure that machine learning solutions are integrated effectively into products or services. This role is typically entry-level or early career and is a stepping stone toward more advanced machine learning positions.

What are the key skills and qualifications needed to thrive as an associate machine learning engineer?

To thrive as an Associate Machine Learning Engineer, you need a solid background in mathematics, programming (especially Python), and foundational machine learning concepts, usually supported by a relevant degree. Familiarity with tools like TensorFlow, PyTorch, scikit-learn, and experience with data processing libraries and version control systems is typically required. Strong analytical thinking, problem-solving ability, and effective collaboration skills help you stand out in this role. These competencies are essential for developing robust models, working efficiently with teams, and delivering impactful data-driven solutions.

What are some common challenges faced by associate machine learning professionals when transitioning from academic projects to real-world business applications?

Associate Machine Learning professionals often find that moving from academic or theoretical projects to business-focused environments introduces new challenges. Real-world datasets can be messy, incomplete, or imbalanced, requiring additional data cleaning and preprocessing. Moreover, business timelines may require rapid prototyping and iterative model development, which is different from the more open-ended nature of academic research. Collaborating with cross-functional teams such as data engineers, product managers, and business stakeholders is also essential to align models with organizational goals. Adapting to these practical aspects is key to succeeding in an Associate Machine Learning role.

What is the difference between Associate Machine Learning vs Data Scientist?

AspectAssociate Machine LearningData Scientist
Required CredentialsBachelor's degree in CS, Data Science, or related field; some roles may require certifications in ML or AIBachelor's or Master's in CS, Statistics, or related; often requires experience with data analysis and programming
Work EnvironmentEntry-level, team-based projects, focused on supporting ML models and data preprocessingMore autonomous, involved in data analysis, model development, and interpretation
Employer & Industry UsageTech companies, startups, research labs; roles in AI and ML teamsWide range of industries including tech, finance, healthcare, and consulting

While both roles involve working with data and machine learning, an Associate Machine Learning typically focuses on supporting ML projects with less experience, whereas a Data Scientist has broader responsibilities including data analysis, model development, and strategic insights. The roles often overlap but differ in scope and experience level.

Associate Machine Learning jobs near you

Postdoc Research Assoc in Geospatial AI (GeoAI) Forest Health

Jefferson City, MO • On-site

LINCOLN UNIVERSITY
See more jobs at LINCOLN UNIVERSITYexternal

$50K/yr

Other

Posted 8 days ago

Key responsibilities

  • Plan and implement research activities focused on early detection of forest stress, disturbance, and ecological change using geospatial analytics and artificial intelligence.

  • Compile, collect, clean, and process geospatial and ancillary datasets from multiple sources, including satellite imagery, UAV-based LiDAR, multispectral imagery, and environmental data.

  • Develop, train, and optimize GeoAI models using machine learning and deep learning techniques for spatial analysis and predictive modeling.

Job description

Job Summary:

The Postdoctoral Research Associate will engage in research and development of a GeoAI-powered early warning system for forest health by integrating multi-source geospatial data, including satellite imagery, UAV-based LiDAR and multispectral data, and environmental datasets.

This position supports a USDA-NIFA funded project focused on detecting early indicators of forest stress, pest infestation, and environmental disturbances using advanced artificial intelligence and geospatial analytics. The role contributes to research, education, and extension activities in Missouri and supports the broader mission of advancing innovation in geospatial science and environmental monitoring.

ESSENTIAL JOB FUNCTIONS:

  • Plan and implement research activities focused on early detection of forest stress, disturbance, and ecological change using geospatial analytics and artificial intelligence. 
  • Compile, collect, clean, and process geospatial and ancillary datasets from multiple sources, including satellite imagery, UAV-based LiDAR, multispectral imagery, and environmental data. 
  •  Develop, train, and optimize GeoAI models using machine learning and deep learning techniques for spatial analysis and predictive modeling.
  • Validate GeoAI models through field verification and collaboration with the Missouri Ozark Forest Ecosystem Project (MOFEP). 
  • Develop decision-support tools and interfaces that translate complex geospatial outputs into usable information for stakeholders. 
  • Contribute to peer-reviewed publications, conference presentations, and technical documentation required for project deliverables. 
  • Mentor graduate and undergraduate students involved in research activities. 
  • Collaborate with interdisciplinary teams across research, extension, and education initiatives. 
  • Maintain accurate records of research activities, methodologies, and results. 
  • Perform other duties as assigned by the supervisor in support of project goals.

KNOWLEDGE, SKILLS, & ABILITIES:

  • Strong understanding of Geospatial Artificial Intelligence (GeoAI), including integration of machine learning and deep learning methods with geospatial and environmental datasets. 
  • Proficiency in programming languages such as Python or R, including experience with relevant libraries for data analysis, modeling, and visualization. 
  • Knowledge of spatial data processing, geostatistics, and remote sensing techniques. 
  • Familiarity with multi-source data integration and spatial modeling workflows. 
  • Experience working with geospatial software and tools such as GIS platforms, remote sensing tools, and data processing frameworks. 
  • Ability to interpret scientific data and translate findings into actionable insights. 
  • Strong analytical, problem-solving, and critical thinking skills. 
  • Effective written and verbal communication skills for technical and academic audiences. 
  • Ability to work both independently and collaboratively within interdisciplinary research teams. 
  • Strong organizational skills and ability to manage multiple tasks and deadlines.

QUALIFICATIONS:

  • Ph.D. in Geospatial Science, Geography, Remote Sensing, Data Science, Forestry, Environmental Science, or a closely related field.
  • Valid driver's license. 
  • Must have or be able to obtain a Remote Pilot Certificate (FAA Part 107). 
  • Demonstrated experience conducting independent research. 
  • Ability to manage research timelines and deliverables within a grant-funded project.

PREFERRED QUALIFICATIONS:

  • Experience working with UAV or LiDAR data for environmental or forestry applications. 
  • Background in applying machine learning methods to geospatial or ecological datasets. 
  • Demonstrated record of peer-reviewed publications or scientific research dissemination. 
  • Ability to work independently and manage projects with minimal supervision. 
  • Strong organizational and problem-solving skills, particularly when working with large or complex datasets. 
  • Experience collaborating across interdisciplinary teams.

PHYSICAL DEMANDS:

  • Work will be conducted in both office and outdoor field environments.
  • Fieldwork may involve walking in forested terrain and working in variable weather conditions. 
  • Ability to lift and transport equipment weighing up to 40 pounds. 
  • Ability to travel to research sites as needed.

Lincoln University is an Equal Opportunity Employer. Employment decisions are based on qualifications, merit, and institutional needs. Applicants requiring a reasonable accommodation during the application or interview process should contact the Office of Human Resources at 573-681-5018.