... and inference services. * Work with business leads to imagine agentic products and drive ... AuraDB, Causal Clustering) and/or Stardog (SPARQL, OWL 2 reasoning, Virtual Graphs, SHACL ...
... and inference services. * Work with business leads to imagine agentic products and drive ... AuraDB, Causal Clustering) and/or Stardog (SPARQL, OWL 2 reasoning, Virtual Graphs, SHACL ...
$77K - $105K/yr
Senior Machine Learning Engineer We're looking for a Senior ML Engineer to advance our age bracket ... We run 5 binary classifiers (+12/+15/+18/+21/+25) deployed as ONNX models for client-side inference ...
$77K - $105K/yr
Senior Machine Learning Engineer We're looking for a Senior ML Engineer to advance our age bracket ... We run 5 binary classifiers (+12/+15/+18/+21/+25) deployed as ONNX models for client-side inference ...
$77K - $105K/yr
Senior Machine Learning Engineer Reporting to: Data Science Manager Schedule: 35-hour work week ... Improve inference performance for deployed models, including complex graph inference models ...
$77K - $105K/yr
Senior Machine Learning Engineer Reporting to: Data Science Manager Schedule: 35-hour work week ... Improve inference performance for deployed models, including complex graph inference models ...
Machine Learning Engineer The Mission: You are the engineer who ships the model, not just the one ... You will own the full arc: raw sensor data to production inference, dataset curation to deployment ...
Machine Learning Engineer The Mission: You are the engineer who ships the model, not just the one ... You will own the full arc: raw sensor data to production inference, dataset curation to deployment ...
... and/or causal representation methods, supporting downstream applications such as target ... Deploy and run inference on generative AI models using proprietary datasets on cloud platforms.
... and/or causal representation methods, supporting downstream applications such as target ... Deploy and run inference on generative AI models using proprietary datasets on cloud platforms.
$60K - $81K/yr
Design, build, and deploy machine learning models into production ... Develop scalable ML pipelines for training, evaluation, monitoring, and inference * Build ...
$60K - $81K/yr
Design, build, and deploy machine learning models into production ... Develop scalable ML pipelines for training, evaluation, monitoring, and inference * Build ...
$64K - $78K/yr
The successful candidate will apply advanced probability, stochastic processes, statistical inference, machine learning, and statistical learning to noisy and sparse mission data, translating complex ...
$64K - $78K/yr
The successful candidate will apply advanced probability, stochastic processes, statistical inference, machine learning, and statistical learning to noisy and sparse mission data, translating complex ...
$175K - $308K/yr
Knowledge of Bayesian statistical methods and how they are used for scientific inference. Preferred ... Familiarity with interpretability methods like activation patching/causal tracing. * Demonstrate ...
$175K - $308K/yr
Knowledge of Bayesian statistical methods and how they are used for scientific inference. Preferred ... Familiarity with interpretability methods like activation patching/causal tracing. * Demonstrate ...
Role As a Principal Machine Learning Engineer, you are a deep technical authority responsible for ... You operate across training, inference, evaluation, and infrastructure, solving the hardest ...
Role As a Principal Machine Learning Engineer, you are a deep technical authority responsible for ... You operate across training, inference, evaluation, and infrastructure, solving the hardest ...
$102K - $194K/yr
Develop machine learning and statistical models, including approaches such as regression, classification, clustering, attribution modeling, marketing mix modeling, lead scoring, causal inference, and ...
$102K - $194K/yr
Develop machine learning and statistical models, including approaches such as regression, classification, clustering, attribution modeling, marketing mix modeling, lead scoring, causal inference, and ...
Build and optimise model serving and inference infrastructure for high-throughput and low-latency ... Experience building ML infrastructure, platforms, or production machine learning systems
Build and optimise model serving and inference infrastructure for high-throughput and low-latency ... Experience building ML infrastructure, platforms, or production machine learning systems
$77K - $105K/yr
Zof AI is seeking a Senior Machine Learning Engineer for the traditional ML discipline: training ... Experience with model serving and inference optimization. * Published work, competition results, or ...
$77K - $105K/yr
Zof AI is seeking a Senior Machine Learning Engineer for the traditional ML discipline: training ... Experience with model serving and inference optimization. * Published work, competition results, or ...
$135/hr
... data, causal inference, precision health, network analysis, computational and systems biology ... machine learning methods, and environmental biostatistics. Our innovative approaches to the ...
$135/hr
... data, causal inference, precision health, network analysis, computational and systems biology ... machine learning methods, and environmental biostatistics. Our innovative approaches to the ...
Role As Technical Lead, Machine Learning, you own the execution layer of A1's intelligence. You ... Architect and operate scalable inference systems, balancing latency, cost, and reliability.
Role As Technical Lead, Machine Learning, you own the execution layer of A1's intelligence. You ... Architect and operate scalable inference systems, balancing latency, cost, and reliability.
Role As Technical Lead, Machine Learning, you own the execution layer of A1's intelligence. You ... Architect and operate scalable inference systems, balancing latency, cost, and reliability.
