ML Engineer
Omaha, NE · On-site
... ML inference systems that handle high-volume, low-latency predictions in production environments • Create comprehensive monitoring and alerting systems for model performance, data drift, and system ...
ML Engineer
Omaha, NE · On-site
... ML inference systems that handle high-volume, low-latency predictions in production environments • Create comprehensive monitoring and alerting systems for model performance, data drift, and system ...
... ML inference systems that handle high-volume, low-latency predictions in production environments Create comprehensive monitoring and alerting systems for model performance, data drift, and system ...
... ML inference systems that handle high-volume, low-latency predictions in production environments Create comprehensive monitoring and alerting systems for model performance, data drift, and system ...
ML Engineer
Omaha, NE · On-site
... ML inference systems that handle high-volume, low-latency predictions in production environments • Create comprehensive monitoring and alerting systems for model performance, data drift, and system ...
ML Engineer
Omaha, NE · On-site
... ML inference systems that handle high-volume, low-latency predictions in production environments • Create comprehensive monitoring and alerting systems for model performance, data drift, and system ...
ML Engineer
Omaha, NE · On-site
... ML inference systems that handle high-volume, low-latency predictions in production environments • Create comprehensive monitoring and alerting systems for model performance, data drift, and system ...
ML Engineer
Omaha, NE · On-site
... ML inference systems that handle high-volume, low-latency predictions in production environments • Create comprehensive monitoring and alerting systems for model performance, data drift, and system ...
Machine Learning Engineer
Omaha, NE · On-site
... model inference services. You will learn and apply new techniques from open source packages and ... Work spans classical ML through LLM systems. You improve search and retrieval quality using real ...
Machine Learning Engineer
Omaha, NE · On-site
... model inference services. You will learn and apply new techniques from open source packages and ... Work spans classical ML through LLM systems. You improve search and retrieval quality using real ...
As a Data Scientist at Agile Defense, you will be joining a team of professionals that build AI/ML ... inference, and statistical modeling techniques · Strong programming skills in Python or R with ...
As a Data Scientist at Agile Defense, you will be joining a team of professionals that build AI/ML ... inference, and statistical modeling techniques · Strong programming skills in Python or R with ...
As a Data Scientist at Agile Defense, you will be joining a team of professionals that build AI/ML ... inference, and statistical modeling techniques Strong programming skills in Python or R with ...
As a Data Scientist at Agile Defense, you will be joining a team of professionals that build AI/ML ... inference, and statistical modeling techniques Strong programming skills in Python or R with ...
Data Scientist
Omaha, NE · On-site
As a Data Scientist at Agile Defense, you will be joining a team of professionals that build AI/ML ... inference, and statistical modeling techniques • Strong programming skills in Python or R with ...
Data Scientist
Omaha, NE · On-site
As a Data Scientist at Agile Defense, you will be joining a team of professionals that build AI/ML ... inference, and statistical modeling techniques • Strong programming skills in Python or R with ...
Some of the core areas of focus for our team include pricing, online advertising, uplift and long term value modeling, and general causal inference. Search & Discovery ML : The Search and Discovery ...
Some of the core areas of focus for our team include pricing, online advertising, uplift and long term value modeling, and general causal inference. Search & Discovery ML : The Search and Discovery ...
Some of the core areas of focus for our team include pricing, online advertising, uplift and long term value modeling, and general causal inference. Search & Discovery ML : The Search and Discovery ...
Some of the core areas of focus for our team include pricing, online advertising, uplift and long term value modeling, and general causal inference. Search & Discovery ML : The Search and Discovery ...
Some of the core areas of focus for our team include pricing, online advertising, uplift and long term value modeling, and general causal inference. Search & Discovery ML : The Search and Discovery ...
Some of the core areas of focus for our team include pricing, online advertising, uplift and long term value modeling, and general causal inference. Search & Discovery ML : The Search and Discovery ...
Some of the core areas of focus for our team include pricing, online advertising, uplift and long term value modeling, and general causal inference. Search & Discovery ML : The Search and Discovery ...
