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Causal Inference Machine Learning Postdoctoral Jobs in Oregon

Anno.ai

OR

$104K - $143K/yr

Evaluate and integrate emerging MLOps, distributed training, and edge inference technologies to ... software engineering, machine learning engineering, MLOps, or related roles * Experience ...

Netflix

OR · On-site

Background in neural networks, natural language processing, or causal inference Contributions to open-source projects in machine learning or related fields. Experience working with cross functional ...

phData

OR · On-site

We are looking for a Machine Learning Architect to join our Machine Learning team. In this role ... inference, retraining, monitoring, and lifecycle management from the application layer down to ...

Cresta

OR · On-site

$104K - $143K/yr

About the role We are looking for a Senior Machine Learning Engineer, Voice Experience to help ... Partner with platform and backend engineers to productionize real-time inference, streaming ...

Hitachi

Data Scientist I or II (MAD-BS-OR)

Hillsboro, OR · On-site +1

$121K - $167K/yr

PRIMARY RESPONSIBILITIES * Hands-on development and write algorithms in machine learning ... Graph-based reasoning or causal inference * Full software development lifecycle experience, must be ...

Netflix

OR

$372K - $600K/yr

... causal inference, and modeling, but also through exceptional judgment, partnership, and business ... Machine Learning Scientists, and Research Engineers. You will use data to identify opportunities ...

CookUnity

OR · On-site

Design and implement robust experimentation frameworks that enable rapid, high-quality product testing and learning * Develop causal inference methodologies to understand true incrementality of ...

Near AI

LLM Inference Engineer

OR · On-site +1

... machine learning infrastructure to enable user-owned AI. Our mission is to build highly scalable ... We are specifically seeking an expert in high-performance LLM serving systems and inference ...

Instacart

Ads AI Analytics Lead II

OR · Remote

Strong understanding of AI and machine learning concepts, with experience creating AI-driven ... and causal inference. * Hands-on experience with workflow automation and low-code development ...

Netflix

OR

$466K - $750K/yr

We are looking for an experienced Machine Learning Engineer with deep expertise in training and ... Scale model training and inference into robust, performant systems integrated into Netflix ...

phData

OR · On-site

We are looking for a Principal Solutions Architect to join our Machine Learning team. In this role ... inference, retraining, and monitoring through to production operations. * Translate business and ...

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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, and why are they important?

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?

AspectCausal Inference Machine Learning PostdoctoralData Scientist
Required CredentialsPhD in statistics, machine learning, or related fieldBachelor's or Master's in data science, computer science, or related field
Work EnvironmentAcademic research, research labs, universitiesCorporate, tech companies, startups
Industry UsageResearch, academia, specialized industry projectsBusiness analytics, product development, data-driven decision making
Common Search/ComparisonYesYes

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.

What are popular job titles related to Causal Inference Machine Learning Postdoctoral jobs in Oregon? For Causal Inference Machine Learning Postdoctoral jobs in Oregon, the most frequently searched job titles are:
What job categories do people searching Causal Inference Machine Learning Postdoctoral jobs in Oregon look for? The top searched job categories for Causal Inference Machine Learning Postdoctoral jobs in Oregon are:
What cities in Oregon are hiring for Causal Inference Machine Learning Postdoctoral jobs? Cities in Oregon with the most Causal Inference Machine Learning Postdoctoral job openings:
Causal Inference Machine Learning Postdoctoral jobs near you
Senior Machine Learning Engineer

Senior Machine Learning Engineer

Anno.ai

OR

$104K - $143K/yr

Other

Posted 29 days ago

Job description

Disclaimer: Due to the sensitive nature of our engineering work, Anno.ai enforces strict digital footprint and identity verification. We actively monitor for synthetic profiles, proxy networks, and AI interview assistants; any fraudulent activity will result in immediate disqualification.  

Position Overview 

As a Senior Machine Learning Engineer at Anno.ai, you will design, develop, test, document, deploy, and maintain production machine learning and statistical modeled software to automate processes and streamline our customer's mission operations. MLEs work directly with product, user-facing, hardware, and platform teams to deliver the highest quality products. You will join a team of beasts known as "Annomals" are notable for their practical, mission-driven, and fun demeanor. MLEs work directly with product, user-facing, hardware, and platform teams to deliver the highest quality products, and because of these diverse interfaces, we value good, seasoned judgment in your approach to management, your career growth, and maintaining ethical and responsible practices.  

For this opportunity we are looking for MLEs who have a fairly uniform distribution of talent across a breadth the range of machine learning tasks and skills. You are an experienced MLE, part solid software engineer, and part modeling expert. You have been through the trenches and bring key knowledge and intuition through your combination of training and experience.  

Candidates need to be able to obtain and maintain U.S. Government security clearance (U.S. citizenship required).  Candidates must be able to travel up to 20% of the time. 

What You Will Do 

  • Operationalize machine learning models by building and maintaining robust, scalable pipelines for training, evaluation, deployment, and lifecycle management across cloud, on-prem, and edge compute environments
  • Work closely with autonomy researchers, software engineers, systems teams, and field operators to translate mission requirements into deployable ML capabilities
  • Implement automated CI/CD workflows tailored to ML systems, ensuring repeatable experiments, reliable packaging, and continuous delivery of both up to date models and associated data pipelines
  • Manage ML runtime infrastructure using containerization and orchestration frameworks (e.g., Docker, Kubernetes) and incorporating model serving platforms (e.g., Seldon, KServe, BentoML)
  • Develop monitoring systems to track model health, performance, data drift, system utilization, and mission relevance using tools such as Prometheus, Grafana, and ELK/EFK stacks
  • Ensure ML deployments meet defense, customer, and platform security requirements, with emphasis on data integrity, traceability, and operational reliability
  • Evaluate and integrate emerging MLOps, distributed training, and edge inference technologies to enhance reproducibility, extensibility, scalability, and deployment speed of ML systems 

Required Qualifications 

  • Bachelor's degree in Computer Science, Electrical Engineering, Data Science, or a related technical field (Master's preferred)
  • 5+ years of professional experience in software engineering, machine learning engineering, MLOps, or related roles
  • Experience operationalizing ML systems at production scale, including model training, versioning, packaging, deployment, and monitoring
  • Strong proficiency in Python and familiarity with at least one deep learning framework (e.g., PyTorch, TensorFlow)
  • Hands-on experience with MLOps frameworks and workflow tooling (e.g., MLflow, Kubeflow, Airflow, DVC, BentoML)
  • Experience deploying containerized ML services using Docker and orchestrating workloads using Kubernetes (including air-gapped or constrained deployments)
  • Understanding of CI/CD workflows and DevOps practices applied to ML systems (e.g., Git, Code Review, Metrics Evaluation)
  • Familiarity with monitoring, observability, and logging platforms (e.g., Prometheus, Grafana, ELK/EFK)
  • Ability to obtain and maintain U.S. Government security clearance (U.S. Citizenship required)
  • Ability to travel up to 20% 

Preferred Qualifications 

  • Experience with deploying models and associated runtimes to Edged Devices
  • Experience optimizing models for memory and CPU constrained systems (e.g., embedded systems, microcontrollers)
  • Prior experience supporting U.S. Department of War programs, cUAS systems, or mission-critical autonomous platforms
  • Experience working with diverse or atypical data sources (e.g., Audio/Acoustics, RF signals, EO/IR imagery)
  • Experience deploying and optimizing ML inference on edge or resource-limited compute systems
  • Experience with Explainable/Auditable AI/ML tools and interpretable model design
  • Experience with AI Software Development Tools (e.g., GitHub CoPilot, Claude) 

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