Role As Technical Lead, Machine Learning, you own the execution layer of A1's intelligence. You ... Architect and operate scalable inference systems, balancing latency, cost, and reliability.
Position: Staff Engineer, Machine Learning Operations The Staff Engineer, Machine Learning ... Production experience with both batch and real-time inference architectures * Understanding of ...
Position: Staff Engineer, Machine Learning Operations The Staff Engineer, Machine Learning ... Production experience with both batch and real-time inference architectures * Understanding of ...
$50K - $75K/yr
Job Title Postdoctoral Associate Division Divison of Academic Affairs Department National ... Experience in developing machine learning models using Pytorch, TensorFlow, and/or other relevant ...
$50K - $75K/yr
Job Title Postdoctoral Associate Division Divison of Academic Affairs Department National ... Experience in developing machine learning models using Pytorch, TensorFlow, and/or other relevant ...
$84K - $101K/yr
... Machine Learning, Cyber Security and Cutting Edge Technology across the US Government. Be a part of ... Engineer high-quality features and maintain training/inference pipelines. Cloud and Platform ...
$84K - $101K/yr
... Machine Learning, Cyber Security and Cutting Edge Technology across the US Government. Be a part of ... Engineer high-quality features and maintain training/inference pipelines. Cloud and Platform ...
$90K - $119K/yr
We are seeking the best Machine Learning Engineers with a background in computer vision, LiDAR ... Experience with model optimization for real-time inference on embedded or automotive platforms (e.g ...
$90K - $119K/yr
We are seeking the best Machine Learning Engineers with a background in computer vision, LiDAR ... Experience with model optimization for real-time inference on embedded or automotive platforms (e.g ...
$77K - $105K/yr
Title and Location: Sr Machine Learning Engineer in Santa Clara, CA. Job Responsibilities ... Evaluate model inference performance and runtime behavior across heterogeneous hardware platforms ...
$77K - $105K/yr
Title and Location: Sr Machine Learning Engineer in Santa Clara, CA. Job Responsibilities ... Evaluate model inference performance and runtime behavior across heterogeneous hardware platforms ...
Causal Inference Machine Learning Postdoctoral information
What is a causal inference machine learning postdoctoral researcher?
A Causal Inference Machine Learning Postdoctoral researcher is a scientist who specializes in developing and applying machine learning methods to understand cause-and-effect relationships in data. They typically hold a recent PhD in statistics, computer science, economics, or a related field, and work in academic or industry research settings. Their work involves designing experiments, analyzing complex datasets, and creating models that can infer causal relationships, which are crucial for making robust predictions and informed decisions. This role often collaborates with interdisciplinary teams to apply these techniques to domains such as healthcare, social science, or economics.
What are the key skills and qualifications needed to thrive as a causal inference machine learning postdoctoral researcher?
To thrive as a Causal Inference Machine Learning Postdoctoral researcher, you need a strong background in statistics, causal inference methodologies, and advanced machine learning, usually evidenced by a PhD in a relevant field. Familiarity with programming languages such as Python or R, experience using statistical software (e.g., TensorFlow, PyTorch, Stan), and knowledge of causal inference libraries are typically required. Outstanding analytical thinking, problem-solving abilities, and strong communication skills help you collaborate effectively and explain complex concepts to diverse audiences. These skills and qualifications are vital for advancing research, deriving actionable insights from data, and contributing to impactful scientific discoveries.
What are some common challenges faced by causal inference machine learning postdoctoral researchers when integrating causal models with real-world data?
Causal Inference Machine Learning Postdoctoral researchers often encounter challenges such as dealing with unobserved confounding variables, ensuring data quality, and addressing biases inherent in observational datasets. Integrating advanced machine learning techniques with causal inference frameworks requires careful consideration of model assumptions and validation methods. Collaboration with domain experts is essential to properly interpret results and to translate findings into actionable insights, especially in interdisciplinary settings like healthcare or social sciences.
What is the difference between Causal Inference Machine Learning Postdoctoral vs Data Scientist?
| Aspect | Causal Inference Machine Learning Postdoctoral | Data Scientist |
|---|---|---|
| Required Credentials | PhD in statistics, machine learning, or related field | Bachelor's or Master's in data science, computer science, or related field |
| Work Environment | Academic research, research labs, universities | Corporate, tech companies, startups |
| Industry Usage | Research, academia, specialized industry projects | Business analytics, product development, data-driven decision making |
| Common Search/Comparison | Yes | Yes |
The main difference is that Causal Inference Machine Learning Postdoctoral roles focus on academic research and developing new methods in causal inference, often requiring a PhD. Data Scientists typically work in industry, applying existing models to solve business problems, with a focus on data analysis and visualization. While both roles involve machine learning, the postdoctoral position emphasizes research and theory, whereas data science emphasizes practical application.
Is it difficult to get a causal inference machine learning postdoctoral position?
Securing a causal inference machine learning postdoctoral position can be competitive due to specialized skills required, such as expertise in statistical methods, programming (e.g., Python or R), and a strong research background. Candidates with relevant publications, strong recommendations, and experience in machine learning frameworks often have better chances, but the availability of such positions varies by institution and funding.
