105 matching jobs for “Staff Back-end Engineer”

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Coupang

Sr. Staff Backend Engineer

Seattle, Washington, United States

Full time (company default)

… across teams. Hands-on expertise with a wide range of technologies, spanning front-end interfaces to back-end systems. A customer-first mindset, with the ability to work backward from customer needs to deliver …

Added Oct 14, 2026

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Block

Staff Software Engineer, Payments Acquiring

Bay Area, CA, United States of America +1 location 2 postings

Remote
Regular
Lead / principal

… answer in under a second, around the clock, in partnership with card networks and bank partners. As a Staff Software Engineer on the Acquiring team, you will bring deep card payments experience to some of the most …

Added Oct 9, 2026

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GE HealthCare Technologies

Senior Staff AI Application Engineer

IND19-01-Bengaluru-EPIP 122 (Phase II)

Summary We are looking for a senior full-stack engineer who has shipped production applications, and who has owned software that other engineering teams depended …

Added Oct 27, 2026

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Coupang

Senior Staff Backend Engineer, Security

Mountain View, California, United States

Full time (company default)

… integrating them with Coupang’s service infrastructure. The successful candidate will be a well-rounded engineer who balances strategic and design thinking with attention to detail in implementation and operations. If …

Added Oct 4, 2026

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Coupang

Senior Staff Backend Engineer (Service Mesh)

India

Full time (company default)

… experience in this always-on, high-tech, and hyper-connected world. Role Overview We are looking for a Sr Staff Backend Engineer who has a deep understanding of the interplay of large scale microservices and hands-on …

Added Oct 4, 2026

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Coupang

Senior Staff Backend Engineer - Cloud Infrastructure

Mountain View, California, United States +1 location 2 postings

Full time (company default)

… an epic experience in this always-on, high-tech, and hyper-connected world. Job Overview: As a Senior Staff Backend Engineer - Cloud Infrastructure, this role will focus on Cloud Platform Infrastructure, Services …

Added Oct 4, 2026

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Databricks

Senior Staff Applied AI Engineer - Context Retrieval

Mountain View, California, United States +1 location

Pay posted by employer$228,600–$342,800 USD

Added Oct 4, 2026

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Coupang

Staff Backend Engineer (ECommerce Engineering; Core Member)

Seoul, South Korea

Full time (company default)

… 만들어갈 쿠팡에 지금 합류하세요. 팀 소개 쿠팡의 E-commerce Engineering 조직에서는 엔지니어 팀을 이끌고 쿠팡의 핵심 데이터 서빙 백엔드 시스템의 기술 방향을 수립할 유능한 Back-end Engineer를 모집하고 있습니다. Back-end Engineer는 팀의 기술 비전을 구체화하고 전세계 수백만 건에 달하는 쿠팡 사용자의 요청을 처리할 수 있는 차세대 데이터 서빙 플랫폼을 …

Added Oct 15, 2026

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Coupang

Staff, Full-Stack Engineer [Rocket Growth Engineering]

Seattle, Washington, United States

Full time (company default)

… team Hands-on expertise in many disparate technologies, ranging from front-end user interfaces through to back-end systems and all points in between Experience working backwards from the customer and delivering innovative, …

Added Oct 4, 2026

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Cisco Systems

Splunk Senior Staff Software Engineer - Performance Optimization & Innovation (PerfOpt)

Kraków, Poland

… that move the numbers, catch work that fails under a profile, redirect quickly. Lead and mentor senior and staff engineers. Minimum Qualifications Technical depth Expert-level C++ in production systems - memory …

Added Oct 27, 2026

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Capital One Financial

Staff Software Engineer - Capital One Travel Tech - Full-stack

Toronto, ON

Pay posted by employer$172,960–$197,400 per year

… architecture and development Proficiency in building both the user-facing front-end and the server-side back-end of applications as this is a Full-Stack role Experience providing mentorship to Junior/Intermediate …

Added Oct 27, 2026

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Capital One Financial

Staff Software Engineer - Capital One Travel Tech - Full-stack

Toronto, ON

Pay posted by employer$172,960–$197,400 per year

Added Oct 27, 2026

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Capital One Financial

Staff Software Engineer - Capital One Travel Tech - Full-stack

Toronto, ON

Pay posted by employer$173,000–$197,400 per year

Added Oct 27, 2026

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The Home Depot

Enterprise Staff UX Designer

STORE SUPPORT CENTER, ATLANTA - 9090

In person
Regular

Added Oct 16, 2026

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Coupang

Senior Staff Applied Scientist

Seattle, Washington, United States

Full time (company default)

… and identify high-leverage opportunities for improvement. Closely collaborate with product, operations, and back-end engineers to align technical solutions with business goals and customer experience. Mentor and grow …

Added Oct 4, 2026

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Warner Bros. Discovery

Staff Data Scientist (Growth & ML), Hyderabad

Hyderabad - Phoenix Equinox Tower 2

Hybrid
Full time
Regular

… company powered by creativity and technology, we’d be thrilled to welcome you aboard. LET’S GO! ㅤ Staff Data Scientist (Growth & ML), Hyderabad About Warner Bros. Discovery: Warner Bros. Discovery is a premier …

