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Causal Inference Machine Learning Postdoctoral Jobs in San Juan, PR

All JobsCausal Inference Machine Learning Postdoctoral JobsCausal Inference Machine Learning Postdoctoral Jobs in San Juan, PR

Causal Inference Machine Learning Postdoctoral information

See San Juan, PR salary details

$34.8K

$53.2K

$59.8K

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

As of Aug 5, 2026, the average yearly pay for causal inference machine learning postdoctoral in San Juan, PR is $53,169.00, according to ZipRecruiter salary data. Most workers in this role earn between $52,500.00 and $55,400.00 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.

Causal Inference Machine Learning Postdoctoral jobs near you
Infographic showing various Causal Inference Machine Learning Postdoctoral job openings in San Juan, PR as of June 2026, with employment types broken down into 74% Full Time, 25% Part Time, and 1% Contract. Highlights an 90% Physical, 1% Hybrid, and 9% Remote job distribution, with an average salary of $53,169 per year, or $25.6 per hour.

Senior AI Software Developer

Hewlett Packard Enterprise Development LP

San Juan, PR • Hybrid

$53.50 - $70.50/hr

Full-time

Re-posted 12 hours ago

Job description

Senior AI Software DeveloperThis role has been designed as 'Hybrid' with an expectation that you will work on average 2 days per week from an HPE office.

Who We Are:

Hewlett Packard Enterprise is the global edge-to-cloud company advancing the way people live and work. We help companies connect, protect, analyze, and act on their data and applications wherever they live, from edge to cloud, so they can turn insights into outcomes at the speed required to thrive in today's complex world.Our culture thrives onfinding new and better ways to accelerate what's next.We know varied backgrounds are valued and succeed here. We have the flexibility to manage our work and personal needs.We make bold moves, together, and are a force for good. If you are looking to stretch and grow your career our culture will embrace you.Open up opportunities with HPE.

Job Description:

The Senior AI Engineer owns end-to-end delivery of AI features-from design to production-while raising the engineering bar through code quality, reliability, and mentoring. The engineer will convert architecture into robust implementations, proactively manage risks, and ensure observable, secure, and performant AI systems. Important to have Good Networking knowledge

Responsibilities:
Solution Engineering & Delivery

  • Translate high-level designs into clear component contracts, APIs, and service boundaries.
  • Implement LLM integrations, RAG pipelines, agents, tool/function calling, and prompt strategies.
  • Own feature delivery for sprints/releases; maintain high code quality and documentation.

Modeling & Evaluation

  • Fine-tune models when needed; design evaluation harnesses and metrics.
  • Build A/B testing setups; track accuracy, latency, robustness, and task success rates.
  • Conduct error analysis; iterate using feedback efficacy loops and prompt refinement.

Data & Retrieval Engineering

  • Build ETL/ELT pipelines; curate datasets with metadata, lineage, and validation.
  • Implement vector indexing (chunking, embeddings, reranking), tune chunk size & overlap.
  • Enforce data governance: PII handling, redaction, consent, auditability.

MLOps & Platform Readiness

  • Containerize workloads (Docker); orchestrate deployments (Kubernetes/Helm).
  • Own CI/CD for ML: train evaluate package deploy monitor rollback.
  • Maintain model/agent registries, experiment tracking, and reproducible environments.

Software Engineering & Integration

  • Build microservices and async inference paths; support batch/stream processing.
  • Integrate with enterprise auth, observability, telemetry, and logging.
  • Write unit/integration/e2e tests, performance benchmarks, and failure-injection tests.

Observability, Reliability & Performance

  • Instrument with metrics/logs/traces; define SLOs (latency, throughput, error rate).
  • Optimize inference: batching, caching (KV cache), quantization, token efficiency.
  • Implement guardrails (safety filters, jailbreak detection), auto-evals and alerts.

