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Scientific Machine Learning Jobs in Missouri (NOW HIRING)

BigHat

Principal, Machine Learning Scientist

California, MO · On-site

$254 - $290/hr

Principal, Machine Learning Scientist Department: DS/ML (Data Science/Machine Learning) Employment ... Share your findings at top-tier conferences and publish in leading scientific journals to advance ...

Jobtailor

Machine Learning Scientist II

California, MO · On-site

$120 - $180/hr

... science ecosystem (e.g., pandas, scikit-learn, pySpark), plus proficiency in SQL. Core Competencies Demonstrates expertise in designing and implementing end-to-end machine learning solutions ...

Jobtailor

Machine Learning Engineer, Level 5

California, MO · On-site

$170 - $210/hr

Bachelor's Degree in a relevant technical field such as computer science or equivalent years of practical work experience * 5+ years of post-Bachelor's machine learning experience; or Master's degree ...

Catawiki

So what's the job As a Senior Machine Learning Data Scientist in the Data Team at Catawiki you will focus on delivering scalable and impactful data products. You'll work closely with our product and ...

Varsity Tutors

Machine Learning Tutor

Kansas City, MO · Remote

$18 - $40/hr

... science roles and advanced AI coursework. * Conceptual Teaching & Problem-Solving: Skilled at ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

Varsity Tutors

Machine Learning Tutor

Saint Louis, MO · Remote

$18 - $40/hr

... science roles and advanced AI coursework. * Conceptual Teaching & Problem-Solving: Skilled at ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

Varsity Tutors

Machine Learning Tutor

Columbia, MO · Remote

$18 - $40/hr

... science roles and advanced AI coursework. * Conceptual Teaching & Problem-Solving: Skilled at ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

Jobtailor

Machine Learning Engineer II

California, MO · On-site

$120 - $180/hr

Bachelor's degree in Computer Science, Engineering, Mathematics, or a related technical field, or equivalent practical experience * 2+ years of industry experience in machine learning engineering ...

iHerb

Principal Machine Learning Engineer

California, MO · On-site

$205 - $230/hr

The Machine Learning Engineer will partner closely with Data Scientists, Applied Scientists, and Software Developers to ensure predictive models make business impact. Responsibilities * Partner with ...

Jobtailor

Principal Applied Scientist

California, MO · On-site

$180 - $280/hr

Defines and drives enterprise artificial intelligence and machine learning strategy aligned with ... Mentors data scientists, machine learning engineers, researchers, and technical leaders ...

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Scientific Machine Learning information

What is scientific machine learning?

Scientific machine learning (SciML) is an interdisciplinary field that combines principles from machine learning and scientific computing to solve complex scientific and engineering problems. It involves developing algorithms and models that can learn from data and physical laws, such as differential equations, to make predictions, optimize systems, or gain insights into phenomena. SciML is widely used in areas like physics, biology, climate science, and engineering, enabling researchers to accelerate simulations and make data-driven discoveries. The field often leverages both traditional numerical methods and modern machine learning techniques, making it a rapidly evolving area of research.

What are the key skills and qualifications needed to thrive as a scientific machine learning professional, and why are they important?

To thrive as a Scientific Machine Learning professional, you need a strong background in mathematics, statistics, programming (often Python), and domain-specific scientific knowledge, typically with a graduate degree in a STEM field. Proficiency in machine learning frameworks (such as TensorFlow or PyTorch), scientific computing tools (like NumPy, SciPy), and experience with high-performance computing are commonly required. Critical thinking, problem-solving, and collaborative communication are vital soft skills for designing experiments and interpreting complex data. These skills ensure robust, reproducible results and the ability to bridge scientific inquiry with advanced computational methods.

What are some common challenges faced by professionals in scientific machine learning, and how can they be addressed?

Professionals in Scientific Machine Learning often encounter challenges such as integrating domain-specific scientific knowledge with machine learning models, managing large and complex datasets, and ensuring that models are interpretable and physically consistent. Collaboration with domain experts and interdisciplinary teams is essential to bridge knowledge gaps and validate results. To address these challenges, it is helpful to invest time in understanding the underlying scientific principles, keep up-to-date with advancements in both machine learning and scientific fields, and utilize specialized tools and frameworks designed for scientific data.

