Member of Technical Staff

We need to output HTML fragment with sections: About the role? Actually we have sections: About Labelbox (maybe not needed?), Why Join Us, The Role (What You'll Do), What We're Looking For, Preferred, Candidate Archetype, Why This Role Matters, Life at Labelbox, Our Vision, etc. But we must only include sections when posting has real specific content. We need to use

for section headings (e.g., About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). However they gave examples; but we can use other headings? The instruction: "use

for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule)." Means we should use those headings if applicable, but we can also use other headings? Probably they want us to map content to those standard headings. But we can also use other headings if needed? The instruction says use

for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). It implies those are the allowed headings. So we should structure using those headings where appropriate. Let's map: - About the role: could be summary of role (The Role section). Use

About the role

then

describing role. - Responsibilities: list of duties (What You'll Do). Use

Responsibilities

with

• . - Requirements: mandatory qualifications (What We're Looking For). Use

  Requirements

  with
  . - Qualifications: maybe Preferred? Could be Qualifications (preferred). Use

  Qualifications

  for preferred. - Skills: maybe specific skills? Could combine but we have many. Could put under Skills. - Benefits: Life at Labelbox? Could be Benefits. Use

  Benefits

  for Life at Labelbox (environment, growth, vision). Also maybe Pay: we have salary range. - Pay: we have salary range. Use

  Pay

  with

  . - Schedule: not mentioned, so omit. We must not include sections with filler like "commensurate with experience". We have salary range specific, so include Pay. We must not repeat job title as heading. Not include Job ID. We must remove boilerplate: cookie/navigation/legal/EEO etc. At end there is privacy notice etc; remove. We must preserve all substantive information. Let's extract content: About Labelbox: description of company, three integrated solutions. Could be part of About the role? Maybe we can include a brief About Labelbox under About the role? But the instruction: About the role heading for role summary. Could also include About Labelbox as part of role? Might be okay to include under About the role? However they said use headings like About the role, Responsibilities, etc. So we can put company description under About the role? Might be weird but okay. Alternatively we could create a separate section like "About Labelbox" but not in allowed headings. Safer to stick to allowed headings. Thus:

