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Ml Inference Jobs in Missouri (NOW HIRING)

Lead Engineer AI/ML - Onsite

Springfield, MO ยท On-site

$93K - $122K/yr

This role is responsible for developing production-ready model code, inference logic, and reusable ML components that convert approved enterprise data into reliable operational signals ...

$79K - $104K/yr

The role spans managed AWS machine learning services, open-source ML tooling, model serving, inference pipelines, and production integrations. You will also drive the engineering strategy for GenAI ...

Experience developing AI and ML algorithms or technologies (e.g. LLM Inference, Similarity Search and VectorDBs, Guardrails, Memory) using Python, C++, C#, Java, or Golang * Experience developing and ...

Machine Learning Scientist II

California, MO ยท On-site

$120 - $180/hr

... causal inference to accurately measure impact. * Able to design end-to-end ML solutions: framing the problem, choosing data sources, selecting algorithms, and defining evaluation strategy.

Production experience with LLM-centric services, including inference, orchestration, evaluation, and monitoring * Familiarity with large-scale ML experimentation, benchmarking, or simulation ...

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Ml Inference information

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 are the key skills and qualifications needed to thrive in ML inference?

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 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.

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 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 August 2026, with employment types broken down into 94% Full Time, 2% Part Time, and 4% Contract. Highlights an 79% Physical, 6% Hybrid, and 15% Remote job distribution.

Senior ML Inference Engineer - Platform

California, MO โ€ข On-site

$140 - $190/hr

Other

Posted 9 days ago


Job description

Responsibilities
  • Design, build, and operate the ML deployment platform that automates the path from trained model to on-vehicle inference.
  • Drive cross-organization model deployments to the autonomous vehicle stack, partnering with model development teams to take high-value models from training to production on-vehicle.
  • Build agentic tools that diagnose and fix deployment-blocking issues, automating workflows currently performed manually by engineers.
  • Build the developer experience that ML model development teams use day to day: tooling, dashboards, automation, and observability.
  • Drive shift-left validation that surfaces deployment risk (compile, runtime, parity, latency) early in the model development cycle.
  • Build platform tools that integrate the work of our sister teams (kernels, compiler, reduced precision and parity) so their optimization wins land directly in the deployment workflow.
  • Partner with the team's Performance pillar and model development teams across the AV organization.
Requirements
  • BS, MS, or PhD in Computer Science or a related technical field.
  • 3+ years of relevant industry experience.
  • Strong fundamentals and excellent coding ability in Python.
  • Experience building or operating production platform or infrastructure systems where reliability, observability, and extensibility matter.
  • Experience with ML model deployment, inference integration, model optimization workflows, or model serving infrastructure, with at least one prior context where you owned the path from a trained model to a running inference workload.
  • Experience using coding agents (Cursor, Claude Code, GitHub Copilot, or equivalent) as part of your engineering workflow.
  • Experience designing clean, well-tested software with clear interfaces and good abstractions.
  • Strong cross-team collaboration skills.
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