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Ml Inference Jobs in Missouri (NOW HIRING)

Evaluate and adopt emerging tools in the modern data and ML stack Data Engineering Development ... inference serving * Contribute to MLOps practices including model versioning and monitoring ...

Evaluate and adopt emerging tools in the modern data and ML stack Data Engineering Development ... inference serving * Contribute to MLOps practices including model versioning and monitoring ...

Principal, Data Scientist

Anderson, MO · On-site

$110K - $220K/yr

Architect end‑to‑end ML systems--from feature engineering through production deployment and ... Engineer computer vision systems for real‑time inference (YOLO, RT‑DETR, CLIP) with multi‑GPU ...

Principal, Data Scientist

Cassville, MO · On-site

$110K - $220K/yr

Architect end‑to‑end ML systems--from feature engineering through production deployment and ... Engineer computer vision systems for real‑time inference (YOLO, RT‑DETR, CLIP) with multi‑GPU ...

Principal, Data Scientist

Noel, MO · On-site

$110K - $220K/yr

Architect end‑to‑end ML systems--from feature engineering through production deployment and ... Engineer computer vision systems for real‑time inference (YOLO, RT‑DETR, CLIP) with multi‑GPU ...

$46.25 - $60.25/hr

Experience self-hosing ML inference * Hands-on experience with Vertex AI, Kubeflow, or TensorFlow Serving in production * Background in event-driven architectures and message streaming (e.g., Pub/Sub ...

Senior Product Manager

Kansas City, MO · On-site

$145K - $160K/yr

Partner with ML and data science on extraction and GL-inference model performance - framing the problems, defining evaluation metrics, and prioritizing the data and feedback loops that improve them ...

Senior Product Manager

Kansas City, MO · On-site

$145K - $160K/yr

Partner with ML and data science on extraction and GL-inference model performance -- framing the problems, defining evaluation metrics, and prioritizing the data and feedback loops that improve them ...

Apply Early

Senior Product Manager

Kansas City, MO · On-site +1

$145K - $160K/yr

Partner with ML and data science on extraction and GL-inference model performance - framing the problems, defining evaluation metrics, and prioritizing the data and feedback loops that improve them ...

Google AI Lead Architect

Saint Louis, MO

$53.75 - $73.75/hr

Integrate and fine-tune Large Language Models (LLMs) and other AI/ML models into enterprise applications. Develop and implement strategies for model deployment, inference, and monitoring, with an ...

Integrate and fine-tune Large Language Models (LLMs) and other AI/ML models into enterprise applications. Develop and implement strategies for model deployment, inference, and monitoring, with an ...

$50.25 - $63.75/hr

... AI/ML services such as Amazon Bedrock and SageMaker. * Proven experience deploying and scaling GenAI workloads, including LLM fine-tuning, inference optimization, and agent-based architectures.

AI/ML Technical Leadership * Provide hands-on technical guidance and thought leadership to multi ... Experience with GenAI, prompt engineering patterns, or large-scale inference infrastructure.

Distinguished, Software Engineer

Noel, MO · On-site

$130K - $260K/yr

AI/ML Technical Leadership * Provide hands-on technical guidance and thought leadership to multi ... Experience with GenAI, prompt engineering patterns, or large-scale inference infrastructure.

AI/ML Technical Leadership * Provide hands-on technical guidance and thought leadership to multi ... Experience with GenAI, prompt engineering patterns, or large-scale inference infrastructure.

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Showing results 1-20

Ml Inference information

What is a $900000 AI job?

A $900,000 AI job typically refers to high-level roles in artificial intelligence, such as senior machine learning engineers or AI research directors, often involving advanced skills in deep learning, data modeling, and programming with tools like Python and TensorFlow. These positions usually require extensive experience, specialized knowledge, and may include leadership responsibilities or strategic decision-making.

What is ML inference?

ML inference refers to the process of using a trained machine learning model to make predictions or decisions based on new data. After a model has been trained on historical data, inference is the phase where that model is deployed and used in real-world applications, such as recognizing speech, detecting objects in images, or recommending products. The focus in ML inference is on speed, efficiency, and scalability to ensure quick predictions, often in real time. This process is critical for practical applications like mobile apps, web services, and embedded systems. Optimizing inference involves reducing latency, memory usage, and computational requirements.

What is the difference between Ml Inference vs Data Scientist?

