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Remote Retrieval Augmented Generation Jobs in Boston, MA

... a modern, AI-augmented growth team operates. This role offers full remote flexibility for ... Plan and execute end-to-end demand generation campaigns across digital and offline channels ...

... a modern, AI-augmented growth team operates. This role offers full remote flexibility for ... Plan and execute end-to-end demand generation campaigns across digital and offline channels ...

Evaluate and apply AI-assisted testing techniques, such as LLM-based test generation and ... Remote within the United States. This role requires 100% of work to be performed in a remote office ...

Our next-generation autonomous driving stack depends on finding the rare edge cases, long-tail ... and data retrieval. By building smarter mining tools and efficient data pipelines, you will ...

Our next-generation autonomous driving stack depends on finding the rare edge cases, long-tail ... and data retrieval. By building smarter mining tools and efficient data pipelines, you will ...

Showing results 21-31

Remote Retrieval Augmented Generation information

What skills and qualifications are needed to thrive as a remote retrieval augmented generation engineer?

To thrive as a Remote Retrieval Augmented Generation (RAG) Engineer, you need a strong background in machine learning, natural language processing, and information retrieval, often backed by a degree in computer science or a related field. Familiarity with tools and frameworks like PyTorch, TensorFlow, Hugging Face Transformers, and experience with retrieval systems such as Elasticsearch or FAISS are typically required. Problem-solving, effective communication, and adaptability are important soft skills for collaborating remotely and iterating on rapidly evolving AI solutions. These skills ensure the engineer can design, deploy, and optimize robust RAG systems that effectively combine retrieval and generation for high-quality AI outputs.

What is the difference between Remote Retrieval Augmented Generation vs Remote Data Scientist?

AspectRemote Retrieval Augmented GenerationRemote Data Scientist
CredentialsAI/ML knowledge, programming skillsStatistics, programming, domain expertise
Work EnvironmentAI development, NLP projectsData analysis, model building
Industry UsageAI, NLP, machine learningTech, finance, healthcare
Search & ComparisonOften compared for AI roles involving language modelsCompared for data analysis roles

Remote Retrieval Augmented Generation focuses on developing AI models that combine retrieval techniques with language generation, requiring expertise in AI, NLP, and programming. Remote Data Scientists analyze data, build models, and interpret results, often with statistical and domain knowledge. While both roles may work remotely and involve data handling, Retrieval Augmented Generation emphasizes AI model development, whereas Data Scientists focus on data analysis and insights.

What are common challenges faced by professionals working in remote retrieval augmented generation roles, and how can they be addressed?

Professionals in Remote Retrieval Augmented Generation (RAG) roles often encounter challenges related to integrating diverse data sources, ensuring low latency in information retrieval, and maintaining the quality and relevance of augmented outputs. Coordinating effectively with distributed teams and adapting to rapidly evolving AI technologies are also common hurdles. To address these, staying current with best practices in data engineering, leveraging robust APIs, and participating in regular team check-ins can help ensure smooth collaboration and system performance.

What is remote retrieval augmented generation?

Remote Retrieval Augmented Generation (RAG) is an advanced AI technique that combines large language models with external information sources. In a remote RAG setup, the model retrieves relevant data from remote databases or APIs during the generation process, enhancing its responses with up-to-date or domain-specific knowledge. This approach is widely used in applications that require accurate, context-aware answers, such as chatbots, search engines, and virtual assistants. By leveraging remote retrieval, RAG systems can access a broader range of information without needing to store all data locally.
What are the most commonly searched types of Retrieval Augmented Generation jobs in Boston, MA? The most popular types of Retrieval Augmented Generation jobs in Boston, MA are:
What are popular job titles related to Remote Retrieval Augmented Generation jobs in Boston, MA? For Remote Retrieval Augmented Generation jobs in Boston, MA, the most frequently searched job titles are:
What job categories do people searching Remote Retrieval Augmented Generation jobs in Boston, MA look for? The top searched job categories for Remote Retrieval Augmented Generation jobs in Boston, MA are:
What cities near Boston, MA are hiring for Remote Retrieval Augmented Generation jobs? Cities near Boston, MA with the most Remote Retrieval Augmented Generation job openings:
Infographic showing various Remote Retrieval Augmented Generation job openings in Boston, MA as of August 2026, with employment types broken down into 70% Full Time, 27% Part Time, and 3% Contract. Highlights an 71% Physical, 3% Hybrid, and 26% Remote job distribution.

