Home › Companies › 5cd3ca7e Dea2 4f9c Ad9e 9fb7fa8d0232 182489917 1729 › Principal Scientist – AI/ML Specialization - WFH1651
Principal Scientist – AI/ML Specialization - WFH1651
5cd3ca7e Dea2 4f9c Ad9e 9fb7fa8d0232 182489917 1729 · Reston, VA, US, Reston, VA · Remote · Active · $100,000–$300,000 / year · ADP Workforce Now Recruiting
Job facts
| Field | Value |
|---|---|
| Company | 5cd3ca7e Dea2 4f9c Ad9e 9fb7fa8d0232 182489917 1729 |
| Title | Principal Scientist – AI/ML Specialization - WFH1651 |
| Normalized title | - |
| Department / team | - |
| Location | Reston, VA, United States |
| Work model | Remote / Remote |
| Employment type | Full Time |
| Salary | $100,000–$300,000 / year |
| Status | active |
| ATS provider | ADP Workforce Now Recruiting |
| Posted / first seen | 2026-04-22 / 2026-06-02 |
| Changed / last seen | 2026-06-06 / 2026-06-06 |
Related slices
| Page | What it contains | Open |
|---|---|---|
| Company jobs | Active postings from 5cd3ca7e Dea2 4f9c Ad9e 9fb7fa8d0232 182489917 1729. | Open |
| Company breakdowns | Role, location, ATS, and work model facets for this company. | Open |
| ATS provider jobs | Active postings observed through ADP Workforce Now Recruiting. | Open |
| Provider filtered search | The same provider as a filtered job collection. | Open |
| City jobs | Active postings in Reston. | Open |
| Work model jobs | Active Remote postings. | Open |
| Lifecycle events | Open, update, close, and reopen events for this posting. | Open |
| Original posting | Canonical source or apply URL captured from the ATS. | Open |
Linked records
| Company | 5cd3ca7e Dea2 4f9c Ad9e 9fb7fa8d0232 182489917 1729 |
| Source | 481c0487-24ee-4787-9f14-9a9b25a8d478 |
| ATS provider | ADP Workforce Now Recruiting |
Description
Clearance Level: Public Trust (Secret Eligible)
US Citizenship: Required
Job Classification: Full Time
Location: Remote
Years of Experience: 10+ years of relevant experience
Education Level: Advanced degree (MS or PhD) in Electrical Engineering, Computer Science, Applied Mathematics, or a closely related quantitative field. Experience may be considered in place of education requirement.
Briefly Describe the Work:
GITI is seeking a Principal Scientist to serve as the senior technical authority on an R&D program focused on passive RF emitter identification and network analysis from real-time sensor data streams. The Principal Scientist leads independent, hands-on analysis of NDF (Network Description File) sensor datasets, provides technical direction across parallel research threads, and serves as the primary technical advisor to the government sponsor. The role spans the full research lifecycle: formulating hypotheses, writing and executing analytical code in Python and Jupyter notebooks, interpreting and validating results, and communicating findings to both technical peers and non-specialist stakeholders. This is a deeply technical, hands-on position — the Principal Scientist conducts analysis directly and does not delegate technical work as a substitute for personal proficiency. The candidate will work within a small, distributed team operating in air-gapped Linux environments on resource-constrained tactical edge hardware, with no cloud computing.
Responsibilities:
Conduct independent, hands-on data analysis on RF sensor datasets using Python and Jupyter notebooks — formulating hypotheses, writing and running analytical code, interpreting results, and producing findings that directly advance program research objectives Provide technical advice and research direction across a multidisciplinary team; define analytical objectives, review and validate technical outputs from AI/ML engineers and software developers, and ensure coherence across parallel research threads Serve as primary technical advisor to the government sponsor: translate operational requirements into research objectives, communicate findings clearly to non-specialist stakeholders, and maintain program alignment with sponsor priorities through written reports and technical presentations Design and execute analytical investigations into RF sensor data quality, emitter behavior, and attribution reliability — including characterizing error sources, identifying systematic artifacts, and developing methods to distinguish real physical signatures from sensor or processing artifacts Produce technical documentation — working notes, research findings, monthly status reports, and briefing materials — that accurately represent the scope and confidence level of analytical results
Expert-level career professional recognized as a technical authority in RF systems, signals intelligence, or a closely related applied domain. Exercises broad independent judgment in defining research approach, evaluating methods, and interpreting results. Operates with minimal supervision; accountable for the scientific integrity and practical relevance of program research outputs. Advanced degree (MS or PhD) with 10+ years of hands-on applied R&D experience.
