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Causal Inference Machine Learning Postdoctoral Jobs in Birmingham, AL

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Causal Inference Machine Learning Postdoctoral information

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$33.3K

$50.8K

$57.2K

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

As of Sep 14, 2026, the average yearly pay for causal inference machine learning postdoctoral in Birmingham, AL is $50,817.00, according to ZipRecruiter salary data. Most workers in this role earn between $50,100.00 and $53,000.00 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 Birmingham, AL?

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

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

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

Causal Inference Machine Learning Postdoctoral jobs near you
Infographic showing various Causal Inference Machine Learning Postdoctoral job openings in Birmingham, AL as of August 2026, with employment types broken down into 1% As Needed, 73% Full Time, 22% Part Time, 1% Temporary, and 3% Contract. Highlights an 88% Physical, 2% Hybrid, and 10% Remote job distribution, with an average salary of $50,817 per year, or $24.4 per hour.

Search Engineer (Software Engineer III)

Birmingham, AL • On-site

Seneca Resources Company, LLC
IT Services • 51 - 200 employees
Learn more about Seneca Resources Company, LLCexternal

$130K/yr

Full-time

Medical, Dental, Vision, Retirement

Re-posted 19 days ago

Job description

Position Title: Full Stack Engineer - AI Search & Product Discovery
Location: Remote
Position Status: Full Time
About the Role
We're transforming how millions of industrial buyers discover products through intelligent search experiences powered by AI, machine learning, and modern search technologies.
As a Full Stack Engineer III on our AI Search & Product Discovery team, you'll build the frontend applications, backend services, and search infrastructure that power highly relevant, personalized search experiences at scale. You'll work across the full development lifecycle, from rapid prototyping and experimentation to deploying production-grade solutions and continuously optimizing performance based on real user behavior.
This is an ideal opportunity for an engineer who enjoys solving complex problems at the intersection of full-stack development, search engineering, cloud-native architecture, machine learning, and AI-driven user experiences.
What You'll Do
Build AI-Powered Search Experiences
  • Design, develop, and deploy scalable Python services that power search retrieval, ranking, recommendation, and discovery experiences.
  • Build and integrate machine learning inference pipelines, including embeddings, transformer models, query understanding, reranking services, and LLM-powered features.
  • Develop robust APIs and microservices that support high-volume search workloads and customer-facing applications.
  • Collaborate with product, engineering, and AI teams to translate business goals into impactful search solutions.
  • Continuously improve search relevance, performance, and user engagement through experimentation and data-driven decision making.

Develop Modern Full-Stack Applications
  • Build responsive, accessible, and performant user interfaces using React and modern JavaScript frameworks.
  • Create reusable frontend components and establish engineering standards that improve consistency and developer productivity.
  • Partner with UX and Product teams to transform wireframes and Figma designs into intuitive customer experiences.
  • Contribute to frontend architecture decisions that support long-term scalability and maintainability.

Advance Search & AI Infrastructure
  • Implement hybrid search architectures that combine keyword search, vector search, semantic retrieval, and AI-powered ranking.
  • Build and maintain Elasticsearch/OpenSearch indexing pipelines, query services, and relevance tuning capabilities.
  • Integrate vector databases and retrieval systems such as Pinecone, Weaviate, FAISS, or similar technologies.
  • Develop and optimize Retrieval-Augmented Generation (RAG) and LLM-powered search workflows.
  • Instrument search systems with meaningful metrics, including click-through rate, latency, engagement, and zero-result rates to drive ongoing optimization.

Build Cloud-Native Systems
  • Develop event-driven, distributed systems using Google Cloud Platform (GCP) services such as Cloud Run, GKE, Pub/Sub, and Cloud Functions.
  • Deploy, monitor, and maintain production services using modern DevOps and observability practices.
  • Own service reliability through testing, monitoring, troubleshooting, and operational excellence.

Contribute to Engineering Excellence
  • Participate in architecture discussions, technical design reviews, and code reviews.
  • Mentor peers through collaboration and knowledge sharing.
  • Champion engineering best practices, software craftsmanship, and continuous improvement.

Required Qualifications
  • 4+ years of professional experience in Full Stack Engineering, Backend Engineering, or Software Development.
  • Strong hands-on experience building applications with Python and React.
  • Experience with modern frontend frameworks such as Next.js, Remix, Vite, Gatsby, or similar technologies.
  • Proven experience building and supporting scalable microservices, REST APIs, and/or gRPC services.
  • Experience deploying and operating cloud-native applications in GCP, AWS, or Azure.
  • Experience with Docker, containerized applications, and serverless architectures.
  • Strong understanding of software design patterns, SOLID principles, testing strategies, and maintainable code practices.
  • Experience working with relational and NoSQL databases such as PostgreSQL, MySQL, Oracle, MongoDB, DynamoDB, or similar platforms.
  • Strong communication skills with the ability to collaborate effectively across engineering, product, and architecture teams.
  • Comfortable leveraging AI-assisted development tools to improve productivity and quality.

Preferred Qualifications
Search Engineering
  • Elasticsearch, OpenSearch, Solr, Algolia, or other enterprise search platforms.
  • Search relevance tuning, query optimization, indexing strategies, and ranking algorithms.
  • Large-scale search infrastructure and information retrieval systems.

AI & Machine Learning
  • Generative AI, Large Language Models (LLMs), and AI-powered search experiences.
  • Retrieval-Augmented Generation (RAG).
  • Prompt engineering and LLM orchestration.
  • LangChain, LangGraph, Google ADK, or similar frameworks.
  • Machine learning model deployment and inference pipelines.

Vector Search & Semantic Retrieval
  • Embeddings and semantic search architectures.
  • Approximate Nearest Neighbor (ANN) search.
  • Pinecone, Weaviate, FAISS, Milvus, or similar vector database technologies.

Additional Experience
  • Monorepo development environments.
  • Event-driven architectures.
  • Distributed systems at scale.
  • A/B testing and experimentation frameworks.

About Seneca Resources:
At Seneca Resources, we are more than just a staffing and consulting firm, we are a trusted career partner. With offices across the U.S. and clients ranging from Fortune 500 companies to government organizations, we provide opportunities that help professionals grow their careers while making an impact.
When you work with Seneca, you're choosing a company that invests in your success, celebrates your achievements, and connects you to meaningful work with leading organizations nationwide. We take the time to understand your goals and match you with roles that align with your skills and career path. Our consultants and contractors enjoy competitive pay, comprehensive health, dental, and vision coverage, 401(k) retirement plans, and the support of a dedicated team who will advocate for you every step of the way.

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