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Ml Inference Jobs in Missouri (NOW HIRING)

Jobgether

$88K - $106K/yr

You will combine deep ML expertise, systems engineering, and performance analysis to deliver faster, more efficient AI experiences. * Optimize machine learning inference systems to improve latency ...

Brain Corp

$83K - $113K/yr

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 ...

Jobgether

$94K - $124K/yr

Experience with ML inference technologies such as vLLM, TensorRT-LLM, Triton Inference Server, SGLang, or similar platforms is a plus. * Knowledge of runtime optimization techniques including cold ...

Deloitte

AI Engineer Consultant

Kansas City, MO · Hybrid

Deliver governed datasets and feature engineering/serving for ML training and real-time inference (online/offline consistency, caching, latency SLOs, backfills). A successful candidate would possess ...

Deloitte

AI Engineer Consultant

Saint Louis, MO · Hybrid

Deliver governed datasets and feature engineering/serving for ML training and real-time inference (online/offline consistency, caching, latency SLOs, backfills). A successful candidate would possess ...

Walmart

Staff, Data Scientist (Pricing)

Noel, MO · On-site

$110K - $220K/yr

Causal Inference & Elasticity: Identification of treatment effects beyond simple log-log approaches (Double ML, Instrumental Variables, Uplift modeling); Optimization & Reinforcement Learning: Multi ...

New

Walmart

Staff, Data Scientist (Pricing)

Cassville, MO · On-site

$110K - $220K/yr

Causal Inference & Elasticity: Identification of treatment effects beyond simple log-log approaches (Double ML, Instrumental Variables, Uplift modeling); Optimization & Reinforcement Learning: Multi ...

New

Walmart

Staff, Data Scientist (Pricing)

Anderson, MO · On-site

$110K - $220K/yr

Causal Inference & Elasticity: Identification of treatment effects beyond simple log-log approaches (Double ML, Instrumental Variables, Uplift modeling); Optimization & Reinforcement Learning: Multi ...

New

DDN

Senior Staff Engineer - AI Data Path

California, MO · On-site

$180 - $250/hr

Partner with AI/ML engineering teams to optimize inference performance in frameworks such as PyTorch and TensorFlow. * Establish benchmarking frameworks and lead performance tuning efforts for ...

Jobgether

You will own critical areas including model fine-tuning, inference optimization, ML infrastructure, evaluation frameworks, and deployment processes. Working within a small, highly skilled engineering ...

Walmart

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 ...

Walmart

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 ...

MasterCard

Senior AI Engineer

O Fallon, MO · Hybrid

$97K - $134K/yr

We are looking for a Senior AI Engineer, Generative AI & ML Engineering for the Operational ... Develop high-performance Python backend services for LLM inference orchestration, async job ...

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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 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 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 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.

Is ML inference a high paying job?

ML inference roles are generally well-paying, especially for those with skills in machine learning frameworks, programming, and cloud platforms. Salaries vary based on experience, location, and industry, but they tend to be higher than average for tech-related positions.
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 job categories do people searching Ml Inference jobs in Missouri look for? The top searched job categories for Ml Inference jobs in Missouri are:
What cities in Missouri are hiring for Ml Inference jobs? Cities in Missouri with the most Ml Inference job openings:
Ml Inference jobs near you
Infographic showing various Ml Inference job openings in Missouri as of July 2026, with employment types broken down into 90% Full Time, 7% Part Time, and 3% Contract. Highlights an 81% Physical, 5% Hybrid, and 14% Remote job distribution.

Machine Learning Engineer - Inference Optimization

Jobgether

On-site, Remote

$88K - $106K/yr

Full-time

Posted 14 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 - Inference Optimization based in Netherlands.

This role offers the opportunity to optimize the performance of advanced machine learning systems used in real-world production environments.
You will work at the intersection of research and engineering, transforming cutting-edge models into fast, reliable, and cost-efficient solutions.
Your work will directly impact model scalability, user experience, and the efficiency of AI-powered products.
You will dive deep into performance optimization, from model architecture and GPU execution to large-scale inference infrastructure.
Working with talented research, infrastructure, and product teams, you will help push the boundaries of what AI systems can achieve.
This position is ideal for an engineer who enjoys solving complex technical challenges and building high-performance ML systems from the ground up.

Accountabilities

As a Machine Learning Engineer specializing in inference optimization, you will own the performance and scalability of machine learning models in production. You will combine deep ML expertise, systems engineering, and performance analysis to deliver faster, more efficient AI experiences.

  • Optimize machine learning inference systems to improve latency, throughput, scalability, and operational cost.
  • Profile and identify bottlenecks across GPU and CPU inference pipelines, including memory usage, kernels, batching strategies, and data flow.
  • Implement advanced optimization techniques such as quantization, KV-cache optimization, speculative decoding, batching, streaming, and model simplification.
  • Collaborate with research engineers to productionize new model architectures and translate experimental results into reliable systems.
  • Build, improve, and maintain inference-serving infrastructure using modern frameworks, custom runtimes, or specialized serving solutions.
  • Benchmark model performance across different hardware environments, including GPUs, CPUs, and cloud-based systems.
  • Improve system reliability, monitoring, observability, and cost efficiency under real production workloads.
  • Contribute to engineering practices that improve the quality, scalability, and maintainability of ML infrastructure.
Requirements:

The ideal candidate is a technically strong machine learning engineer with experience optimizing production inference systems and a passion for high-performance AI engineering. You should enjoy working on complex technical problems, experimenting with new approaches, and taking ownership of critical systems.

  • Strong professional experience in ML inference optimization, high-performance machine learning systems, or related areas.
  • Deep understanding of machine learning fundamentals, including neural network architectures, attention mechanisms, memory optimization, and compute graphs.
  • Hands-on experience with PyTorch or similar deep learning frameworks and deploying models into production environments.
  • Experience with GPU performance optimization, including technologies such as CUDA, ROCm, Triton, or kernel-level tuning.
  • Proven experience scaling inference systems for real users beyond research prototypes or benchmarks.
  • Strong programming skills and the ability to work across machine learning and systems engineering domains.
  • Ability to operate effectively in fast-paced environments with ownership, autonomy, and evolving priorities.
  • Experience with inference frameworks such as TensorRT, ONNX Runtime, vLLM, or Triton is a plus.
  • Familiarity with large language models, long-context inference, distributed systems, low-latency services, or hardware optimization is considered an advantage.
  • Contributions to open-source ML systems or inference tooling are a plus.
Benefits:
  • Competitive compensation package with meaningful equity participation.
  • Opportunity to work on performance-critical AI systems with direct product impact.
  • High level of ownership over infrastructure that shapes scalability and efficiency.
  • Close collaboration with research, infrastructure, and product teams.
  • Opportunity to work on advanced machine learning technologies and real-world AI applications.
  • Engineering-focused culture that values technical excellence, experimentation, and quality.
  • Flexible remote work environment.
  • Opportunity to contribute to the growth of an innovative AI-focused organization.
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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