Required Skills:
10+ years of hands-on applied R&D experience in RF systems, signals intelligence, electronic warfare, or related domains. Proven ability to quickly acquire domain knowledge; specifically in the areas of wireless digital communications and military techniques, tactics, and procedures Demonstrated ability to independently develop and execute data analyses in Python or equivalent tools on real sensor datasets; must be capable of writing production-quality analytical code, not merely directing others to do so Experience addressing common problems with large quantities of real-world data, such as imputation, noise, bias, and errors Track record of working effectively on constrained-hardware edge systems — no cloud, no discrete GPU — with attention to computational efficiency and multi-core, multi-thread performance on x86 platforms
Desired Skills:
Deep familiarity with RF signal characteristics, sensor phenomenology, and the interpretation of passive receiver data — including recognition of processing artifacts, attribution ambiguities, and the limits of sensor-derived measurements Hands-on experience applying machine learning — particularly metric learning, deep learning networks, or similarity-learning architectures — to RF or time-series signal data, including feature engineering, training pipeline development, and model validation Familiarity with TDMA network protocols, emitter identification techniques (CID/PID), and the signal processing challenges of dense, contested electromagnetic environments Experience with interferometric direction-finding, TDOA geolocation, or related passive geolocation methods, including practical knowledge of their failure modes and accuracy limitations Experience with binary serialization formats (FlatBuffers, Protocol Buffers) and high-throughput sensor data pipelines operating in near-real-time on resource-constrained hardware Background in statistical signal processing — error ellipses, bearing estimation uncertainty, feature reliability under noise — with the ability to distinguish statistically significant findings from artifacts of small sample size or improper normalization
Relevant Certifications:
Professional certifications in data science, signal processing, or related technical fields. Advanced academic credentials (PhD, MS) in a relevant quantitative discipline are strongly preferred and may substitute for certifications.
Global InfoTek, Inc. is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, or disability.
About Global InfoTek, Inc. Global InfoTek Inc. has an award-winning track record of designing, developing, and deploying best-of-breed technologies that address the nation’s pressing cyber and advanced technology needs. GITI has rapidly merged pioneering technologies, operational effectiveness, and best business practices for over two decades.
Full job record
| Job ID | 0fcef12ec831c5e209ee830264846ae1cb2b1721 |
| Org ID | 5b455315-28f9-4494-af24-558b05f2fc99 |
| Source ID | 481c0487-24ee-4787-9f14-9a9b25a8d478 |
| Board ID | 481c0487-24ee-4787-9f14-9a9b25a8d478 |
| Provider | adp_workforcenow |
| Provider Job Key | 543340 |
| Title | Principal Scientist – AI/ML Specialization - WFH1651 |
| Normalized Title | — |
| Status | active |
| Active | yes |
| Location Text | Reston, VA, US, Reston, VA |
| Department | — |
| Team | — |
| Employment Type | full_time |
| Workplace Type | remote |
| Remote Policy | remote |
| Country | United States |
| Region | VA |
| City | Reston |
| Salary Raw | 100000.00 To 300000.00 (USD) Annually |
| Salary Min | 100,000 |
| Salary Max | 300,000 |
| Salary Currency | USD |
| Salary Period | year |
| Source URL | https://workforcenow.adp.com/mascsr/default/mdf/recruitment/recruitment.html?cid=5cd3ca7e-dea2-4f9c-ad9e-9fb7fa8d0232&ccId=182489917_1729&lang=en_US&type=JS&jobId=543340&jwId=9200920245062_1 |
| Apply URL | https://workforcenow.adp.com/mascsr/default/mdf/recruitment/recruitment.html?cid=5cd3ca7e-dea2-4f9c-ad9e-9fb7fa8d0232&ccId=182489917_1729&lang=en_US&type=JS&jobId=543340&jwId=9200920245062_1 |