What are popular job titles related to Causal Inference Machine Learning Postdoctoral jobs in Kentucky?
For Causal Inference Machine Learning Postdoctoral jobs in Kentucky, the most frequently searched job titles are:
What job categories do people searching Causal Inference Machine Learning Postdoctoral jobs in Kentucky look for?
The top searched job categories for Causal Inference Machine Learning Postdoctoral jobs in Kentucky are:
What cities in Kentucky are hiring for Causal Inference Machine Learning Postdoctoral jobs?
Cities in Kentucky with the most Causal Inference Machine Learning Postdoctoral job openings:
Consultant Machine Learning & Knowledge Graph Engineer
On-site
Dell GmbH
Other
Posted 12 days ago
Job description
Consultant Machine Learning & Knowledge Graph Engineer
#J-18808-LjbffrData Science is all about breaking new ground to enable businesses to answer their most urgent questions. Pioneering massively parallel data-intensive analytic processing, our mission is to develop a whole new approach to generating meaning and value from petabyte-scale data sets and shape brand new methodologies, tools, statistical methods and models. What’s more, we are in collaboration with leading academics, industry experts and highly skilled engineers to equip our customers to generate sophisticated new insights from the biggest of big data.
Join us to do the best work of your career and make a profound impact asConsultantML & KG Engineer on our growing and dynamic team inRound Rock, Texas.
Whatyou'llachieveLead the architecture, development, and deployment of enterprisescale ML solutions across Dell's global ecosystem.DriveMLOpsstandards, buildproductiongradeML services, and collaborate across engineering, product, and platform teams to enable AI atscale.scaleML solutions across Dell's global ecosystem.As a Consultant Machine Learning & Knowledge Graph Engineer, you will play a pivotal role in advancing our AI and ML capabilities and creating Enterprise wide KG marketplace and Ontology layouts. You willbe responsible fordesigning, building, and operationalizing machine learning systems, includingnext generationagentic andGenAI poweredapplications. You will drive and execute our broader AI/ML strategy.You will also be responsible to architect production-grade Knowledge Graph platforms, design semantic data layers that power Agentic AI, and drive the convergence of graph technologies with large-scale data engineering ecosystems. This role demands a rare combination of deep graph expertise, distributed systems mastery, and strategic business influence. You will work deeply across data pipelines, model development, optimization, and production deployment to deliver scalable,high performanceML solutions.
You will- Lead the end‑to‑end Agentic lifecycle from conceptualizing, prototyping and driving delivery with engineering teams and design and build autonomous AI agents, ML systems, pipelines, and inference services.
- Work with business leads to imagine agentic products and drive accelerated delivery through Spec Driven Development and implement MLOps practices including CI/CD, model monitoring, drift detection, and automated retraining.
- Collaborate with Data Engineering and Platform teams to ensure data, infrastructure, and governance readiness along with providing technical leadership while integrating emerging AI/ML technologies and managing production incidents.
- Design, build, and scale enterprise Knowledge Graph platforms using Neo4j and/or Stardog, establishing graph-native data models that enable entity resolution, relationship discovery, and semantic reasoning across business domains.
- Define and govern enterprise ontologies (OWL 2), taxonomies, and semantic schemas that provide a unified, machine-interpretable view of Dell's data assets, ensuring consistency, reusability, and inferencing capability
- Architect graph-backed Retrieval-Augmented Generation (RAG) systems, tool-calling interfaces, and dynamic prompt-to-graph query pipelines that fuel autonomous AI agent decision-making with deterministic, explainable knowledge
Every Dell Technologies team member brings something unique to the table.Here'swhat we are looking for with this role:
Essential Requirements:- 12+ years of experience delivering complex AI/ML or applied science systems, including deep learning, machine learning, and LLM‑based solutions.
- Advanced Python expertise with strong knowledge of ETL pipelines (Airflow preferred) and modern data‑warehousing concepts.
- Graph Architecture Mastery: Extensive hands‑on experience designing and operating production‑grade graph systems using Neo4j (Cypher, GDS, APOC, AuraDB, Causal Clustering) and/or Stardog (SPARQL, OWL 2 reasoning, Virtual Graphs, SHACL validation)
- Distributed Systems and Data Scale: Expert‑level command over PySpark, Kafka, data lakehouses (Apache Iceberg, Delta Lake), and enterprise orchestration (Airflow), with proven ability to integrate these with graph ecosystems
-
Strong software engineering background with hands‑on experience in AI frameworks, cloud environments, and domains such as ML, NLP, IR, recommender systems, and LLMs and proven experience with Docker, Kubernetes, and major cloud platforms (AWS/GCP/Azure), including training, fine‑tuning, and applying LLMs for agentic AI applications.
- PhD orMaster's degree in Technology, Computer Science, MachineLearningor equivalent quantitative field
- Familiarityleveraginggraph-based techniques, semantic search, hybrid search systems,and implementing solutions that combine traditional IR methods with machine learning models to enhance search relevancy accuracy and efficiency.Familiarity with large scale data handling when dealing with telemetry systems.