总监 of Engineering, Data Labs

PromptCube; United States; A distancia; USD 252.000 - 300.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Annual bonus

Equity

Descripción de la vacante

Invisible Technologies 诚聘 Data Labs 的 Director of Engineering，负责制定面对 AI 训练与评估市场的长期技术战略，管理基准测试系统、数据管道和 RL 环境。你将领导包含工程经理、前向部署工程师与资深IC在内的团队，推动跨部门协作，向高管与客户展示技术蓝图与投资价值。

此职位为美国远程工作，需具备10年以上软件工程经验与5年以上领导经验，能够在快速变化的AI领域制定并执行可落地的路线图，并与产品、商务团队紧密协作实现商业目标。

Formación

• 具备软件工程背景，且有构建机器学习或AI系统的经验。
• 具备领导多团队的工程管理经验，能够跨职能协作。
• 对AI训练与评估领域有深入理解，且能制定技术路线图。

Responsabilidades

• 制定Data Labs的长期技术战略与路线图，确保与AI训练/评估市场走向一致。
• 设定基准测试、数据管道、RL环境等的架构方向，确保可复现与跨客户部署。
• 预测6–18个月的技术变化并据此安排投入，明确AI在设计评审与日常工作中的使用。
• 跟踪竞争格局，向高管提出投资建议，并帮助产品与商务团队设计新服务及定价逻辑。
• 领导多团队工程组织，培养管理者的技术判断与战略意识，建设招聘与运营流程。

Conocimientos

Software engineering

AI/ML systems

Engineering leadership

Descripción del empleo

岗位概况

Invisible Technologies 招聘 Director of Engineering, Data Labs，领导 Data Labs 制定面向 AI 训练与评估市场的多年技术战略，负责基准测试系统、数据管道和强化学习（RL）环境，并管理多个工程团队。 岗位列于美国远程。

工作职责

• 负责 Data Labs 的多年技术战略与路线图，使路线图与 AI 训练和评估市场的走向相匹配；同时决定哪些能力建设为共享平台、哪些保留为客户项目定制方案，在复用与团队自主权之间取得平衡，避免平台导致过度依赖。
• 为基准测试基础设施、数据管道和强化学习（RL）环境设定架构方向，确保其可复现、可跨客户部署移植，并适应下一代训练方法。
• 预判未来 6–18 个月的技术变化，如新的后训练技术、智能体评估和合成数据，并据此安排投入；明确 AI 在设计评审和日常工程工作中的使用预期。
• 持续形成对前沿实验室和企业如何训练、评估、部署模型的认识，跟踪 AI 数据、评估与 RL 环境供应商的竞争格局，向高管层提出投资建议，并协助产品和商务团队设计新服务、定价逻辑及技术叙事。
• 定义公司在基准测试与评估上的观点，包括合作前后对比、能力差距分析和回归跟踪；让评估设计对接客户定义的成效指标，用人工判断校验自动检查，并明确每种方法的盲区。
• 识别可复用的数据资产、框架和管道，使其成为跨项目的持久优势并改善利润率；将数据许可、隐私与污染风险的实践塑造成战略差异化因素，而不只是合规要求。
• 在高管沟通、战略客户关系和售前中代表工程团队，担任客户研究与工程负责人的可信技术顾问，并把客户洞察带回路线图；帮助领导者权衡客户特定需求与更广泛的平台及业务目标。
• 领导由多个团队组成的工程组织，成员包括工程经理、高级经理、前向部署工程师（forward deployed engineers，FDEs）和 Staff+ 独立贡献者；培养管理者的技术判断与战略意识，建设招聘管道，建立规划、质量和运营实践，并将日常交付下放给经理。

任职要求

• 10 年以上软件工程经验，并具备构建机器学习或 AI 系统的丰富经验。
• 5 年以上工程领导经验，包括跨多个团队管理经理的经历。
• 有制定多年技术战略并做出取得商业回报的平台投资决策的过往记录。
• 对 AI 训练与评估领域有深入且与时俱进的理解，包括前沿实验室和企业如何进行微调、RLHF/RLAIF 及评估方法。
• 对 LLM 训练管道有一线熟悉度，深入程度足以做出并论证架构决策。
• 有构建基准测试、数据整理或 RL 基础设施并产生可衡量客户成果的经验。
• 能够解读市场与竞争信号，并将其转化为技术路线图和清晰的产品观点。
• 能够与应用研究、产品和商务团队建立强有力的跨职能合作。
• 具备出色的高管沟通能力，并有信心在客户和售前对话中担任技术顾问。