Some of the core areas of focus for our team include pricing, online advertising, uplift and long term value modeling, and general causal inference. Search & Discovery ML : The Search and Discovery ...
Some of the core areas of focus for our team include pricing, online advertising, uplift and long term value modeling, and general causal inference. Search & Discovery ML : The Search and Discovery ...
Some of the core areas of focus for our team include pricing, online advertising, uplift and long term value modeling, and general causal inference. Search & Discovery ML : The Search and Discovery ...
Industrial Engineer - AI Trainer
Lincoln, NE · On-site +1
$60/hr
... inference. Write clear technical explanations and well‐documented analytical code. Provide ... Relevant credentials are a plus (e.g., Kaggle Competition ranking, AWS/GCP ML certifications, or ...
Industrial Engineer - AI Trainer
Lincoln, NE · On-site +1
$60/hr
... inference. Write clear technical explanations and well‐documented analytical code. Provide ... Relevant credentials are a plus (e.g., Kaggle Competition ranking, AWS/GCP ML certifications, or ...
Survey Statistician - AI Trainer
Lincoln, NE · On-site +1
$60/hr
... inference. Write clear technical explanations and well‐documented analytical code. Provide ... Relevant credentials are a plus (e.g., Kaggle Competition ranking, AWS/GCP ML certifications, or ...
Survey Statistician - AI Trainer
Lincoln, NE · On-site +1
$60/hr
... inference. Write clear technical explanations and well‐documented analytical code. Provide ... Relevant credentials are a plus (e.g., Kaggle Competition ranking, AWS/GCP ML certifications, or ...
Epidemiologist - AI Trainer
Lincoln, NE · On-site +1
$60/hr
... inference. Write clear technical explanations and well‐documented analytical code. Provide ... Relevant credentials are a plus (e.g., Kaggle Competition ranking, AWS/GCP ML certifications, or ...
Epidemiologist - AI Trainer
Lincoln, NE · On-site +1
$60/hr
... inference. Write clear technical explanations and well‐documented analytical code. Provide ... Relevant credentials are a plus (e.g., Kaggle Competition ranking, AWS/GCP ML certifications, or ...
Astrophysicist - AI Trainer
Lincoln, NE · On-site +1
$60/hr
... inference. Write clear technical explanations and well‐documented analytical code. Provide ... Relevant credentials are a plus (e.g., Kaggle Competition ranking, AWS/GCP ML certifications, or ...
Astrophysicist - AI Trainer
Lincoln, NE · On-site +1
$60/hr
... inference. Write clear technical explanations and well‐documented analytical code. Provide ... Relevant credentials are a plus (e.g., Kaggle Competition ranking, AWS/GCP ML certifications, or ...
Computational Biologist - AI Trainer
Lincoln, NE · On-site +1
$60/hr
... inference. Write clear technical explanations and well‐documented analytical code. Provide ... Relevant credentials are a plus (e.g., Kaggle Competition ranking, AWS/GCP ML certifications, or ...
Computational Biologist - AI Trainer
Lincoln, NE · On-site +1
$60/hr
... inference. Write clear technical explanations and well‐documented analytical code. Provide ... Relevant credentials are a plus (e.g., Kaggle Competition ranking, AWS/GCP ML certifications, or ...
Analytics Engineer - AI Trainer
Lincoln, NE · On-site +1
$60/hr
... inference. Write clear technical explanations and well‐documented analytical code. Provide ... Relevant credentials are a plus (e.g., Kaggle Competition ranking, AWS/GCP ML certifications, or ...
Analytics Engineer - AI Trainer
Lincoln, NE · On-site +1
$60/hr
... inference. Write clear technical explanations and well‐documented analytical code. Provide ... Relevant credentials are a plus (e.g., Kaggle Competition ranking, AWS/GCP ML certifications, or ...
Research Data Scientist - AI Trainer
Lincoln, NE · On-site +1
$40/hr
... statistical inference. Write clear technical explanations and well-documented analytical code ... Relevant credentials are a plus (e.g., Kaggle Competition ranking, AWS/GCP ML certifications, or ...
