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Appen

Provides human-annotated data for AI training

Strategic Project Lead - Physical Engineering

Project ManagementPosted on Nov 12
Full-timeRemote in USANo salary listedRemote

About the Job

Requirements

  • A bachelor's degree or higher in Mechanical Engineering, Electrical Engineering, Civil Engineering, Structural Engineering or a closely related engineering discipline.
  • Three to five or more years of hands-on experience in at least one physical engineering discipline, with a background in applied engineering work and technical problem-solving.
  • Practical experience with engineering tools and computer-aided engineering workflows, including interpreting analyses and validating assumptions, constraints and results.
  • Experience working with technical artefacts such as computer-aided design or building information modeling models, engineering drawings, schematics, calculations, simulations or specifications.
  • Experience leading technical projects from initial scoping and estimation through execution and delivery, with direct involvement in the technical work.
  • Ability to evaluate engineering outputs, identify technical inaccuracies and establish quality standards and acceptance criteria.
  • Experience assessing or guiding technical specialists, with the judgement to recognise when additional expertise is required.
  • Ability to translate complex requirements into clear instructions and lead cross-functional teams.

Responsibilities

  • Translate customer requirements into realistic engineering tasks, data specifications and measurable acceptance criteria.
  • Assess technical requirements, review source files and develop representative samples to establish project feasibility.
  • Shape the physical engineering domain strategy across areas such as computer-aided design, building information modeling, computer-aided engineering and electrical engineering.
  • Develop multi-step engineering tasks and reference solutions that reflect real-world technical challenges across models, drawings, calculations and specifications.
  • Lead pilots from sample development and expert onboarding through quality assurance and customer acceptance.
  • Partner with Operations to transition projects into production, maintaining technical standards and ownership of domain quality as delivery scales.
  • Build and qualify a network of specialist engineering contributors and reviewers.
  • Work with Applied Researchers and tooling teams to develop technical verification methods and improve the consistency of engineering evaluations.
  • Provide effort estimates and identify specialist requirements, tooling needs and delivery risks to support commercial proposals.
  • Turn successful projects into reusable engineering samples, task libraries and delivery methods.

Desired Qualifications

  • A master's or PhD in Mechanical Engineering, Electrical Engineering, Civil Engineering, Structural Engineering or a related technical discipline.
  • Experience developing engineering data, technical tasks or evaluation resources for AI training, model evaluation or benchmarking.
  • Familiarity with computer-aided design/building information modeling interoperability, open engineering formats or structured validation of engineering models and documentation.
  • Experience building specialist contributor networks or leading distributed technical delivery with external engineering experts.

About the Company

Appen

Provides human-annotated data for AI training

Appen creates and curates large datasets used to train and improve artificial intelligence. It relies on a global, crowdsourced workforce to produce human-annotated data across text, images, audio, and video, which powers AI systems such as search engines and social feeds. Its products include data annotation services and platforms (like Figure Eight) and specialized data like mobile location data (Quadrant) to support ML pipelines. The company differentiates itself through scale, a broad range of data types, and a history of strategic acquisitions that expand its capabilities (e.g., Leapforce for search relevance, Figure Eight for annotation tooling, Quadrant for location data), enabling end-to-end data supply for enterprise AI. Its goal is to help organizations build reliable AI by providing high-quality labeled data and to capitalize on opportunities in generative and enterprise AI while returning to profitability under new leadership.

Company size
10,001+
Company stage
IPO
Headquarters
Sydney NSW, Australia
Founded
1996

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