申请条件与限制

• 岗位标注为美国远程（United States
• Remote）；远程不代表全球可申请。原招聘未列明居住地、工作许可、时区、语言或出差要求，申请前应向招聘人员确认。
• Invisible 是混合办公组织：部分岗位可远程，但多数岗位需要到办公室协作并与特定地点绑定；任何基于地点的要求或混合办公安排将由招聘团队在招聘过程中说明。
• 招聘信息未注明雇佣类型。
• 申请和面试流程可能使用 AI 与自动化决策技术；公司可能根据申请问题回答及其他提供信息筛选候选人，如候选人反对可联系公司。需要申请或面试便利的残障申请人可通过公司提供的表格提出请求。

加分项

• 曾在 AI 服务公司、应用研究咨询机构、前沿实验室或企业 AI 产品公司工作。
• 在 AI 数据与评估市场有公开观点，例如演讲、写作或开源工作。
• 熟悉涉及客户数据的数据许可、合成数据生成或污染检测。
• 有以技术为基础塑造新产品线或服务产品的经验。
• 有带领组织经历快速增长或重大变革的经验。

薪酬与福利

• 旧金山/纽约（SF/NY）的基本年薪范围为 252,000–300,000 美元，按年计算。
• 年度薪酬包含 20% 年度奖金及额外激励结构。
• 所有全职录用均包含奖金与股权。
• 公司按地区设置薪酬层级；招聘人员将在面试流程中确认适用层级。
• 美国境外候选人的薪酬将按当地市场和生活成本调整。
• 最终薪酬由地点、岗位相关经验、技能、知识、内部薪酬公平性和整体市场状况共同决定，因此每份录用方案不同；总薪酬与福利的更多细节将在招聘流程中讨论。
• 医疗、假期或其他福利未在招聘信息中具体说明。

国际远程岗位，招聘地区：United States

• Remote。请核对原招聘中的居住地、工作许可、时区、语言与出差要求；远程岗位不等于全球可申请。以下保留招聘来源英文原文。

Employer-published remote role. Listed location(s): United States

• Remote. Remote does not imply worldwide eligibility. Check the original listing for residency, work authorization, time zone, language and travel requirements.

About Invisible

Invisible Technologies makes AI work. Our end-to-end AI platform structures messy data, automates digital workflows, deploys agentic solutions, measures outcomes, and integrates human expertise where it matters most.

Our platform cleans, labels, and structures company data so it is ready for AI. It adapts models to each business and adds human expertise when needed, the same approach we have used to improve models for more than 80% of the world’s top AI companies, including Microsoft, AWS, and Cohere.

Our successes span industries, from supply chain automation for Swiss Gear to AI-enabled naval simulations with SAIC, and validating NBA draft picks for the Charlotte Hornets.

Profitable for more than half a decade, Invisible reached $134M in revenue and ranked as the number two fastest growing AI company on the 2024 Inc. 5000. In September 2025, we raised $100M in growth capital to accelerate our mission of making AI actually work in the enterprise and to advance our platform technology.

About the Role

We're looking for a Director of Engineering to set the technical strategy for Data Labs, the team that builds the benchmarking systems, data pipelines, and RL environments behind our AI training engagements. The frontier labs and enterprises we work with are changing how they train, evaluate, and deploy models every quarter. Your job is to see where that market is going and make sure Data Labs is built for it before our clients ask.

You'll decide which capabilities we invest in, which we standardize into platform, and which we leave to individual engagements. You'll track how training methods, evaluation practices, and data needs are shifting across the industry, and turn that into a technical roadmap and a point of view our commercial and research teams can sell against.

You'll lead a group of engineering managers, forward deployed engineers (FDEs), and senior ICs, and partner closely with applied research, product, and go‑to‑market leadership. This is a hands‑on strategic role. You won't spend much time writing code, but you'll stay close enough to the technical work to make and defend hard architectural calls, and close enough to clients to know which calls matter.

What You'll Do

Technical Strategy & Roadmap

• Own the multi‑year technical strategy for Data Labs, and translate it into a roadmap tied to where the AI training and evaluation market is heading.
• Decide where to build shared platform versus engagement‑specific solutions, weighing reuse against team autonomy so platforms speed teams up rather than creating over‑dependency.
• Set architectural direction for benchmarking infrastructure, data pipelines, and RL environments so they stay reproducible, portable across client deployments, and ready for the next generation of training methods.
• Anticipate technical shifts 6–18 months out, such as new post‑training techniques, agentic evaluation, and synthetic data, and place the bets that keep Invisible ahead of them.
• Set expectations for how AI is used in design reviews and day‑to‑day engineering work.