Security & Compliance

  • Apply secure coding practices; manage secrets, encryption, and least privilege.
  • Ensure compliance (data residency, consent, audit trails); respect IP policies.
  • Enforce policy-based access and content safety in user-facing features.

Collaboration & Mentoring

  • Review designs/PRs; coach L3 engineers on best practices.
  • Coordinate with AI Architects, Data Engineers, QA, and Product.

Education and Experience Required:

  • Bachelor's or master's degree in computer science, engineering, data science, machine learning, artificial intelligence, or closely related quantitative discipline.
  • Typically, 7-10 years' experience.

Knowledge and Skills:

  • LLMs & Agents: Prompt engineering, function/tool calling, orchestration frameworks, RAG.
  • ML/DS: Evaluation metrics (precision/recall, BLEU/ROUGE where relevant), error analysis.
  • Data/RAG: Embeddings, similarity (cosine/IP), chunking, rerankers, vector DB operations.
  • Backend: Python (FastAPI/Flask), microservices patterns.
  • MLOps/Infra: Docker, Kubernetes, CI/CD, artifact management, GPU scheduling.
  • Observability: Metrics/logging/tracing, dashboards, automated evaluation pipelines.
  • Frameworks: PyTorch/TensorFlow, Hugging Face, LangChain/LlamaIndex.
  • Data: Pandas, SQL/NoSQL, Parquet/Arrow, Kafka/queues.
  • Vector DBs: FAISS, Milvus, pgvector, Pinecone, Weaviate.
  • Ops: GitHub Actions/Azure DevOps, MLFlow/W&B

#LI-Hybrid

Additional Skills:

Artificial Intelligence Technologies, Cross Domain Knowledge, Data Engineering, Data Science, Design Thinking, Development Fundamentals, Full Stack Development, IT Performance, Machine Learning Operations, Scalability Testing, Security-First Mindset

What We Can Offer You:

Health & Wellbeing

We strive to provide our team members and their loved ones with a comprehensive suite of benefits that supports their physical, financial and emotional wellbeing.

Personal & Professional Development

We also invest in your career because the better you are, the better we all are. We have specific programs catered to helping you reach any career goals you have - whether you want to become a knowledge expert in your field or apply your skills to another division.

Unconditional Inclusion

We are unconditionally inclusive in the way we work and celebrate individual uniqueness. We know varied backgrounds are valued and succeed here. We have the flexibility to manage our work and personal needs. We make bold moves, together, and are a force for good.

Let's Stay Connected:

Follow @HPECareers on Instagram to see the latest on people, culture and tech at HPE.

#puertorico#networking

Job:

Engineering

Job Level:

TCP_04

HPE is an Equal Employment Opportunity/ Veterans/Disabled/LGBT employer. We do not discriminate on the basis of race, gender, or any other protected category, and all decisions we make are made on the basis of qualifications, merit, and business need. Our goal is to be one global team that is representative of our customers, in an inclusive environment where we can continue to innovate and grow together. Please click here: Equal Employment Opportunity.

Hewlett Packard Enterprise is EEO Protected Veteran/ Individual with Disabilities.

HPE will comply with all applicable laws related to employer use of arrest and conviction records, including laws requiring employers to consider for employment qualified applicants with criminal histories.

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It has come to HPE's attention that there has been an increase in recruitment fraud whereby scammer impersonate HPE or HPE-authorized recruiting agencies and offer fake employment opportunities to candidates. These scammers often seek to obtain personal information or money from candidates.

Please note that Hewlett Packard Enterprise (HPE), its direct and indirect subsidiaries and affiliated companies, and its authorized recruitment agencies/vendorswill never charge any candidate a registration fee, hiring fee, or any other fee in connection with its recruitment and hiring process.The credentials of any hiring agency that claims to be working with HPE for recruitment of talent should be verified by candidates and candidates shall be solely responsible to conduct such verification. Any candidate/individual who relies on the erroneous representations made by fraudulent employment agencies does so at their own risk, and HPE disclaims liability for any damages or claims that may result from any such communication.

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