Research Data Scientist - AI Trainer
Lincoln, NE · On-site +1
$40/hr
... statistical inference. Write clear technical explanations and well-documented analytical code ... Relevant credentials are a plus (e.g., Kaggle Competition ranking, AWS/GCP ML certifications, or ...
Ml Inference information
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 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 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?
| Aspect | ML Inference | Data Scientist |
|---|---|---|
| Required Credentials | Knowledge of machine learning models, programming skills | Degree in data science, statistics, or related fields |
| Work Environment | Deploying models in production, real-time data processing | Data analysis, model development, research |
| Industry Usage | AI product deployment, software companies | Research 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 Nebraska? For Ml Inference jobs in Nebraska, the most frequently searched job titles are:
What job categories do people searching Ml Inference jobs in Nebraska look for? The top searched job categories for Ml Inference jobs in Nebraska are:
What cities in Nebraska are hiring for Ml Inference jobs? Cities in Nebraska with the most Ml Inference job openings:
Job description
Job Summary:
Agile Defense is a company focused on adaptive innovation to support national missions through advanced technologies. They are seeking an ML Engineer to join their team, responsible for building scalable data architectures and AI/ML pipelines to support national defense priorities.
Responsibilities:
• Design and implement medallion architecture (Bronze/Silver/Gold) using Databricks for reliable data processing and ML model training
• Develop automated data pipelines that process structured and unstructured data from multiple sources into analytics-ready formats
• Create robust ETL/ELT workflows using Apache Spark and modern data engineering practices for both batch and streaming data
• Build and maintain data quality monitoring and validation systems across the entire data and ML lifecycle
• Implement MLOps best practices including automated model training, validation, deployment, and monitoring using MLflow and Databricks workflows
• Design scalable ML inference systems that handle high-volume, low-latency predictions in production environments
• Create comprehensive monitoring and alerting systems for model performance, data drift, and system health
• Build self-service ML capabilities that enable data scientists to deploy and monitor their own models efficiently
• Design and maintain data models that support both machine learning workloads and business intelligence requirements
• Create integration points between ML systems and business intelligence platforms (Tableau, PowerBI, Qlik Sense)
• Implement data governance standards and metadata management systems that ensure data quality and compliance
• Collaborate with analysts and data scientists to optimize data architecture for both predictive modeling and reporting needs
• Implement comprehensive data governance frameworks including data lineage tracking, quality monitoring, and compliance controls
• Design and maintain data catalogs and metadata management systems that enable efficient data discovery across the organization
• Establish data quality standards and automated testing frameworks for both analytical and ML workloads
• Work with stakeholders to define data definitions, business logic, and governance policies
• Build integrations with MAVEN Smart Systems (Palantir Foundry) environments to support operational and predictive analytics
• Connect Databricks-based systems with enterprise data warehouses, streaming platforms, and business applications
• Implement security and compliance controls that meet enterprise requirements while enabling self-service capabilities
• Collaborate with platform engineers to integrate ML systems with broader application architecture and infrastructure
Qualifications:
Required:
• 5+ years of technical experience, including 3+ years building production data pipelines and ML infrastructure using distributed computing platforms like Databricks.
• Strong data engineering skills in Python, PySpark, and Spark SQL with experience implementing medallion architecture and modern data platform patterns
• Production ML systems experience including model deployment, monitoring, and MLOps practices in cloud environments
• Data architecture expertise with experience designing scalable data processing systems and implementing data governance frameworks
• Experience integrating with platforms such as Qlik, Tableau, PowerBI, MAVEN Smart System (Palantir), or similar.