Market Intelligence & Competitive Positioning

• Build a clear, current view of how frontier labs and enterprises are training, evaluating, and deploying models, and what that means for the data and infrastructure they'll need from us.
• Track the competitive landscape across AI data, evaluation, and RL environment providers, and help define where Invisible differentiates.
• Turn client signals, research developments, and market trends into investment recommendations for executive leadership.
• Partner with product and commercial teams to shape new offerings, pricing logic, and the technical story behind them.

Evaluation & Data Strategy

• Define Invisible's point of view on benchmarking and evaluation, including pre‑and post‑engagement comparisons, capability gap analyses and regression tracking.
• Make sure evaluation design ties to client‑defined success metrics, validate automated checks against human judgment, and be explicit about what each approach can't see.
• Identify reusable data assets, frameworks, and pipelines that become durable advantages and improve margins across engagements.
• Shape practices for data licensing, privacy, and contamination risk as a strategic differentiator, not just a compliance requirement.

Client & Executive Engagement

• Represent engineering in executive conversations, strategic client relationships, and pre‑sales, translating technical reality into clear, honest narratives.
• Act as a trusted technical advisor to client research and engineering leaders, bringing back what you learn to inform the roadmap.
• Help leaders weigh client‑specific asks against broader platform and business goals, with clear short‑and long‑term paths.

Organizational Leadership

• Lead and grow an engineering org of multiple teams, including engineering managers, senior managers, FDEs, and Staff+ ICs.
• Coach managers to build sound technical judgment and strategic awareness in their teams.
• Build hiring pipelines that reinforce a rigorous, client‑focused, strategy‑aware engineering culture.
• Make sure the org has the planning, quality, and operational practices it needs to execute on the strategy reliably, delegating day‑to‑day delivery to strong managers.

What We're Looking For

• 10+ years in software engineering, with significant experience building ML or AI systems.
• 5+ years in engineering leadership, including experience managing managers across multiple teams.
• A track record of setting multi‑year technical strategy and making platform investment decisions that paid off commercially.
• Deep, current understanding of the AI training and evaluation landscape, including how frontier labs and enterprises approach fine‑tuning, RLHF/RLAIF, and evaluation methodology.
• Hands‑on familiarity with LLM training pipelines, deep enough to make and defend architectural decisions.
• Experience building benchmarking, data curation, or RL infrastructure that produced measurable client outcomes.
• Ability to read market and competitive signals and turn them into a technical roadmap and a clear product point of view.
• Strong cross‑functional partnership with applied research, product and commercial teams.
• Excellent executive communication, and confidence acting as a technical advisor in client and pre‑sales conversations.

Nice to Have

• Prior experience at an AI services firm, applied research consultancy, frontier lab, or enterprise AI product company.
• A visible point of view on the AI data and evaluation market, such as talks, writing, or open‑source work.
• Familiarity with data licensing, synthetic data generation, or contamination detection involving client data.
• Experience shaping new product lines or service offerings from a technical foundation.
• Experience leading an org through rapid growth or significant change.

What’s In It For You

Invisible is committed to fair and competitive pay, ensuring that compensation reflects both market conditions and the value each team member brings. Our salary structure accounts for regional differences in cost of living while maintaining internal equity.

For this position, the annual salary will include a 20% annual bonus and an additional incentive structure. The base salary range for SF/NY are:

Salary Range

$252,000—$300,000 USD

You can find more information about our geographic pay tiers here. During the interview process, your Invisible Talent Acquisition Partner will confirm which tier applies to your location. For candidates outside the U.S., compensation is adjusted to reflect local market conditions and cost of living.

*Bonuses and equity are included in all full‑time offers. Final compensation is determined by a combination of factors, including location, job‑related experience, skills, knowledge, internal pay equity, and overall market conditions. Because of this, every offer is unique. Additional details on total compensation and benefits will be discussed during the hiring process.

What It's Like to Work at Invisible:

At Invisible, we’re not just redefining work—we’re reinventing it. We operate at the intersection of advanced AI and human ingenuity, pushing the boundaries of what’s possible to unlock productivity and scale. Ownership is at the core of everything we do. Here, you won’t …

We expect bold ideas, …

Country Hiring Guidelines:

Invisible is a hybrid organization …

AI Interviewing Guidelines:

Our hiring team thoughtfully uses AI to support an efficient, engaging, and inclusive interview process. Since AI can also be a helpful tool … Click here to learn more about how we use AI and our guidelines for candidates.

Accessibility Statement:

We are committed to providing reasonable accommodations …

Equal Opportunity Statement:

We’re an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability, or veteran status, or any other basis protected by law.

Due to a high volume of candidates, Invisible may use automated decision‑maker technologies … If you object to our use of automated decision‑making please contact us.