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Software Engineer Ai Model Training Jobs in Missouri

Jobgether

This role offers the opportunity to improve the foundations behind large-scale AI model development ... Accountabilities As a Machine Learning Engineer focused on Training Optimization, you will improve ...

Jobgether

$80 - $150/hr

You will work on advanced AI training initiatives by evaluating and improving how models reason about complex software systems. The position combines cloud architecture, platform engineering ...

MasterCard

Senior Software Engineer

O Fallon, MO

$114K - $151K/yr

Title and Summary Senior Software Engineer Who is Mastercard? Mastercard is a global technology ... model training, tuning, and inference workflows Implement and operate AI systems in production ...

Mastercard, Inc.

Senior Software Engineer

O Fallon, MO · On-site

$114K - $151K/yr

... model training, tuning, and inference workflows • Implement and operate AI systems in production, including deployment frameworks, automated pipelines, and model lifecycle management (versioning ...

Epiq

Lead AI Software Engineer

Kansas City, MO · Remote

Epiq is seeking a highly skilled Lead AI Software Engineer to join our Operations Engineering team ... Hands-on experience with GenAI, model training, evaluation, and hyperparameter tuning. * Experience ...

ASCENSUS

Senior Software Engineer

Kansas City, MO

$119K - $157K/yr

AI-powered applications and agents * Retrieval-augmented generation, or RAG, pipelines * Agent ... Model Context Protocol, function calling, or enterprise tool integrations * REST APIs, service ...

Leidos

Senior AI Software Engineer

Saint Louis, MO · On-site

$131K - $237K/yr

The Leidos National Solutions team is seeking a Senior AI Software Engineer to lead advanced ... Ensures AI models, pipelines, and clouddeployed services adhere to governance, compliance, and ...

Relativity

Software Engineer

Kansas City, MO · On-site

This role reports to the Manager of Software Engineering and provides the opportunity to work on ... Experience working with large language model (LLM) APIs or generative AI systems * Experience ...

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Software Engineer Ai Model Training information

What are some common challenges faced by Software Engineers specializing in AI model training, and how can they be addressed?

Software Engineers focusing on AI model training often encounter challenges such as managing large datasets, ensuring data quality, and optimizing model performance. Addressing these issues typically involves close collaboration with data scientists, domain experts, and DevOps engineers to streamline the data pipeline and refine training processes. Staying up to date with the latest advancements in machine learning frameworks and tools can also help overcome technical hurdles. Regular code reviews and cross-functional meetings further support problem-solving and foster a productive work environment.

What are the key skills and qualifications needed to thrive as a Software Engineer in AI Model Training, and why are they important?

To excel as a Software Engineer in AI Model Training, you need strong programming skills (especially in Python), a solid grasp of machine learning fundamentals, and typically a degree in computer science or a related field. Experience with frameworks like TensorFlow or PyTorch, familiarity with data processing tools, and sometimes certifications in AI or ML are highly valuable. Analytical thinking, problem-solving, and effective collaboration enhance your ability to develop and refine complex AI models. These skills ensure that AI solutions are robust, scalable, and aligned with organizational goals in a rapidly evolving technological landscape.

What is the difference between Software Engineer Ai Model Training vs Data Scientist?

AspectSoftware Engineer Ai Model TrainingData Scientist
Required CredentialsBachelor's in CS, related field; experience with ML frameworksBachelor's or higher in CS, statistics, or related field; strong analytical skills
Work EnvironmentDevelopment teams, AI labs, cloud platformsData analysis, research environments, business units
Employer & Industry UsageTech companies, AI startups, research institutionsTech firms, finance, healthcare, consulting

While both roles involve working with data and machine learning, Software Engineer Ai Model Training focuses on developing and optimizing AI models through coding and engineering practices. Data Scientists analyze data, build models, and generate insights. The roles often collaborate but differ in their core responsibilities and skill sets.

What does a Software Engineer in AI Model Training do?

