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Causal Inference Machine Learning Postdoctoral Jobs in Michigan

Wayve

Machine Learning Engineer, App SW

Detroit, MI · Hybrid

$283K - $381K/yr

About you: In order to set you up for success as a Machine Learning Engineer at Wayve, we're ... Familiarity with personalization, human behavior modeling, or driver intent inference. * Experience ...

AMC Health

Senior Software Engineer Applied AI

Lansing, MI · On-site

$124K - $163K/yr

... machine learning * End-to-end ML pipelines: feature engineering, model training, and scheduled inference * Imbalanced, messy real-world data; calibration and explainability for non-technical ...

New

Pwc

AI Engineer

Detroit, MI · On-site

$50K - $112K/yr

Certifications aligned to data engineering, machine learning, and cloud platforms, including AWS ... using quantization, inference acceleration, and model-routing techniques - Designing agent ...

Pwc

AI Engineer

Grand Rapids, MI

$50K - $112K/yr

Certifications aligned to data engineering, machine learning, and cloud platforms, including AWS ... using quantization, inference acceleration, and model-routing techniques - Designing agent ...

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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?

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 Michigan?

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

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

The top searched job categories for Causal Inference Machine Learning Postdoctoral jobs in Michigan are:

Causal Inference Machine Learning Postdoctoral jobs near you

AI Machine Learning Engineer (AI / ML: Python / Go)

Benzinga

Detroit, MI • On-site, Remote

Full-time

Re-posted 20 hours ago

Job description

About Benzinga
Benzinga is a fast-growing financial media and data technology company reshaping how investors access information. We combine artificial intelligence, machine learning, and real-time data pipelines to surface insights before they hit the mainstream. Our platforms deliver structured news, sentiment analytics, and financial data APIs used by leading banks, fintechs, and AI companies worldwide.
We're seeking a highly motivated AI / Machine Learning Engineer who thrives at the intersection of data science and backend engineering - someone who can take a model from notebook to production, and architect intelligent systems in Go and Python that scale to millions of requests.
The ideal candidate is a self-starter who independently identifies opportunities, experiments with new approaches, and ships production-ready solutions without constant direction.
Key Responsibilities
AI / Machine Learning
  • Research, design, and deploy machine learning models across NLP, time-series forecasting, and event detection domains.
  • Build LLM-driven systems (e.g. summarization, RAG pipelines, embedding search) optimized for financial news and quantitative data.
  • Develop model serving APIs and scalable inference layers using Go or Python.
  • Implement model monitoring, drift detection, and continuous retraining pipelines.
  • Work with financial text (earnings call transcripts, filings, news) to extract structured insights.
  • Collaborate with data engineers to build training datasets, feature stores, and embedding databases.

Backend & Infrastructure
  • Develop and maintain high-performance Python or Go microservices that integrate with AI systems and Go data APIs.
  • Design and optimize real-time inference pipelines on AWS, leveraging ECS/EKS, S3, and Lambda.
  • Ensure low-latency, fault-tolerant, and scalable delivery of AI-powered data.
  • Implement CI/CD for ML workflows, including containerization, automated deployment, and versioning.
  • Partner with DevOps to manage cloud infrastructure and ensure robust observability for AI workloads.

Requirement for applying:
  • During your screening you will be required to submit a Loom video walkthrough of your most exceptional product, share relevant code/repo links, and describe the biggest challenge you faced building it.

Required Qualification
  • 4+ years of experience in AI/ML or data engineering roles, with a proven track record of deploying ML models in production.
  • Computer science degree (Bachelor minimum)
  • Deep proficiency in Python (data, ML) and Go (backend, microservices).
  • Hands-on experience with ML frameworks such as PyTorch, TensorFlow, or Hugging Face.
  • Experience with transformer architectures, embeddings, or fine-tuning LLMs.
  • Strong understanding of data pipelines, feature extraction, and model lifecycle management.
  • Familiarity with Docker, Kubernetes, and AWS (EKS, S3, Lambda, EC2).
  • Excellent problem-solving skills and ability to work independently in a distributed environment.

Preferred Skills / Experience
  • Startup experience.
  • Financial services or fintech background
  • Experience building LLM-powered APIs or retrieval-augmented generation (RAG) systems.
  • Knowledge of vector databases (e.g., Pinecone, Weaviate, FAISS, OpenSearch kNN).
  • Experience with Kafka, LangChain, or data streaming architectures.
  • Familiarity with financial data systems, real-time analytics, or news NLP.
  • Exposure to MLOps tools (MLflow, BentoML, SageMaker, Airflow, etc.).
  • Contributions to open-source ML or Go projects are a strong plus.

Tech Stack
  • Languages: Python, Go
  • ML Frameworks: PyTorch, TensorFlow, Hugging Face, LangChain
  • Cloud: AWS (EKS, ECS, S3, Lambda, EC2, IAM)
  • Containers & Orchestration: Docker, Kubernetes
  • Data & Streaming: Kafka, Postgres, OpenSearch
  • CI/CD: GitHub Actions, GitLab CI
  • Monitoring: Datadog, Prometheus, Grafana
  • Version Control: Git (Gitlab / Github)

Why Join Benzinga
  • Build and ship production AI systems that shape how financial markets understand information.
  • Operate with full creative freedom - explore, experiment, and execute your ideas end-to-end.
  • Work with a lean, highly technical team where initiative and ownership are celebrated.
  • Fully remote, high-trust environment that rewards curiosity, speed, and execution.
Benzinga logo

About Benzinga

Sourced by ZipRecruiter

Benzinga is a full-service news and media company with three main areas of expertise: real-time news, actionable trading ideas and insightful commentary. We offer coverage of all aspects of the financial market including corporate, economic and political content. With strong connections in and around the market, we strive to provide high quality and relevant news for the real-time environment that defines today's world.

Industry

Video and audio streaming services

Company size

11 - 50 Employees

Headquarters location

Detroit, MI, US

Year founded

2010

Social media

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