Added Oct 27, 2026

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Palo Alto Networks

Sr Staff UX Designer (AIRS UX)

Office - USA - WA - Seattle

… that are new and innovative. Work closely with cross-functional stakeholders, including front-end and back-end engineers, UX leadership, and product stakeholders, to ensure all needs and requirements are accounted for. …

Added Oct 28, 2026

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Amgen

Associate IS Engineer

Hyderabad, India

… and human genetic data to push beyond what is known today. Role Description We are seeking an Associate IS Engineer to support the delivery, operation, maintenance, and continuous improvement of enterprise applications and …

Added Oct 27, 2026

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Amgen

Associate IS Engineer

Hyderabad, India

Added Oct 27, 2026

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Capital One Financial

Staff Software Engineer - Capital One Travel Tech - Full-stack / développeur principal ou développeuse principale en logiciels

Toronto, ON

Pay posted by employer$173,000–$197,400 per year

Added Oct 27, 2026

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Databricks

Senior Staff Applied AI Engineer - Context Retrieval

Mountain View, California, United States +1 more

Pay posted by employer$228,600–$342,800 USD

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What We're Looking For · employer excerpt

10+ years of software engineering experience, with significant time spent building production retrieval, search, or RAG systems at scale.
Deep Information Retrieval (IR) expertise: lexical retrieval (BM25, Lucene/Elasticsearch/OpenSearch), dense retrieval (embeddings, ANN indexes — FAISS, ScaNN, HNSW), hybrid retrieval, and learning-to-rank.
Hands-on experience with modern LLM-era retrieval: RAG architectures, query rewriting, re-ranking with cross-encoders, long-context strategies, and grounding techniques that reduce hallucination.
Experience designing agentic systems on top of retrieval — search planners, multi-hop / iterative retrieval, self-reflection and sufficiency checks, tool-using agents that decide what to fetch and verify what came back.
Strong grasp of relevance evaluation: nDCG, MRR, Precision@K, Recall@K; offline/online experimentation; LLM-as-judge frameworks; building human labeling pipelines.

3 more in the full description below.

About this opportunity

Original employer description

P-1549

At Databricks, we are passionate about enabling data teams to solve the world's toughest problems — from making the next mode of transportation a reality to accelerating the development of medical breakthroughs. We do this by building and running the world's best data and AI infrastructure platform so our customers can use deep data insights to improve their business.

The Mission

Databricks agents are only as good as the context they can retrieve. Whether an agent is answering a question about last quarter's revenue, debugging a failing job, generating SQL against a 10,000-table lakehouse, or summarizing a Wiki page, its quality is bounded by what it can find — and how well it understands what it finds.

We are hiring a Senior Staff Applied AI Engineer to own context retrieval for Databricks agents across SaaS providers. This is a zero-to-one role with two deeply connected charters:

Build the retrieval stack — query understanding, content understanding, ranking, retrieval, and evaluation — across the Enterprise SaaS data stored across multiple systems.
Build the search subagents that sit on top of that stack and reason about what context is needed, how to retrieve it, and whether the right thing actually came back — closing the loop between an agent's intent and the substrate that serves it.

If you have deep Information Retrieval wisdom, have shipped retrieval systems for RAG and agentic workloads, and want to build the substrate — and the agents on top of it — that make every Databricks agent measurably smarter, this role is for you.

What You Will Do

Build the full retrieval stack from scratch. Own the end-to-end system: query understanding, content understanding and indexing, hybrid retrieval, ranking, and evaluation. Make the architectural calls that will define how Databricks agents access context for years to come.
Retrieve across heterogeneous data — structured and unstructured. Index and rank across structured assets (tables, columns, SQL queries, dashboards, code, notebooks, jobs) and unstructured content (docs, wikis, tickets, chat, images, video, audio). Each modality has its own signals — design retrieval that exploits them rather than flattens them.
Connect to the SaaS surface area customers actually use. Build connectors and retrieval adapters for the systems where enterprise knowledge lives. Treat each retrieval source with its own freshness, permissions, and ranking signals.
Optimize for two consumers at once. Retrieval must serve both LLMs (grounded, token-efficient, hallucination-resistant context) and humans (intuitive, explainable discovery). These are different objectives and require different signals — own both.
Crack query understanding for agents. Agent queries don't look like web queries. Build query rewriting, decomposition, intent classification, and entity resolution tuned for multi-turn agentic workflows.
Crack content understanding at scale. Build the pipelines that extract structure, entities, embeddings, summaries, and metadata from every supported asset type — and keep them fresh as customer data evolves.
Build search subagents that reason about retrieval. Design the agentic layer that decides what context is needed, which sources to query, how to decompose and route the search, and — critically — whether the retrieved content is actually sufficient to answer the question. These subagents will plan multi-hop searches, issue follow-up queries when results are weak, ground claims against retrieved evidence, and hand back high-confidence context (or signal failure) to upstream agents. This is where IR meets agentic reasoning.
Build the evaluation flywheel for both retrieval and subagents. Stand up offline evals (nDCG, MRR, Recall@K, Precision@K), LLM-as-judge harnesses, human-in-the-loop labeling, and online experimentation. Extend evaluation beyond ranking metrics to measure subagent decision quality — did it ask the right follow-up?, did it correctly recognize when retrieval failed?, did it ground its answer in the right evidence?. Quality you can't measure is quality you can't ship.
Set technical direction and grow the team. Set the multi-year roadmap, mentor senior engineers, partner with Research, Product, and Platform leaders, and raise the technical bar across the org.

