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Causal Inference Machine Learning Postdoctoral Jobs in Pittsburgh, PA

Apple

Machine Learning Engineer

Pittsburgh, PA · On-site

As a Machine Learning Engineer in the Machine Intelligence Neural Design (MIND) team, you'll have ... inference integration, and on-device model optimizations. Our ideal team member is fearless in ...

Apple

Machine Learning Engineer

Pittsburgh, PA

As a Machine Learning Engineer in the Machine Intelligence Neural Design (MIND) team, you'll have ... inference integration, and on-device model optimizations. Our ideal team member is fearless in ...

Cmu

Associate Data Scientist

Pittsburgh, PA · On-site

$57K - $57K/yr

Data Scientists at the SEI use advanced statistics, data analytics, machine learning, and ... Causal inference / uplift modeling / synthetic controls * Modern ML frameworks: LightGBM/XGBoost ...

Cmu

Data Scientist

Pittsburgh, PA · On-site

Data Scientists at the SEI use advanced statistics, data analytics, machine learning, and ... Causal inference / uplift modeling / synthetic controls * Modern ML frameworks: LightGBM/XGBoost ...

Cmu

Senior Data Scientist

Pittsburgh, PA · On-site

Data Scientists at the SEI use advanced statistics, data analytics, machine learning, and ... Causal inference / uplift modeling / synthetic controls * Modern ML frameworks: LightGBM/XGBoost ...

Gather AI

Director of Machine Learning

Pittsburgh, PA · On-site

They are seeking a Director of Machine Learning to define the ML strategy, lead the computer vision ... embedded inference) • Familiarity with warehouse, logistics, or supply chain domain • ...

Gather AI

Director of Machine Learning

Pittsburgh, PA · On-site +1

About the Team You'll lead the Machine Learning and FPT teams, working closely with the Director of ... Edge ML deployment experience (ONNX, TensorRT, mobile/embedded inference) * Familiarity with ...

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Causal Inference Machine Learning Postdoctoral information

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$34.5K

$52.6K

$59.2K

How much do causal inference machine learning postdoctoral jobs pay per year?

As of Aug 25, 2026, the average yearly pay for causal inference machine learning postdoctoral in Pittsburgh, PA is $52,641.00, according to ZipRecruiter salary data. Most workers in this role earn between $51,900.00 and $54,900.00 per year, depending on experience, location, and employer.

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?

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.

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 Pittsburgh, PA?

For Causal Inference Machine Learning Postdoctoral jobs in Pittsburgh, PA, the most frequently searched job titles are:

What job categories do people searching Causal Inference Machine Learning Postdoctoral jobs in Pittsburgh, PA look for?

The top searched job categories for Causal Inference Machine Learning Postdoctoral jobs in Pittsburgh, PA are:

What cities near Pittsburgh, PA are hiring for Causal Inference Machine Learning Postdoctoral jobs?

Cities near Pittsburgh, PA with the most Causal Inference Machine Learning Postdoctoral job openings:

Causal Inference Machine Learning Postdoctoral jobs near you

Machine Learning Engineer

Apple

Pittsburgh, PA • On-site

Full-time

Posted 20 days ago

Apple rating

8.0

Company rating: 8.0 out of 10

Based on 677 frontline employees who took The Breakroom Quiz

7th of 30 rated technology retailers

Job description

As a Machine Learning Engineer in the Machine Intelligence Neural Design (MIND) team, you'll have the opportunity to be part of a leading ML innovation organization that develops a wide range of impactful Apple features. Within MIND, you'll engage in cutting-edge research in fields such as Foundation Models and Perception, and collaborate to create end products that have high impact on Apple users. This role places a strong emphasis on shipping ML-based features and products. You'll be involved in the entire end-to-end ML development pipeline, which encompasses creative approaches to dataset curation, model training, runtime inference integration, and on-device model optimizations. Our ideal team member is fearless in exploring new ideas and is willing to iterate on concepts. We value team members who can swiftly prototype and iterate, ultimately resulting in high-quality implementations.
Description
Our team seeks a self-driven machine learning engineer with strong experience in building ML training pipelines and developing production-quality inference infrastructure. In this role, you are expected to collaborate closely with ML researchers, SW/FW engineers, and Operation/Data engineers to advance different research and production efforts. Your role is to help deliver the needed pipeline for model development and evaluation, as well as build the production software to integrate these models and related functionality within Apple's software infrastructure. In addition, as part of the development process you are expected to build real-time demos and visualizations of sensing data streams and model predictions.
Minimum Qualifications
A PhD in computer science, computer engineering, or relevant Fields. Alternately, a BS or an MS + 3 to 5 years of ML engineering experience also qualifies.
Strong foundation in machine learning, and more specifically in LLM and multimodal foundation models.
Experience in building model training/eval pipelines in Python/PyTorch.
Experience in prototyping and developing software applications (preferably in Swift).
Experience with sensors and sensing systems.
Strong communication and presentation skills.
* Ability to work in a collaborative environment.
Preferred Qualifications
Proficient programming skills in Python and one of the deep learning toolkits such as JAX, PyTorch, or TensorFlow.
Experience in on-device ML model deployment and on-device optimization.
Experience handling multimodal data including text, images, audio, and other sensors.
Experience in developing production software.
Proficient in Swift, Objective-C, C++, or Go

What Apple employees say

Pay

Benefits

Hours and flexibility

Workplace

Get the full story on Breakroom

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About Apple

Sourced by ZipRecruiter

Imagine what you could do here! At Apple, new ideas have a way of becoming extraordinary products, services, and customer experiences very quickly. Bring passion and dedication to your job and there's no telling what you could accomplish. Dynamic, intelligent people and inspiring, innovative technologies are the norm here. The people who work here have reinvented entire industries with all Apple Hardware products. The same real passion for innovation that goes into our products also applies to our practices strengthening our dedication to leave the world better than we found it.

Industry

Computer and electronic product manufacturing

Company size

10,000+ Employees

Headquarters location

Cupertino, CA, US

Year founded

1976

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