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Ml Inference Jobs in Wisconsin (NOW HIRING)

Jobtailor

WI · On-site

$140 - $190/hr

Work on pricing systems involving causal inference, mixed integer programming, and ML-based demand estimation Requirements * Bachelor's degree in Computer Science, Statistics, Mathematics, or a ...

YDU JC Air Cond & Ref Inc.- Dubai

$85 - $127/hr

The Senior AI/ML Engineer sits at the center of this transformation, building production AI/ML and ... Build and maintain data pipelines, model integration layers, and inference infrastructure for ...

Neurophos Inc.

WI · On-site

$140 - $210/hr

We are seeking a talented ML Compiler Engineer to join our engineering team and lead the ... PyTorch/JAX/Triton integration, custom inference engines * Focus Areas: Compiler backend ...

3M HEALTHCARE

WI · On-site

$100 - $150/hr

Architect data and ML infrastructure in partnership with the platform and engineering teams to support large‑scale training and reliable, low‑latency inference * Communicate strategy and outcomes ...

Deloitte

Google AI Architect

Milwaukee, WI

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 ...

Johnson Controls

AI Engineer

Glendale, WI · On-site

This AI/ML Engineer role sits at the center of that transformation. You will do two things in ... Build and maintain data pipelines, model integration layers, and inference infrastructure for real ...

Johnson Controls

AI Engineer

Glendale, WI · On-site +1

This AI/ML Engineer role sits at the center of that transformation. You will do two things in ... Build and maintain data pipelines, model integration layers, and inference infrastructure for real ...

TBK Bank, SSB

WI · On-site

$206.26 - $330.02/hr

Model inference runs on a mix of FastAPI and Clojure applications, depending on the model type. Our ML systems process more than 1 million documents per day through hundreds of models requiring ...

Devitechs

WI · On-site

$80 - $120/hr

Improve inference time and system throughput. * Participate in code reviews and technical discussions. Maintain high‑quality coding and development standards. * Support integration of AI features ...

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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.

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Ml Inference jobs near you

Machine Learning Scientist III - Package Pricing

Jobtailor

WI • On-site

$140 - $190/hr

Other

Posted 12 days ago

Job description

  • Design, build, and improve machine learning models and systems powering Expedia Group products
  • Perform end-to-end model development, including problem formulation, data exploration, feature engineering, training, evaluation, and production deployment
  • Develop robust data pipelines, data transformations, and data quality checks for ML models and experimentation
  • Partner with product, engineering, and analytics teams to translate business problems into ML solutions
  • Define success metrics and run experiments to validate impact
  • Safely integrate and operate AI/ML-enabled solutions in real-world products
  • Contribute to system design, including model-serving APIs and supporting data models
  • Create documentation and best practices supporting reuse and scalability across product domains
  • Work on pricing systems involving causal inference, mixed integer programming, and ML-based demand estimation
Requirements
  • Bachelor's degree in Computer Science, Statistics, Mathematics, or a related technical field, or equivalent practical experience
  • 5+ professional years of experience as a Machine Learning Scientist or in a similar applied ML role
  • Experience with end-to-end model development and deployment in real-world products
  • Proficiency in at least one programming language commonly used for ML, such as Python
  • Experience with data processing frameworks, model training libraries, and model evaluation techniques
  • Experience owning ML components or services in production
  • Experience monitoring model performance and maintaining data pipelines
  • Experience collaborating with engineering teams on APIs and data models
  • Advanced degree in a quantitative discipline preferred
  • Experience with pricing, revenue optimization, marketplace dynamics, or similar business problems preferred
  • Experience designing and analyzing experiments, such as A/B testing, preferred
  • Experience working with noisy or incomplete data preferred
  • Experience building ML models using user behavior, segmentation, or contextual signals preferred
  • Familiarity with causal inference methods or optimization techniques, such as mixed-integer programming, preferred
Core Competencies

Demonstrates expertise in end-to-end machine learning model development, including data exploration, feature engineering, and deployment. Proficient in collaborating with cross-functional teams to translate business challenges into effective ML solutions while ensuring robust data pipelines and model performance monitoring.

Highest-signal resume keywords
  • Machine Learning Model Development
  • Python Programming
  • Data Processing Frameworks
  • Model Evaluation Techniques
  • Causal Inference Methods
ATS Optimization KeywordsHard Skills
  • End-to-End Model Development
  • Feature Engineering
  • Data Quality Checks
  • Model Training Libraries
  • Model Performance Monitoring
  • A/B Testing
  • Mixed Integer Programming
  • Demand Estimation
  • Data Transformations
  • Experiment Design
Soft Skills
  • Collaboration
  • Communication
Industry Keywords
  • Pricing Systems
  • Revenue Optimization
  • Marketplace Dynamics
  • Quantitative Discipline
  • Statistical Analysis
Tools & Technologies
  • Machine Learning Frameworks
  • APIs
  • Data Pipelines
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