A Software Engineer specializing in AI Model Training is responsible for designing, developing, and optimizing machine learning models. Their work involves preparing and processing large datasets, selecting appropriate algorithms, implementing training pipelines, and evaluating model performance. They collaborate closely with data scientists and other engineers to ensure that AI models are accurate, efficient, and suitable for deployment in real-world applications. Additionally, they may help maintain infrastructure for model training and contribute to research and development of new AI techniques.
What are popular job titles related to Software Engineer Ai Model Training jobs in Missouri? For Software Engineer Ai Model Training jobs in Missouri, the most frequently searched job titles are:
What job categories do people searching Software Engineer Ai Model Training jobs in Missouri look for? The top searched job categories for Software Engineer Ai Model Training jobs in Missouri are:
What cities in Missouri are hiring for Software Engineer Ai Model Training jobs? Cities in Missouri with the most Software Engineer Ai Model Training job openings:
Software Engineer Ai Model Training jobs near you
Infographic showing various Software Engineer Ai Model Training job openings in Missouri as of July 2026, with employment types broken down into 74% Full Time, 23% Part Time, and 3% Contract. Highlights an 66% Physical, 3% Hybrid, and 31% Remote job distribution.

Machine Learning Engineer - Training Optimization

Jobgether

Full-time

Posted 5 days ago

Job description

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Machine Learning Engineer - Training Optimization based in Netherlands.

This role offers the opportunity to improve the foundations behind large-scale AI model development and deployment.
You will work at the intersection of machine learning research, systems engineering, and production optimization.
The position focuses on making model training faster, more stable, and more cost-efficient through advanced engineering techniques.
You will optimize training pipelines, improve distributed systems, and collaborate with researchers to push model capabilities forward.
This is a high-impact opportunity for an engineer who enjoys solving complex performance challenges at scale.
You will have significant ownership in shaping training infrastructure, experimentation workflows, and the future of AI systems.

Accountabilities

As a Machine Learning Engineer focused on Training Optimization, you will improve the efficiency, scalability, and reliability of large-scale model training systems. You will combine deep technical expertise with practical engineering execution to optimize how advanced AI models are developed.

  • Optimize large-scale model training pipelines to improve throughput, convergence, stability, and overall computational efficiency.
  • Improve distributed training approaches, including data parallelism, model parallelism, and pipeline parallelism strategies.
  • Tune key training components such as optimizers, learning rate schedulers, batch sizes, and numerical precision methods including bf16, fp16, and fp8.
  • Identify and resolve performance bottlenecks through profiling, system analysis, and infrastructure-level improvements.
  • Collaborate closely with research teams to develop architecture-aware training strategies and improve model performance.
  • Build and maintain reliable training infrastructure, including checkpointing systems, fault tolerance mechanisms, and reproducible workflows.
  • Evaluate and integrate advanced training techniques such as gradient checkpointing, ZeRO, FSDP, and custom optimization solutions.
  • Define, monitor, and improve training performance metrics to continuously enhance efficiency.
  • Translate research concepts into production-ready systems and scalable engineering solutions.
Requirements

The ideal candidate is a machine learning engineer with strong experience in training large neural networks and optimizing complex AI systems. You should be comfortable working across research and engineering environments while solving challenging scalability and performance problems.

  • Strong experience training large-scale neural networks, including large language models or similarly complex architectures.
  • Hands-on experience with machine learning training optimization, beyond simply using existing models.
  • Strong understanding of backpropagation, optimization algorithms, training dynamics, and model convergence behavior.
  • Experience with distributed machine learning training systems and large-scale computing environments.
  • Proficiency with PyTorch and modern machine learning development workflows.
  • Ability to work close to hardware constraints, including GPU performance, memory limitations, and networking considerations.
  • Strong programming skills with the ability to transform research ideas into reliable production code.
  • Experience with multi-node and multi-GPU training environments is highly preferred.
  • Familiarity with frameworks and technologies such as DeepSpeed, FSDP, Megatron, or custom training stacks is a plus.
  • Experience optimizing workloads on NVIDIA or AMD GPU platforms is beneficial.
  • Contributions to open-source machine learning infrastructure or research projects are valued.
  • Exposure to alternative neural network architectures beyond Transformers is a plus.
Benefits
  • Competitive compensation package with meaningful equity opportunities.
  • Opportunity to work on cutting-edge AI models and large-scale training systems.
  • High ownership role where your contributions directly influence technical direction and company growth.
  • Collaboration with a small, highly technical team focused on engineering excellence and research innovation.
  • Fast feedback loops and an environment that encourages experimentation and impact.
  • Opportunity to solve complex machine learning infrastructure challenges at significant scale.
  • Strong emphasis on technical quality, continuous learning, and advanced AI development.
  • Ability to contribute to foundational systems shaping future AI capabilities.
How Jobgether works:
We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.
We appreciate your interest and wish you the best!
 Why Apply Through Jobgether? 
 
Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.
 
 
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We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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