AspectML InferenceData Scientist
Required CredentialsKnowledge of machine learning models, programming skillsDegree in data science, statistics, or related fields
Work EnvironmentDeploying models in production, real-time data processingData analysis, model development, research
Industry UsageAI product deployment, software companiesResearch institutions, tech firms, consulting

ML Inference focuses on deploying trained models to make predictions on new data, often in real-time. Data Scientists develop and analyze models, working primarily in research and development. While both roles require understanding of machine learning, ML Inference emphasizes deployment and operationalization, whereas Data Scientists focus on model creation and analysis.

What engineer makes $500,000 a year?

Senior machine learning engineers with extensive experience, advanced skills in deep learning, and expertise in deploying large-scale models can earn salaries approaching or exceeding $500,000 annually, especially in high-cost-of-living areas or top tech companies. Compensation often includes base salary, bonuses, and stock options, reflecting their specialized knowledge and impact on product development.

Which 3 jobs will survive AI?

Jobs involving Ml Inference, such as data scientists, machine learning engineers, and AI system architects, are likely to persist as they require specialized expertise in developing, deploying, and maintaining AI models. These roles demand critical thinking, domain knowledge, and skills in programming and data analysis that are less easily automated. Continuous learning and staying updated with AI tools and frameworks are essential for these professions to remain relevant.

What are some common challenges faced by ML Inference Engineers when deploying models to production?

ML Inference Engineers often encounter challenges such as optimizing model latency and throughput to meet production requirements, ensuring compatibility with diverse hardware environments, and managing model versioning and updates without disrupting service. Additionally, balancing resource utilization and inference accuracy while monitoring real-time performance metrics is crucial. Collaboration with data scientists, DevOps, and software engineers is typically essential to streamline deployment and maintain robust, scalable inference pipelines.

Will MLE be replaced by AI?

Machine Learning Engineers (MLEs) design, develop, and optimize AI models and systems. While AI automation tools can assist with certain tasks, MLEs are essential for building, tuning, and maintaining complex models, making complete replacement unlikely in the near term. Their expertise in data handling, model deployment, and system integration remains critical in AI development environments.

What are the key skills and qualifications needed to thrive in ML Inference, and why are they important?

To thrive in ML Inference, you need a solid background in machine learning principles, programming (Python or C++), and experience with deploying models at scale, often supported by a degree in computer science or a related field. Familiarity with frameworks and tools such as TensorFlow, PyTorch, ONNX, and cloud platforms like AWS SageMaker or Google AI Platform is typically required. Strong problem-solving skills, attention to detail, and effective communication are crucial soft skills for collaborating with multidisciplinary teams and optimizing model performance. These skills ensure efficient, scalable, and reliable deployment of machine learning solutions in real-world applications.
What are popular job titles related to Ml Inference jobs in Missouri? For Ml Inference jobs in Missouri, the most frequently searched job titles are:
What cities in Missouri are hiring for Ml Inference jobs? Cities in Missouri with the most Ml Inference job openings:
Infographic showing various Ml Inference job openings in Missouri as of June 2026, with employment types broken down into 95% Full Time, 4% Part Time, and 1% Contract. Highlights an 82% Physical, 5% Hybrid, and 13% Remote job distribution.
Vice President, Software Engineering - DMP - Mastercard

Vice President, Software Engineering - DMP - Mastercard

Mastercard

O Fallon, MO • On-site

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Posted 8 hours ago


Job description

Our Purpose

Mastercard powers economies and empowers people in 200+ countries and territories worldwide. Together with our customers, we’re helping build a sustainable economy where everyone can prosper. We support a wide range of digital payments choices, making transactions secure, simple, smart and accessible. Our technology and innovation, partnerships and networks combine to deliver a unique set of products and services that help people, businesses and governments realize their greatest potential.

