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Remote Retrieval Augmented Generation Jobs in Oregon

Smarsh

Senior Software Engineer, Python + AI Platform

OR · On-site +1

$121K - $163K/yr

Experience with retrieval-augmented generation (RAG), vector search, and embedding-based systems is required (not a plus). * Security and compliance mindset. Experience with multi-tenant systems ...

G-P

Fullstack AI Engineer

OR · On-site +1

$117K - $147K/yr

Experience with Retrieval-Augmented Generation (RAG) architectures and prompt engineering patterns. * Proven track record of building and deploying scalable, production-grade applications. * Ability ...

Responsive

Solution Consultant

OR · On-site +1

$125K - $155K/yr

Design and deliver credible AI proofs-prompting strategies, retrieval-augmented generation (RAG ... This U.S. based role is remote and is not eligible for visa sponsorship. In addition to a ...

Upstart

Staff Machine Learning Model Risk Specialist

OR · On-site +1

$98K/yr

... retrieval-augmented generation, tool use, guardrails, and ongoing monitoring. * Experience ... Remote Travel requirements As a digital first company, the majority of your work can be ...

Care Access

Senior Engineer, Test Automation

OR · On-site +1

$130K - $180K/yr

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 ...

Instacart

Ads AI Analytics Lead II

OR · On-site +1

The team focuses on intelligence generation, predictive analytics, and workflow automation to ... Design and evaluate retrieval workflows (RAG) with existing services for hybrid search and ...

Cresta

Lead Engineer - AI Agent Voice Experience

OR · On-site +1

... generation of AI-powered voice systems for the contact center. In this role, you will work at the ... Solid understanding of transformer-based models, embeddings, retrieval systems, and large-scale ...

Arbital Health

AI Engineer

OR · On-site +1

$155K - $180K/yr

We are looking for an AI Engineer to help build our next-generation conversational AI experiences ... We will also consider highly qualified remote candidates who can travel to San Francisco for in ...

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Remote Retrieval Augmented Generation information

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 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 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 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 the most commonly searched types of Retrieval Augmented Generation jobs in Oregon?

The most popular types of Retrieval Augmented Generation jobs in Oregon are:

What are popular job titles related to Remote Retrieval Augmented Generation jobs in Oregon?

For Remote Retrieval Augmented Generation jobs in Oregon, the most frequently searched job titles are:

What job categories do people searching Remote Retrieval Augmented Generation jobs in Oregon look for?

The top searched job categories for Remote Retrieval Augmented Generation jobs in Oregon are:

What cities in Oregon are hiring for Remote Retrieval Augmented Generation jobs?

Cities in Oregon with the most Remote Retrieval Augmented Generation job openings:

Remote Retrieval Augmented Generation jobs near you
Infographic showing various Remote Retrieval Augmented Generation job openings in Oregon as of August 2026, with employment types broken down into 67% Full Time, 31% Part Time, 1% Contract, and 1% Nights. Highlights an 64% Physical, 3% Hybrid, and 33% Remote job distribution.

Senior Software Engineer, Python + AI Platform

Smarsh

OR • On-site, Remote

$121K - $163K/yr

Full-time

Posted 20 days ago

Job description

Who are we?

Smarsh empowers its customers to manage risk and unleash intelligence in their digital communications. Our growing community of over 6500 organizations in regulated industries counts on Smarsh every day to help them spot compliance, legal or reputational risks in 80+ communication channels before those risks become regulatory fines or headlines.  Relentless innovation has fueled our journey to consistent leadership recognition from analysts like Gartner and Forrester, and our sustained, aggressive growth has landed Smarsh in the annual Inc. 5000 list of fastest-growing American companies since 2008.

Smarsh is hiring a senior backend/platform engineer to build and scale agentic AI systems for enterprise use. You will build fast-moving, early-stage Python services that integrate AI capabilities into a production agentic platform. Your scope spans workflow execution, scale, reliability, and platform hardening as we grow.

This is not a generic backend role. The focus is building and designing agentic systems, shipping working software, and solving hard platform problems in a fast-moving AI-native environment. You will join a small, high-velocity cross-functional group and own problems end to end, designing and building from scratch, making fast architectural calls, and driving ideas from whiteboard to working system with a small, high-agency team.

