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Principal + Staff Software Engineers

AI Summary
Unstructured is hiring senior technical leaders to architect and scale an enterprise platform for transforming unstructured data into AI-ready pipelines. The role centers on distributed systems, Kubernetes, Python services, and deeply optimized PostgreSQL workloads. The engineer will set technical direction, mentor senior peers, and work with engineering, infrastructure, and product leadership to productionize AI and retrieval systems. Success requires extensive experience with resilient, high-throughput infrastructure and sound judgment in an ambiguous, fast-moving environment.
Remote from
USA
Salary
USD 200k–310k / yr
Employment
Full Time
Experience
Senior
Published
Apply before
14 Dec 2026
Listing views
27
Application actions
0
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Opportunity details

About this role.

Unstructured is defining the standard for enterprise data transformation in the age of LLMs and generative AI. In just two years, we’ve raised over $65M from world-class investors, including Menlo Ventures, Bain Capital, Databricks, NVIDIA, Microsoft, and IBM.

Our open-source toolkit has been downloaded 97M+ times and is used by 90% of the Fortune 1000. We power production AI workflows across commercial and federal sectors — transforming PDFs, HTML, Word docs, images, emails, and more into AI-ready data pipelines that scale.

We’re not just building tools; we’re building the backbone of generative AI and the infrastructure that unlocks intelligence across industries.

About the Role

Unstructured is looking for Staff and Principal Software Engineers to define the architectural foundation for how enterprises process and transform unstructured data for large-language-model (LLM) applications.

This is a high-impact, hands-on role for someone who thrives in deep systems work—someone who could write the book on Kubernetes, lives and breathes Python, and understands Postgres at the index and transaction-log level. You’ll own the technical direction for our core platform, guiding how data moves through every layer—from ingestion to transformation to retrieval.

You’ll partner closely with the Head of Engineering, Head of Infrastructure, and product leaders to ensure Unstructured’s systems are performant, resilient, and ready to support the next generation of AI-driven workloads.

Location(s) Preferred: San Francisco, Bay Area, San Jose, Seattle, Sacramento

What You’ll Own & Drive

  • Define and evolve the end-to-end architecture for Unstructured’s data transformation and retrieval platform.

  • Build and scale distributed systems that process massive volumes of unstructured data across diverse formats and sources.

  • Serve as the company-wide authority on Kubernetes orchestration, cluster design, performance tuning, and reliability.

  • Lead Python architecture and best practices—ensuring performance, modularity, and maintainability across services.

  • Design and optimize Postgres schemas, queries, and indexing strategies to support large-scale metadata and retrieval pipelines.

  • Mentor senior engineers through design reviews and code guidance, raising the bar for technical excellence across the org.

  • Partner with the infrastructure and product teams to translate research prototypes into production-grade systems.

  • Evaluate emerging technologies and open-source tools in LLM infrastructure, retrieval, and orchestration—deciding where and how to integrate them.

What We’re Looking for

  • Have 10+ years of software engineering experience with a focus on distributed systems, infrastructure, or data architecture.

  • Are a Python expert—capable of building frameworks and performance-critical services from scratch.

  • Have deep Kubernetes expertise; you can design, deploy, and debug at scale and could teach others how to productionize it securely.

  • Are fluent in Postgres—you understand query planning, partitioning, and tuning for high-throughput environments.

  • Are obsessed with clean, scalable architecture and can lead design reviews that shape how entire systems evolve.

  • Have experience in high-performance data or AI/ML systems—especially those involving retrieval pipelines, embeddings, or hybrid workloads.

  • Thrive in fast-moving, ambiguous environments where technical depth and judgment matter more than process.

Bonus Points

  • Experience building or scaling LLM-powered or RAG systems in production.

  • Familiarity with open-source orchestration frameworks, vector databases, or hybrid cloud infrastructure.

  • Contributions to open-source projects in Python, Kubernetes, or distributed systems.

Why You’ll Love It Here

You’ll be surrounded by smart, kind, low-ego people who genuinely enjoy building together. We invest in our team with company offsites, best-in-tech swag, and the tools you need to do your best work, wherever you’re based.

We support you holistically, not just at work. For full-time, US-based employees, that includes:

  • Health: Medical, dental, and vision coverage through Justworks, effective the 1st of the month following your start date. All medical plans include fertility coverage, plus access to One Medical, Talkspace (12 free therapy sessions a year), Kindbody, and Teladoc. If you waive medical coverage, we pay a $250/month allowance.