Title and Summary

Vice President, Software Engineering - DMP Overview:
Mastercard is seeking a Vice President, Software Engineering to lead the Authorization Decisioning domain within the Decision Management program. This role is accountable for advancing a portfolio of market-facing products by leading and scaling high-performing teams of Software Engineers, Product Managers, and Program Managers.
As a senior technology leader, you will operate at the intersection of engineering excellence, product innovation, and cross-functional collaboration. Success in this role requires strong organizational influence, the ability to navigate complex stakeholder landscapes, and a deep sense of empathy for customer and partner needs.
Decision Management enables faster, smarter decisioning at global scale by structuring and applying complex business logic across the payment journey and beyond. Authorization Decisioning orchestrates high throughput, low latency, and data-intensive processing across Decision Management’s intelligent system on behalf of a suite of market-facing products.
This is a hybrid position based in O’Fallon, MO, requiring three days per week onsite.
Role:
• Define and drive the engineering vision and strategy for Authorization and Authentication Decisioning.
• Lead the delivery of complex, cross-functional initiatives spanning real-time decisioning, rules engines, AI/ML inference, data pipelines, and platform services.
• Establish and scale engineering best practices, standards, and frameworks across teams.
• Ensure platform reliability, performance, scalability, security, and compliance in line with the demands of global, mission-critical systems.
• Own operational excellence, including SLAs, observability, and incident management.
• Build, lead, and develop a high-performing organization of engineering managers and senior technical leaders.
• Set clear goals, performance expectations, and career development plans aligned with Mastercard leadership principles.
• Act as a multiplier by scaling impact through leaders, systems, and culture.
• Balance near-term delivery commitments with long-term platform evolution and modernization.
• Champion strong engineering judgment, operational discipline, and customer-centric thinking.
• Model Mastercard leadership behaviors, fostering a culture of inclusion, ownership, and continuous improvement.
All About You:
• Proven experience as a Director or Vice President of Software Engineering, Architecture, or a comparable senior leadership role.
• Demonstrated success leading distributed, global engineering organizations.
• Deep expertise in modern application architectures, including APIs, microservices, event-driven systems, batch processing, and data platforms.
• Strong hands-on knowledge of technologies such as Java, REST APIs, Kafka, messaging systems (MQ), Spring, CI/CD pipelines (e.g., Jenkins), and cloud platforms (e.g., Pivotal Cloud Foundry or similar).
• Proven track record of delivering high-scale, low-latency, highly available platforms in regulated or mission-critical environments.
• Experience leading large, complex programs with predictable, on-time, and on-budget delivery.
• Strong understanding of SDLC methodologies (Scrum, Kanban, SAFe) and when to apply them effectively.
• Expertise in building and operating resilient systems with a focus on security, reliability, testing, observability, and service-oriented design.
• Excellent communication and storytelling skills, with the ability to influence executive, business, and technical stakeholders.
• Strong analytical thinking and decision-making capabilities in ambiguous and complex environments.
• Bachelor’s degree in Engineering, Computer Science, Mathematics, or a related quantitative field, or equivalent practical experience. Mastercard is a merit-based, inclusive, equal opportunity employer that considers applicants without regard to gender, gender identity, sexual orientation, race, ethnicity, disabled or veteran status, or any other characteristic protected by law. We hire the most qualified candidate for the role. In the US or Canada, if you require accommodations or assistance to complete the online application process or during the recruitment process, please contact reasonable_accommodation@mastercard.com and identify the type of accommodation or assistance you are requesting. Do not include any medical or health information in this email. The Reasonable Accommodations team will respond to your email promptly.

Corporate Security Responsibility


All activities involving access to Mastercard assets, information, and networks comes with an inherent risk to the organization and, therefore, it is expected that every person working for, or on behalf of, Mastercard is responsible for information security and must:

  • Abide by Mastercard’s security policies and practices;

  • Ensure the confidentiality and integrity of the information being accessed;

  • Report any suspected information security violation or breach, and

  • Complete all periodic mandatory security trainings in accordance with Mastercard’s guidelines.

In line with Mastercard’s total compensation philosophy and assuming that the job will be performed in the US, the successful candidate will be offered a competitive base salary and may be eligible for an annual bonus or commissions depending on the role. The base salary offered may vary depending on multiple factors, including but not limited to location, job-related knowledge, skills, and experience. Mastercard benefits for full time (and certain part time) employees generally include: insurance (including medical, prescription drug, dental, vision, disability, life insurance); flexible spending account and health savings account; paid leaves (including 16 weeks of new parent leave and up to 20 days of bereavement leave); 80 hours of Paid Sick and Safe Time, 25 days of vacation time and 5 personal days, pro-rated based on date of hire; 10 annual paid U.S. observed holidays; 401k with a best-in-class company match; deferred compensation for eligible roles; fitness reimbursement or on-site fitness facilities; eligibility for tuition reimbursement; and many more. Mastercard benefits for interns generally include: 56 hours of Paid Sick and Safe Time; jury duty leave; and on-site fitness facilities in some locations.

Pay Ranges

O‘Fallon, Missouri: $212,000 - $339,000 USD