  About the role

  Shape the Future of AI At Labelbox, we're building the critical infrastructure that powers breakthrough AI models at leading research labs and enterprises. Since 2018, we've been pioneering data-centric approaches that are fundamental to AI development, and our work becomes even more essential as AI capabilities expand exponentially. ... (maybe include company description). Then role description: Member of Technical Staff to own design, development, and production of Frontier Data Products... etc. But we need to preserve all substantive information; we can't omit company description. So include it in About the role. Then Responsibilities: list of duties from "What You'll Do". Use bullet list. Requirements: from "What We're Looking For". bullet list. Qualifications: from "Preferred". bullet list. Skills: maybe combine some specific skills like Python, Go/Rust/C++, containerization, RL concepts, debugging, etc. But we already have those in Requirements and Qualifications. Could still have Skills section for things like "Strong debugging instincts, ability to read and implement from academic papers". But maybe we can omit Skills if covered. However instruction: prefer a few substantive sections over many thin ones. We could combine Requirements and Qualifications into one? But they ask to use those headings if applicable. We'll include both. Benefits: from "Life at Labelbox" and "Our Vision". bullet list maybe. Pay: salary range. We must not include sections like Schedule. Now ensure we don't include filler like "commensurate with experience". Not present. Now produce HTML. Let's craft. About the role paragraph: combine company overview and role description. Company overview: "Shape the Future of AI At Labelbox, we're building the critical infrastructure that powers breakthrough AI models at leading research labs and enterprises. Since 2018, we've been pioneering data-centric approaches that are fundamental to AI development, and our work becomes even more essential as AI capabilities expand exponentially. About Labelbox We're the only company offering three integrated solutions for frontier AI development: Enterprise Platform & Tools: Advanced annotation tools, workflow automation, and quality control systems that enable teams to produce high-quality training data at scale Frontier Data Labeling Service: Specialized data labeling through Alignerr, leveraging subject matter experts for next-generation AI models Expert Marketplace: Connecting AI teams with highly skilled annotators and domain experts for flexible scaling" Then role description: "The Role We’re hiring a Member of Technical Staff to own the design, development, and production of Frontier Data Products. You’ll build the sandboxed, reproducible environments AI agents rely on during training and evaluation, the terminals, browsers, and tool-augmented workspaces they operate inside. This is a hands-on engineering role. You’ll write production-quality infrastructure, integrate with the broader RL tooling ecosystem, and partner closely with our data operations team to keep environments robust and observable for annotators and model agents alike. Above all, you’ll need a real grasp of how RL training loops consume environments and where they tend to break." We'll combine. Now Responsibilities bullet list from "What You'll Do". Let's extract each sentence as bullet. - Design, build, and maintain sandboxed RL environments for agentic AI training—including terminal emulators, browser automation harnesses, computer-use simulators, and tool-augmented workspaces (e.g., environments built on frameworks like TerminalBench, OSWorld, and Tau-bench) - Develop reproducible, containerized execution environments (Docker, VMs, lightweight sandboxes) that support deterministic task rollouts and reward signal collection - Integrate with and extend open-source agentic tooling and custom CLI/API harnesses to enable multi-step agent interaction - Build instrumentation and observability layers—structured logging, trajectory capture, state snapshotting, so training runs and human annotation sessions produce clean, auditable data - Collaborate with data operations to design task curricula and evaluation protocols that stress-test model capabilities across environment types - Own environment deployment and reliability: CI/CD pipelines, automated testing of environment configurations, and monitoring for drift or breakage across versions - Rapidly prototype new environment types as client and internal requirements evolve, moving from spec to working system in days, not weeks Now Requirements bullet list from "What We're Looking For": - 2+ years of professional software engineering experience, with strong fundamentals in Python and at least one systems-level language (Go, Rust, C++) - Demonstrated experience with containerization and sandboxing (Docker, Podman, Firecracker, or similar) in production or near-production contexts - Familiarity with RL concepts: MDPs, reward shaping, episode structure, observation/action spaces. You don’t need to have trained models, but you need to understand what an environment must provide to an RL training loop - Experience building or maintaining developer tooling, CLI tools, or infrastructure automation - Comfort working with browser automation frameworks or terminal interaction tooling - Strong debugging instincts, you can trace failures across process boundaries, container layers, and network calls - Ability to read and implement from academic papers and open-source benchmark repositories without extensive hand-holding Now Qualifications bullet list from "Preferred": - Direct experience building or contributing to RL environments (Gymnasium/Gym, PettingZoo, or custom environment implementations) - Experience with agentic AI evaluation frameworks (SWE-bench, WebArena, OSWorld, TerminalBench, or similar) - Familiarity with GCP or AWS infrastructure (Compute Engine, ECS/EKS, Cloud Build) - Prior work at an AI data company, ML platform company, or AI research lab - Contributions to open-source projects in the RL, agents, or dev-tools space Now Benefits bullet list from "Life at Labelbox" and "Our Vision". Also maybe include "Environment: Fast-paced and high-intensity, perfect for ambitious individuals who thrive on ownership and quick decision-making", "Growth: Career advancement opportunities directly tied to your impact", "Vision: Be part of building the foundation for humanity's most transformative technology", "Our Vision We believe data will remain crucial in achieving artificial general intelligence. As AI models become more sophisticated, the need for high-quality, specialized training data will only grow. Join us in developing new products and services that enable the next generation of AI breakthroughs." Also mention investors and customers? Might be considered substantive. Could include but maybe not needed. We'll include key points. Benefits bullets: - Fast-paced and high-intensity environment, ideal for ambitious individuals who thrive on ownership and quick decision-making - Career advancement opportunities directly tied to your impact - Opportunity to be part of building the foundation for humanity's most transformative technology - Belief that data will remain crucial in achieving artificial general intelligence, with growing need for high-quality, specialized training data - Chance to develop new products and services that enable the next generation of AI breakthroughs - Backed by leading investors including SoftBank, Andreessen Horowitz, B Capital, Gradient Ventures, Databricks Ventures, and Kleiner Perkins - Customers include Fortune 500 enterprises and leading AI labs Now Pay: salary range. - Annual base salary range $140,000—$200,000 USD (not inclusive of equity or additional benefits; exact compensation varies based on skills, experience, location) We'll put in

  . Now ensure we don't include any boilerplate like "Candidate Archetype", "Why This Role Matters", etc. Those are substantive but we may incorporate them into sections? The candidate archetype and why role matters could be included in About the role or Benefits? But we have to preserve all substantive information. If we omit those sections, we lose info. We need to include them somewhere. Could incorporate into About the role or Benefits. Let's see content: Candidate Archetype: description of ideal candidate. Could be part of Requirements or Qualifications? It's more about traits. Could be added to Requirements as bullet? Might be okay. Why This Role Matters: explanation of importance. Could be added to About the role or Benefits. Life at Labelbox we already used. Our Vision we used. Thus we need to embed those paragraphs somewhere. Let's extract Candidate Archetype paragraph: "The ideal candidate is a strong software engineer first, with genuine curiosity and working knowledge of how modern AI systems are built and evaluated. You’ve probably built infrastructure or developer tooling at a startup or mid-stage company, and you’ve been pulled toward the AI space maybe through side projects, open-source contributions, or a prior role adjacent to an applied AI or ML team. You’re the kind of engineer who reads a benchmark paper or evaluation framework and immediately thinks about how to make the underlying system more robust, not just how to improve the model’s output. You thrive in ambiguity. You can take a loosely defined project requirement, “build an environment that tests an agent’s ability to navigate a file system and execute multi-step bash workflows” and deliver a working, tested, documented system without needing a detailed spec. You move fast, but you care about reliability because you know systems that break silently poison the data