  • Financial: 401(k) with company match (100% of the first 3% you contribute, 50% of the next 2%), company-paid life insurance, and optional supplemental life, short- and long-term disability, accident, critical illness, and hospital coverage. FSA and HSA options available.

  • Equity: Stock options, so you share in what we’re building.

  • Time off: Flexible paid time off (unlimited), up to 80 hours of paid sick leave a year, 12 company holidays, and a company-wide winter break between Christmas and New Year’s.

  • Family: 16 weeks of fully paid parental leave for all parents, available after 6 months, plus up to 6 additional unpaid weeks.

  • Remote setup: A $500 home office stipend and a $75/month cell and internet stipend.

  • Team: Team and company offsites throughout the year.

We want you focused on building, growing, and staying energized for the long haul. If you’re excited about what we’re building, we’d love to meet you.

Unstructured is proud to be an Equal Opportunity Employer. We’re committed to building a diverse team and an inclusive culture where everyone can do their best work. We do not discriminate on the basis of race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, disability, veteran status, or any other characteristic protected by applicable law.

Apply now >

This job listing has been manually reviewed by the Jobicy Trust & Safety Team for compliance with our posting guidelines, including verification of the company's legitimacy, accuracy of job details, clarity of remote work policy, and absence of misleading or fraudulent content.

Role DNA

A quick view of the complexity, pace, ownership and collaboration implied by the job description.

Job Complexity

5/5
EasyHard

Pace & Pressure

5/5
RelaxedFast-paced

Autonomy Level

5/5
GuidedFull ownership

Communication Load

4/5
IndependentCollaborative
AI insightThis is a principal/staff-level architecture role requiring 10+ years of experience and deep expertise across distributed systems, Kubernetes, Python, and PostgreSQL internals. The scope includes company-wide technical leadership and production-scale AI infrastructure decisions.

Salary analysis

Estimated compensation compared with the broader US market for similar roles.

Estimated job median (per year)Market rate
$255,000
US market range (per year)$210k–$350k
AI insightThe employer discloses a yearly base compensation range of $200,000 to $310,000 USD, with a midpoint of $255,000. This is a credible range for a US-based Staff or Principal Software Engineer specializing in distributed systems, Kubernetes, and AI infrastructure. The estimated US market range for comparable roles is $210,000 to $350,000 per year, excluding equity and other benefits.

Core skills

Skills and capabilities most closely associated with this opportunity.

Sample interview questions
How would you design a platform to process very large volumes of heterogeneous unstructured documents reliably?

I would separate ingestion, parsing, transformation, enrichment, and indexing into independently scalable stages connected through durable queues or event streams. Each stage would be idempotent, observable, and protected by retry, back-pressure, and dead-letter mechanisms. I would define clear document lineage and metadata contracts so failures can be diagnosed and individual workloads replayed safely.

Describe how you would investigate and improve a slow PostgreSQL query in a high-throughput retrieval pipeline.

I would begin with query plans, latency distributions, row estimates, and production workload characteristics. I would verify index selection, reduce unnecessary scans and joins, assess partitioning and data-model choices, and test improvements against representative data. Any change would be validated for write overhead, transaction contention, and operational impact before rollout.

What principles guide your Kubernetes cluster design for critical production services?

I focus on workload isolation, secure defaults, autoscaling, capacity planning, and failure-domain resilience. Services should have explicit resource requests and limits, health checks, deployment safeguards, observability, and tested recovery procedures. I also treat cluster upgrades, networking, secrets, and policy enforcement as core architecture concerns rather than operational afterthoughts.

How would you turn an LLM or RAG prototype into a production-grade system?

I would first establish measurable quality, latency, cost, and reliability targets. Then I would build versioned ingestion and indexing workflows, evaluate retrieval quality with representative test sets, add tracing across retrieval and generation steps, and implement safeguards for data access and model failures. The system should support controlled experimentation while retaining reproducibility and rollback capability.

How do you lead architecture decisions when teams have competing technical priorities?

I make trade-offs explicit by documenting the problem, constraints, alternatives, expected outcomes, and operational risks. I involve the relevant engineers early, use evidence such as benchmarks and incident history, and align the decision with product and reliability goals. After deciding, I communicate the rationale clearly and define ownership, milestones, and signals that would trigger a reassessment.

This analysis is generated from the job description. Salary estimates, role characteristics and sample answers are guidance, not employer-provided facts.
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