Senior / Staff Documentation Engineer (AI & Docs Tooling)

TetraScience

Boston, MA • On-site, Remote

$150K - $210K/yr

Full-time

Life, Retirement, PTO

Re-posted 19 days ago


Job description

About TetraScience

TetraScience is the Scientific Data and AI Company building Tetra OS, the operating system for scientific intelligence. We help the world's leading life sciences firms turn fragmented scientific data into AI-native assets and scientific workflows that accelerate discovery, development, and manufacturing. TetraScience's growing ecosystem of strategic partners includes NVIDIA, Databricks, Thermo Fisher Scientific, Snowflake, Google, and Microsoft.

In connection with your candidacy, you will be asked to carefully review "The Tetra Way," authored by our CEO, Patrick Grady; it is impossible to overstate the importance of this document, and you should take it literally as you decide whether our mission, culture, and expectations are right for you.

The Role

TetraScience is the scientific data and AI company. Our documentation is how customers, from bench scientists to platform engineers, learn to build on the platform, and increasingly it is how AI agents consume the platform too. We are looking for a Documentation Engineer to own documentation as a system: the pipelines that build and publish it, the AI-augmented workflows that generate drafts for human review and refinement, the review and publish process, and the infrastructure that makes it reliably consumable by AI agents.

This is primarily a documentation systems role, not only a writer who uses tools. The differentiator is building and owning the systems that produce, validate, publish, and AI-enable our documentation. Strong writing and editorial judgment are still required, but the center of gravity is tooling and systems, and a large portion of the day to day is building.

You will lead, not just maintain. You will take our existing docs-as-code foundation and AI-assisted documentation workflows and grow them into a docs-as-AI-agents capability that is differentiated for a life-sciences AI platform. You will still own editorial quality and the release-notes cadence, but you will spend most of your time building leverage rather than absorbing work.

Own the documentation site and its publishing as software: the docs-as-code repo, the CI/CD publishing pipelines, build performance, and automated link, structure, and quality checks.

Build and grow AI-augmented documentation workflows: AI-assisted drafting, summarization, classification, consistency and staleness checks, and a feedback loop that improves generation quality over time, all with human oversight.

Build our docs-as-AI-agents position: structure and transform content so AI systems can reliably chunk, index, and reason over it, and stand up and maintain MCP-style interfaces so agents and assistants consume our docs accurately.

Generate reference documentation from source (OpenAPI and related specs) and keep docs in lockstep with the platform as code changes.

Lower the barrier for internal contributors (PMs, squad leads, engineers) to ship their own docs through the docs-as-code workflow, and reduce repetitive work through automation.

Own the release-notes and customer-communications cadence that goes out with every platform release, and run the SME review that keeps it accurate and on time.

Own the documentation style guide, hold the review-and-publish gate, and keep the team runbook current so the function is not dependent on any one person.

Requirements

Basics Requirements
  • 5+ years owning documentation tooling, content engineering, or developer documentation for a developer-platform or enterprise B2B product.
  • Engineering ability in a scripting or web stack (for example Python, TypeScript, or JavaScript) and real fluency with docs-as-code: Git, pull-request review, CI/CD, and a static-site or CMS publishing pipeline.
  • Hands-on experience building AI-augmented or LLM-backed workflows: integrating LLM APIs, AI-assisted authoring, and structuring content for AI consumption.
  • Ability to read and reason about a real codebase and API surface well enough to document it accurately and to build tooling against it.
  • Strong editorial judgment: you can take a dense engineering change and make it clear, correct, and customer-safe.
  • Bachelors or Masters degree in a technical field, or equivalent practical experience.
Preferred Requirements
  • Experience making documentation consumable by AI agents (llms.txt, content negotiation, RAG pipelines, MCP servers)
  • Experience in BioPharma or scientific software, or in regulated and validated (GxP) environments.
  • Experience generating reference docs from OpenAPI or related specifications with two-way Git sync.
  • Developer-relations or developer-education exposure.

Benefits

US Benefits

  • 100% employer-paid benefits for all eligible employees and immediate family members
  • Unlimited paid time off (PTO)
  • 401K
  • Flexible working arrangements - Remote work 
  • Company paid Life Insurance, LTD/STD
  • A culture of continuous improvement where you can grow your career and get coaching
  • The salary range for this position is $150K-$210K USD. The salary range posted reflects our target baseline for this role. Final compensation is determined by a thorough evaluation of factors including the candidate's specific experience, localized market data, and internal team equity.

We are not currently providing visa sponsorship for this position