Data Labeling Specialist (Machine Learning)
Job Description
Greetings from Synergy Resource Solutions ( a leading HR Consultancy Firm.\nOur client company operates within the Industrial...
Job Description
Greetings from Synergy Resource Solutions ( a leading HR Consultancy Firm.
\nOur client company operates within the Industrial Intelligence sector, focusing on enhancing operational efficiency and safety in complex industrial environments. Their solutions bridge the gap between physical assets and digital systems, enabling real-time data-driven decision-making.
\nPosition: Data Labeling Specialist (Machine Learning)
\nExperience:- 6+years (minimum 2 years relevant experience)
\nLocation:- Anywhere in India / Remote
\nQualification:
\n- \n
- High school diploma or equivalent \n
- Very Good understanding of engineering drawings or technical diagrams \n
- Associate degree or certificate in engineering, drafting, or related technical field \n
- Familiarity with P&ID symbols, components, and conventions (valves, pumps, instruments, piping) \n
- Experience in process industries (oil & gas, chemical, manufacturing, utilities) \n
Communicate skills required for this role: Strong verbal and written communication skills in English for effective collaboration across teams.
\nOffice Timing & Working Days/Week Details: Regular business hours in India
\nFixed CTC: 50000 - 70000 INR monthly
\nAbout the role
\nData Labeling Specialist (Machine Learning)
\nWe are looking for a detail-oriented and methodical Data Labeling Specialist to support the development of machine learning models.
\nYou will play a crucial role in preparing high-quality, accurately labeled datasets that train and validate our AI systems used for interpreting engineering diagrams, process data, and industrial information models.
\nThis position is ideal for someone with strong attention to detail, patience, and a passion for working with structured and visual data.
\nResponsibilities:
\n- \n
- Accurately label and annotate visual and textual data (e.g., symbols, equipment, lines, and relationships) used to train ML models. \n
- Work closely with ML engineers to understand labeling guidelines, edge cases, and model requirements. \n
- Follow established labeling protocols and ontology structures to maintain consistency across datasets. \n
- Review, validate, and perform quality control on labeled datasets to ensure accuracy and completeness. \n
- Identify and report data quality issues or ambiguities in source data and propose improvements. \n
- Use internal or third-party annotation tools (e.g., CVAT, Label Studio, SuperAnnotate) to perform labeling tasks. \n
- Maintain detailed documentation of labeling processes and contribute to continuous improvement of annotation standards. \n
- Maintain detailed documentation of labeling decisions, conventions, and exceptions to ensure reproducibility and consistency \n
- Contribute to continuous improvement of labelling accuracy, tooling, and overall dataset quality \n
Required Skills
\n- \n
- Hands-on experience interpreting P&ID diagrams either through previous roles in data labeling, engineering support, drafting, or technical documentation. \n
- Familiarity with ISA/ISO P&ID standards (e.g., ISA-5.1, ISO 14617). \n
- Ability to identify and categorize P&ID components consistently, including instrument loops, signal lines, and control systems . \n
- Understanding of process flow and interconnections can infer functional relationships between components. \n
Preferred Skills
\nDeep familiarity with P&ID structures and industrial system logic .
\n- \n
- Can help define and refine labeling guidelines and ontologies used for ML training. \n
- Experience working with engineering data standards or digital twin systems . \n
- Comfortable liaising with ML engineers and process engineers to ensure annotations reflect real-world process semantics. \n
Additionally required abilities:
\n- \n
- Strong attention to detail ability to spot small inconsistencies or edge cases. \n
- Discipline and patience can perform repetitive tasks with accuracy. \n
- Clear communication able to describe ambiguities or propose annotation improvements. \n
- Collaborative mindset works effectively with ML engineers and domain experts. \n
- Process-oriented thinking appreciates guidelines, consistency, and versioning of labeled data. \n
Benefits:
\n- \n
- They offer paid Indian public holidays as well as unlimited paid time off (after candidates first 3 months of work). \n
- Fully Remote - They value work/life balance and trust. They are happy to offer the flexibility to allow team members to work from home. \n
- 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 \n
- 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. \n
Candidates having matching experience & qualification can share their updated resume with details of their present salary, expectations & notice period.
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