What We're Looking For

10+ years of software engineering experience, with significant time spent building production retrieval, search, or RAG systems at scale.
Deep Information Retrieval (IR) expertise: lexical retrieval (BM25, Lucene/Elasticsearch/OpenSearch), dense retrieval (embeddings, ANN indexes — FAISS, ScaNN, HNSW), hybrid retrieval, and learning-to-rank.
Hands-on experience with modern LLM-era retrieval: RAG architectures, query rewriting, re-ranking with cross-encoders, long-context strategies, and grounding techniques that reduce hallucination.
Experience designing agentic systems on top of retrieval — search planners, multi-hop / iterative retrieval, self-reflection and sufficiency checks, tool-using agents that decide what to fetch and verify what came back.
Strong grasp of relevance evaluation: nDCG, MRR, Precision@K, Recall@K; offline/online experimentation; LLM-as-judge frameworks; building human labeling pipelines.
Experience working across structured and unstructured data — you've indexed and ranked over tables, code, and documents in the same system, and have opinions about how to do it well.
Track record of building 0→1: you've stood up a retrieval system from an empty repo, made the foundational architectural decisions, and grown it into something that customers depend on.
Demonstrated ability to operate as a technical leader: setting direction across teams, mentoring senior engineers, and influencing roadmap with research, product, and platform partners.

Nice to Have

Experience building retrieval over enterprise SaaS sources (permissions, freshness, multi-tenancy, ACL-aware indexing).
Background in agentic systems, tool use, or multi-turn retrieval for LLM agents.
Contributions to open-source IR/search projects, or publications at SIGIR, KDD, WWW, EMNLP, or similar venues.
Experience training or fine-tuning embedding models, rerankers, or query understanding models.

Why This Role

Foundational impact. Retrieval is the single biggest lever on agent quality. The stack you build will sit underneath every Databricks agent and every customer-built agent on our platform.
Greenfield with scale. You get the rare combination of starting from a clean sheet and having immediate access to massive enterprise scale, real customer data, and a world-class research org.
The right team. You'll work alongside engineers and researchers behind Lakehouse, Apache Spark™, Delta Lake, MLflow, MosaicML, and DBRX.

Location

This role is based in our Mountain View, CA or San Francisco, CA office. Hybrid in-office collaboration expected.

Pay Range Transparency

Databricks is committed to fair and equitable compensation practices. The pay range(s) for this role is listed below and represents the expected salary range for non-commissionable roles or on-target earnings for commissionable roles. Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to job-related skills, depth of experience, relevant certifications and training, and specific work location. Based on the factors above, Databricks anticipates utilizing the full width of the range. The total compensation package for this position may also include eligibility for annual performance bonus, equity, and the benefits listed above. For more information regarding which range your location is in visit our page here.

Local Pay Range

$228,600—$342,800 USD

About Databricks

Databricks is the Data and AI company. More than 20,000 organizations worldwide — including adidas, AT&T, Bayer, Block, Mastercard, Rivian, Unilever, and 70% of the Fortune 500 — rely on the Databricks Data + AI Platform to build and scale data and AI apps, analytics and agents. Headquartered in San Francisco with 30+ offices around the globe, Databricks offers a unified platform that includes Genie, Lakebase, Agent Bricks, Lakeflow, Lakehouse, and Unity Catalog. To learn more, follow Databricks on LinkedIn, X, YouTube, and Instagram.

Benefits

At Databricks, we strive to provide comprehensive benefits and perks that meet the needs of all of our employees. For specific details on the benefits offered in your region click here.

Our Commitment to Diversity and Inclusion

At Databricks, we are committed to fostering a diverse and inclusive culture where everyone can excel. We take great care to ensure that our hiring practices are inclusive and meet equal employment opportunity standards. Individuals looking for employment at Databricks are considered without regard to age, color, disability, ethnicity, family or marital status, gender identity or expression, language, national origin, physical and mental ability, political affiliation, race, religion, sexual orientation, socio-economic status, veteran status, and other protected characteristics.

Compliance

If access to export-controlled technology or source code is required for performance of job duties, it is within Employer's discretion whether to apply for a U.S. government license for such positions, and Employer may decline to proceed with an applicant on this basis alone.

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