Preferred:
• Deep expertise in distributed computing, performance optimization, and large-scale data processing using Databricks and Apache Spark
• Advanced MLOps knowledge including automated retraining, model versioning, model testing frameworks, and production ML monitoring
• Experience conducting regression analysis, and building predictive models for business applications with measurable impact
• Advanced statistical knowledge including experimental design, hypothesis testing, causal inference, and statistical modeling techniques
• Experience designing and building enterprise-level dashboards, reports, and self-service analytics platforms
• Analytics platform knowledge including experience with Advana / MAVEN Smart Systems (Palantir Foundry) or similar enterprise analytics environments
Company:
Agile Defense is an information technology company located in Reston. It is a sub-organization of Agile-BOT. Founded in 1998, the company is headquartered in Reston, USA, with a team of 1001-5000 employees. The company is currently Late Stage.
Agile Defense is a company focused on adaptive innovation to support national missions through advanced technologies. They are seeking an ML Engineer to join their team, responsible for building scalable data architectures and AI/ML pipelines to support national defense priorities.
Responsibilities:
• Design and implement medallion architecture (Bronze/Silver/Gold) using Databricks for reliable data processing and ML model training
• Develop automated data pipelines that process structured and unstructured data from multiple sources into analytics-ready formats
• Create robust ETL/ELT workflows using Apache Spark and modern data engineering practices for both batch and streaming data
• Build and maintain data quality monitoring and validation systems across the entire data and ML lifecycle
• Implement MLOps best practices including automated model training, validation, deployment, and monitoring using MLflow and Databricks workflows
• Design scalable ML inference systems that handle high-volume, low-latency predictions in production environments
• Create comprehensive monitoring and alerting systems for model performance, data drift, and system health
• Build self-service ML capabilities that enable data scientists to deploy and monitor their own models efficiently
• Design and maintain data models that support both machine learning workloads and business intelligence requirements
• Create integration points between ML systems and business intelligence platforms (Tableau, PowerBI, Qlik Sense)
• Implement data governance standards and metadata management systems that ensure data quality and compliance
• Collaborate with analysts and data scientists to optimize data architecture for both predictive modeling and reporting needs
• Implement comprehensive data governance frameworks including data lineage tracking, quality monitoring, and compliance controls
• Design and maintain data catalogs and metadata management systems that enable efficient data discovery across the organization
• Establish data quality standards and automated testing frameworks for both analytical and ML workloads
• Work with stakeholders to define data definitions, business logic, and governance policies
• Build integrations with MAVEN Smart Systems (Palantir Foundry) environments to support operational and predictive analytics
• Connect Databricks-based systems with enterprise data warehouses, streaming platforms, and business applications
• Implement security and compliance controls that meet enterprise requirements while enabling self-service capabilities
• Collaborate with platform engineers to integrate ML systems with broader application architecture and infrastructure
Qualifications:
Required:
• 5+ years of technical experience, including 3+ years building production data pipelines and ML infrastructure using distributed computing platforms like Databricks.
• Strong data engineering skills in Python, PySpark, and Spark SQL with experience implementing medallion architecture and modern data platform patterns
• Production ML systems experience including model deployment, monitoring, and MLOps practices in cloud environments
• Data architecture expertise with experience designing scalable data processing systems and implementing data governance frameworks
• Experience integrating with platforms such as Qlik, Tableau, PowerBI, MAVEN Smart System (Palantir), or similar.
Preferred:
• Deep expertise in distributed computing, performance optimization, and large-scale data processing using Databricks and Apache Spark
• Advanced MLOps knowledge including automated retraining, model versioning, model testing frameworks, and production ML monitoring
• Experience conducting regression analysis, and building predictive models for business applications with measurable impact
• Advanced statistical knowledge including experimental design, hypothesis testing, causal inference, and statistical modeling techniques
• Experience designing and building enterprise-level dashboards, reports, and self-service analytics platforms
• Analytics platform knowledge including experience with Advana / MAVEN Smart Systems (Palantir Foundry) or similar enterprise analytics environments
Company:
Agile Defense is an information technology company located in Reston. It is a sub-organization of Agile-BOT. Founded in 1998, the company is headquartered in Reston, USA, with a team of 1001-5000 employees. The company is currently Late Stage.
About Agile Defense
Sourced by ZipRecruiter
Industry
It services
Company size
201 - 500 Employees
Headquarters location
Reston, VA, US
Year founded
1998