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Remote Retrieval Augmented Generation Jobs in Oregon

... augmented and virtual reality, gaming and so much more, our technology is all around us. We design ... This role is remote-based in the Boise, Idaho metropolitan area, with frequent, hands-on engagement ...

Digital Asset and Banking Analyst

OR · On-site +1

$57K - $124K/yr

... next-generation digital banking experiences. This is a hands-on role designed for someone who ... However, the remote location must be within the US. How you will spend your time: * Support ...

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 ...

Remote (Occasional travel as needed) Reports to: Global AI Center of Excellence Lead Why Us ... Automated classification and taxonomy generation * Anomaly detection and data quality monitoring

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Remote Retrieval Augmented Generation information

What are the key skills and qualifications needed to thrive as a Remote Retrieval Augmented Generation Engineer, and why are they important?

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 some 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 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:
Infographic showing various Remote Retrieval Augmented Generation job openings in Oregon as of June 2026, with employment types broken down into 88% Full Time, 8% Part Time, 2% Contract, and 2% Nights. Highlights an 65% Physical, 2% Hybrid, and 33% Remote job distribution.
Senior Engineering Manager, Search

Senior Engineering Manager, Search

Instacart

OR • Remote

Other

Posted 8 days ago


Instacart rating

6.7

Company rating: 6.7 out of 10

Based on 29 frontline employees who took The Breakroom Quiz


Job description

Overview

Instacart is the leading grocery technology company in North America, partnering with more than 1,800 retail banners to provide online shopping, delivery and pickup services from more than 100,000 stores. Search is the single most important way customers find and add products to their cart, and Cross-Retailer Search is how we help people decide not just what to buy, but where to shop. Together they sit at the top of the discovery funnel, where improvements in relevance and ranking compound directly into conversion, retailer engagement, and growth.

We are standing up a dedicated Search engineering team and are looking for a seasoned engineering leader to run it. You will own the strategy and execution for Single-Retailer Search (SRS), Cross-Retailer Search (XRS), search suggestions and typeahead, and the whitelabel search we provide to Storefront Pro retailers. This is a heavily ML-weighted team operating one of Instacart's largest revenue surfaces, and one of the most exciting frontiers in applied search and ranking.

About the job

You will lead and grow a senior team of engineers, partnering with embedded MLEs and ML leads, responsible for the relevance, ranking, and infrastructure behind Instacart Search. In this role, you will:

  • Own the team's technical strategy, roadmap, and goals across Single and Cross retailer search, query understanding, suggestions, Ads on Search.
  • Drive top-line growth while holding a strict quality bar.
  • Champion the use of LLMs in search.
  • Partner with the platform team to design and ship the next-generation search retrieval architecture, while managing the latency and reliability tradeoffs of a tier-1 surface.
  • Double the team's experimentation velocity by investing in evaluation, AI-native debugging tooling, and ML foundations.
  • Partner closely with ML, Product, Data Science, the Home and Discovery Platform teams, the Ads organization, and whitelabel retailer teams to translate ranking changes into business and retailer outcomes.
  • Hire, develop, and coach engineers, and build an inclusive, high-trust team culture.

You'll roll up your sleeves on technical and architectural decisions as needed, while spending most of your time setting direction, removing blockers, and growing the people on your team.

Minimum qualifications
  • 4+ years of engineering management experience leading high-performing teams, with minimum 10+ years of total engineering experience.
  • Direct experience building or leading search, recommendations, ranking, or other applied ML systems, as a manager or as a senior individual contributor.
  • Familiarity with modern retrieval techniques, including neural/semantic retrieval, ANN, and LLM-based query understanding or re-ranking.
  • Strong technical depth across machine learning and large-scale distributed/backend systems; able to set direction with and earn the trust of staff-level engineers.
  • A track record of driving complex, cross-functional technical programs from strategy through launch while holding a high bar for quality and reliability.
  • Demonstrated product and business judgment: able to connect modeling and ranking decisions to metrics like conversion and revenue, and to reason through tradeoffs (e.g., relevance vs. latency, organic vs. ads).
  • Strong communication and stakeholder skills; comfortable bridging technical and non-technical partners.
Preferred qualifications
  • Experience with ranking and personalization at scale: multi-stage rankers, multi-task deep models, embeddings, and real-time/session-based features.
  • Experience operating low-latency, high-throughput services with strict SLAs, including on-call and reliability ownership.
  • Experience leading a 01 or ambiguous product area, and driving alignment across teams where ownership boundaries are still forming.
  • Experience working with LLMs in production, and with experimentation/A-B testing infrastructure.
  • Exposure to two-sided marketplaces, e-commerce, or ads-adjacent ranking problems.

#LI-Remote


What Instacart employees say

Pay

Benefits

Hours and flexibility

Workplace

Get the full story on Breakroom


Instacart logo

About Instacart

Sourced by ZipRecruiter

Instacart, based in San Francisco, CA, US, operates within the retail industry, specifically grocery delivery and pick-up service. It is recognized as a pioneer in this field, delivering fresh groceries from local stores directly to customers' doors. The company, which launched its services in 2012, continues to pioneer change in the online grocery shopping sector through its commitment to cutting-edge technology, new business ideas, and dedicated service.

Industry

Technology, communication and media

Company size

10,000+ Employees

Headquarters location

San Francisco, CA, US

Year founded

2012