Data Labeling Specialist (Machine Learning)

• High school diploma or equivalent
• Very Good understanding of engineering drawings or technical diagrams
• Associate degree or certificate in engineering, drafting, or related technical field
• Familiarity with P&ID symbols, components, and conventions (valves, pumps, instruments, piping)
• Experience in process industries (oil & gas, chemical, manufacturing, utilities)

• Accurately label and annotate visual and textual data (e.g., symbols, equipment, lines, and relationships) used to train ML models.
• Work closely with ML engineers to understand labeling guidelines, edge cases, and model requirements.
• Follow established labeling protocols and ontology structures to maintain consistency across datasets.
• Review, validate, and perform quality control on labeled datasets to ensure accuracy and completeness.
• Identify and report data quality issues or ambiguities in source data and propose improvements.
• Use internal or third-party annotation tools (e.g., CVAT, Label Studio, SuperAnnotate) to perform labeling tasks.
• Maintain detailed documentation of labeling processes and contribute to continuous improvement of annotation standards.
• Maintain detailed documentation of labeling decisions, conventions, and exceptions to ensure reproducibility and consistency
• Contribute to continuous improvement of labelling accuracy, tooling, and overall dataset quality

• Hands-on experience interpreting P&ID diagrams either through previous roles in data labeling, engineering support, drafting, or technical documentation.
• Familiarity with ISA/ISO P&ID standards (e.g., ISA-5.1, ISO 14617).
• Ability to identify and categorize P&ID components consistently, including instrument loops, signal lines, and control systems.
• Understanding of process flow and interconnections can infer functional relationships between components.

• Can help define and refine labeling guidelines and ontologies used for ML training.
• Experience working with engineering data standards or digital twin systems.
• Comfortable liaising with ML engineers and process engineers to ensure annotations reflect real-world process semantics.

• Strong attention to detail ability to spot small inconsistencies or edge cases.
• Discipline and patience can perform repetitive tasks with accuracy.
• Clear communication able to describe ambiguities or propose annotation improvements.
• Collaborative mindset works effectively with ML engineers and domain experts.
• Process-oriented thinking appreciates guidelines, consistency, and versioning of labeled data.

• They offer paid Indian public holidays as well as unlimited paid time off (after candidates first 3 months of work).
• Fully Remote - They value work/life balance and trust. They are happy to offer the flexibility to allow team members to work from home.
• Flexible Time Off - They have unlimited time off (after initial 3 months) and paid public holidays. They want their employees to rest and recharge and be their best selves
• Great Culture - It is a welcoming place to work, with team members that support and lift each other. In their 2025 annual review cycle, 58 out of 59 employees reported they would recommend them as a great place to work.

Expertia AI Technologies