What will you do?
  • Drive backend development for AI workflows as part of a collaborative team. Build and evolve Python/FastAPI services powering core agentic workflows and platform capabilities.
  • Productionize LLM integrations. Implement systems around Bedrock usage, quotas, retries, failover, cost controls, model configuration, and approval constraints.
  • Design for security and compliance. Address customer data handling, tenant isolation, auditability, observability, and secure processing for regulated workloads. Apply auditable data design patterns to ensure AI outputs are traceable, reproducible, and built to withstand regulatory scrutiny.
  • Build for scale. We're a nimble team, but our enterprise customers process data at petabyte scale. Help the platform grow to meet that bar through async job orchestration, performance tuning, and data-layer optimization.
  • Support multi-tenant architecture. Contribute to tenant-aware services, role-based access, SSO integration, and admin/reporting capabilities.
  • Improve platform reliability. Add monitoring, tracing, alerting, and operational tooling for LLM pipelines, workflow execution, and report generation.
  • Build real-time capabilities. Design and implement real-time event delivery and pub/sub patterns to support live workflow state, notifications, and agent feedback loops.
  • Contribute to technical decisions. Partner on shared services decisions, platform architecture, and integration boundaries across the stack.
  • Work across ambiguity. Translate evolving product requirements and non-functional requirements into practical technical solutions with product, architecture, legal, and security stakeholders.
  • Champion code quality. Drive strong typing, automated testing, and continuous integration practices that keep the team fast and safe.
  • Design typed API contracts. Own the API surface as a product contract: designing clean, schema-driven APIs that support typed client generation and reliable integration across services.

What will you bring?
  • Strong Python backend engineering. 7+ years professional software development, including 5+ years building Python services in production. Deep experience with APIs, async processing, background jobs, and workflow orchestration.
  • Cloud-native backend experience. AWS experience, ideally with services relevant to secure enterprise workloads (compute, storage, networking, CI/CD, identity, secrets, encryption).
  • Production distributed systems. Proven ability to productionize complex backend systems with reliability, observability, retries, throughput, failure handling, and performance tuning.
  • Data-intensive system design. Strong knowledge of PostgreSQL, large-scale data processing patterns, indexing, query tuning, and batch/stream tradeoffs. Experience with retrieval-augmented generation (RAG), vector search, and embedding-based systems is required (not a plus).
  • Security and compliance mindset. Experience with multi-tenant systems, RBAC, audit logging, secure data handling, and regulated environments.
  • Strong ambiguity handling. Ability to work from partial requirements and shape implementation around product and non-functional requirement constraints.
  • Agentic workflow engineering. Hands-on experience building LLM-driven workflows: tool-calling, state machines, human-in-the-loop approval patterns, checkpoint/resume, and multi-step agent orchestration. Familiarity with frameworks like LangGraph or equivalent.
  • AI-native engineering. Experience working on or alongside AI-native engineering teams, where AI agents are first-class participants in the development workflow, not just productivity tools. Includes hands-on prompt engineering, eval design, and LLM cost optimization: caching strategies, token efficiency, and model selection tradeoffs.
  • Product mindset. Bias for shipping, learning from real usage, and making pragmatic tradeoffs grounded in customer problems.
Strong Pluses
  • LLM / AI platform experience. Bedrock, OpenAI, Anthropic, LangChain/LangGraph, prompt workflows, evals, tool-calling systems. Experience integrating external AI services safely and reliably.
  • Identity and access. SSO/SAML/OIDC, enterprise auth patterns.
  • Graph-shaped data and entity resolution. Experience with graph-backed data models, entity deduplication, mention linking, and building systems that reason over connected, structured records.
  • Observability stack. OpenTelemetry, tracing, metrics, alerting, cost/usage dashboards.
  • Regulated communications or compliance domain. Background in systems that handle sensitive communications, audit trails, or data subject to legal or regulatory review is a meaningful differentiator.
  • Infrastructure as code. Terraform, feature flags, canary deployments, release strategies.
$195,000 - $260,000 a year
The salary range above represents Smarsh's good faith and reasonable estimate of the range of possible base compensation at the time of posting. Any applicable bonus programs will be discussed during the recruiting process.
 
The salary for this role will be set based on a variety of factors, including but not limited to, internal equity, experience, education, location, specialty and training.
 
Local cost of living assessments are done for each new hire at the time of offer.
About our culture

Smarsh hires lifelong learners with a passion for innovating with purpose, humility and humor. Collaboration is at the heart of everything we do. We work closely with the most popular communications platforms and the world's leading cloud infrastructure platforms. We use the latest in AI/ML technology to help our customers break new ground at scale. We are a global organization that values diversity, and we believe that providing opportunities for everyone to be their authentic self is key to our success. Smarsh leadership, culture, and commitment to developing our people have all garnered Comparably.com Best Places to Work Awards. Come join us and find out what the best work of your career looks like.
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