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

AspectScientific Machine LearningData Scientist
Required credentialsAdvanced degrees in CS, ML, or related fields; knowledge of scientific computingDegree in CS, statistics, or related fields; strong analytical skills
Work environmentResearch labs, academia, industry R&D teamsBusiness analytics, tech companies, consulting firms
Industry usageResearch, scientific computing, engineering simulationsBusiness insights, predictive modeling, data analysis

Scientific Machine Learning focuses on integrating scientific knowledge with machine learning techniques for research and engineering applications. Data Scientists analyze data to extract insights and build predictive models for business or operational purposes. While both roles require strong technical skills, Scientific Machine Learning emphasizes scientific computing and domain-specific modeling, whereas Data Scientists focus on data analysis and visualization.

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

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

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

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

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

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

Scientific Machine Learning jobs near you
Infographic showing various Scientific Machine Learning job openings in Missouri as of August 2026, with employment types broken down into 1% As Needed, 75% Full Time, 22% Part Time, and 2% Contract. Highlights an 86% Physical, 2% Hybrid, and 12% Remote job distribution.

Principal Machine Learning Scientist - Agentic Experiences

California, MO • On-site

Jobtailor
See more jobs at Jobtailorexternal

$150 - $190/hr

Other

Posted 12 days ago

Job description


  • Design, build, and evaluate multi-step agentic AI systems with planning, tool use, memory management, and multi-agent collaboration

  • Research and implement agentic architecture techniques including ReAct, reflection loops, chain-of-thought prompting, and tool-augmented reasoning

  • Develop and maintain agent orchestration frameworks for task decomposition, delegation, failure handling, and recovery

  • Integrate large language models with external tools, APIs, databases, and code execution environments

  • Define and own evaluation frameworks measuring task success, reliability, latency, cost, and safety

  • Collaborate with product, engineering, and research teams to translate business requirements into production-grade agentic solutions

  • Identify and mitigate agentic-system risks such as prompt injection, unintended actions, hallucinations, and unsafe tool use

  • Synthesize academic research and industry developments to inform technical direction

  • Mentor senior ML engineers and scientists on agentic design patterns, LLM practices, and experimentation methodology


Requirements

  • BS in Computer Science, Machine Learning, Statistics, Engineering, or a related field, or equivalent professional experience

  • 10+ years of related industry experience

  • Experience designing and deploying agentic or multi-step AI systems in production or research settings

  • Strong proficiency in Python and ML frameworks such as PyTorch, TensorFlow, or JAX

  • Experience with LLM APIs and orchestration libraries such as LangChain or LlamaIndex

  • Experience integrating LLMs with external tools, APIs, and structured data sources

  • Understanding of prompt engineering, including chain-of-thought, few-shot prompting, and structured output generation

  • Experience defining and running ML-system evaluation frameworks, including offline benchmarking and production monitoring

  • Experience taking models from prototype to production with Machine Learning Engineering teams

  • Advanced degree in Computer Science, Machine Learning, Statistics, or Engineering is preferred

  • Experience in travel or e-commerce is preferred

  • Publications in top-tier ML conferences or journals are preferred

  • Patented inventions are preferred

  • Contributions to open-source ML projects are preferred


Core Competencies

Demonstrates expertise in designing and deploying agentic AI systems, integrating large language models with external tools, and defining evaluation frameworks for performance metrics. Proven ability to mentor teams and translate complex business requirements into effective AI solutions.


Highest-signal resume keywords

  • Agentic AI System Design

  • Python Proficiency

  • LLM API Integration

  • ML System Evaluation Frameworks

  • Mentoring ML Engineers


ATS Optimization Keywords
Hard Skills

  • Machine Learning

  • Agentic Architecture Techniques

  • Prompt Engineering

  • Task Decomposition

  • Failure Handling

  • Tool-Augmented Reasoning

  • Multi-Agent Collaboration

  • Production Monitoring

  • Benchmarking

  • Data Structures


Soft Skills

  • Collaboration

  • Mentoring

  • Research Synthesis

  • Problem Solving

  • Communication


Industry Keywords

  • E-Commerce

  • Travel

  • Machine Learning Engineering

  • Open-Source Contributions

  • Publications in ML Conferences


Tools & Technologies

  • PyTorch

  • TensorFlow

  • JAX

  • LangChain

  • LlamaIndex

  • APIs

  • Databases

  • Code Execution Environments

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