| First Seen At | 2026-06-02 09:26:19Z |
| Last Seen At | 2026-06-06 20:19:53Z |
| Last Checked At | 2026-06-06 20:19:53Z |
| Last Changed At | 2026-06-06 20:19:53Z |
| Inactive At | — |
| Source Posted At | 2026-04-22 19:35:00Z |
| Source Updated At | — |
| Raw Payload Uri | s3://job-postings-prod-raw-590183727216/raw/provider=adp_workforcenow/board=5cd3ca7e-dea2-4f9c-ad9e-9fb7fa8d0232|182489917_1729/date=2026-06-06/2026-06-06T20-19-53-099Z-2d779624b59c1201944e802da5c2804de95bf052b2af0fd8f13dbde129d007a9.json |
Event Fields
{
"content_hash": "6fd263a047339bc1ba5110547b8a1afad1e667659b7411b9d918753000f9e2e8",
"source_hash": "e2f122bad42f62f2bd7313127e12a06ce6910fb5ce59e13815d59de1fa74da2f",
"last_changed_at": "2026-06-06T20:19:53.498Z",
"active_status": "active"
}Parsed Structured
{
"language": "en",
"location": {
"raw": "Reston, VA, US, Reston, VA",
"city": "Reston",
"region": "VA",
"country": "United States",
"is_remote": false,
"confidence": 0.95
},
"salary_max": 300000,
"salary_min": 100000,
"inferred_at": "2026-06-06T20:19:53.494Z",
"launch_scope": {
"reason": "english_us_canada",
"included": true,
"language": "en",
"location": {
"raw": "Reston, VA, US, Reston, VA",
"city": "Reston",
"region": "VA",
"country": "United States",
"is_remote": false,
"confidence": 0.95
},
"countries": [
"United States"
]
},
"remote_policy": "remote",
"salary_period": "year",
"workplace_type": "remote",
"salary_currency": "USD"
}Extensions
{}Native Structured
{
"detail": {
"links": [],
"itemID": "9200920245062_1",
"postDate": "2026-04-22T15:35:00.000-04:00",
"payGradeRange": {
"maximumRate": {
"amountValue": 300000,
"currencyCode": "USD"
},
"minimumRate": {
"amountValue": 100000,
"currencyCode": "USD"
}
},
"workLevelCode": {
"shortName": "Full-time Regular"
},
"customFieldGroup": {
"codeFields": [
{
"nameCode": {
"codeValue": "SalaryType"
},
"codeValue": "AN",
"shortName": "Annually"
},
{
"nameCode": {
"codeValue": "SalaryRangeType"
},
"codeValue": "RANGE",
"shortName": "RANGE"
}
],
"dateFields": [
{
"nameCode": {
"codeValue": "PostingDate"
},
"dateValue": "2026-04-22T15:35Z"
},
{
"nameCode": {
"codeValue": "CurrentServerDateTime"
},
"dateValue": "2026-06-06T16:19Z"
}
],
"numberFields": [
{
"numberValue": 0,
"categoryCode": {
"codeValue": "ApplicantCount"
}
},
{
"categoryCode": {
"codeValue": "AwardAmount"
}
}
],
"stringFields": [
{
"nameCode": {
"codeValue": "ExternalJobID"
},
"stringValue": "543340"
},
{
"nameCode": {
"codeValue": "CareerCenterRefId"
}
},
{
"nameCode": {
"codeValue": "GuidelineOid"
}
},
{
"nameCode": {
"codeValue": "CurrencySymbolOrCode"
}
},
{
"nameCode": {
"codeValue": "HomeDepartment"
},
"stringValue": ""
},
{
"nameCode": {
"codeValue": "JobClass"
},
"stringValue": "Senior Management"
},
{
"nameCode": {
"codeValue": "SalaryRange"
},
"stringValue": "100000.00 To 300000.00 (USD) Annually"
}
],
"indicatorFields": [
{
"nameCode": {
"codeValue": "PriortyStatusFlag"
},
"indicatorValue": false
},
{
"nameCode": {
"codeValue": "InternalPostingFlag"
},
"indicatorValue": false
},
{
"nameCode": {
"codeValue": "MinValue"
},
"indicatorValue": true
},
{
"nameCode": {
"codeValue": "IsVsidApplicable"
},
"indicatorValue": true
},
{
"nameCode": {
"codeValue": "IsSassDlReqForExtPostFlag"
},
"indicatorValue": false
},
{
"nameCode": {
"codeValue": "IsSassDlReqForIntPostFlag"
},
"indicatorValue": false
},
{
"nameCode": {
"codeValue": "IsMonetaryFlag"
},
"indicatorValue": false
},
{
"nameCode": {
"codeValue": "IsNonMonetaryFlag"
},
"indicatorValue": false
}
]
},
"requisitionTitle": "Principal Scientist – AI/ML Specialization - WFH1651",
"clientRequisitionID": "1651",
"organizationalUnits": [],
"postingInstructions": [],
"additionalProperties": {},
"requisitionLocations": [
{
"address": {
"cityName": "Reston",
"postalCode": "20191",
"countrySubdivisionLevel1": {
"codeValue": "VA"
}
},
"nameCode": {
"shortName": " Reston, VA, US"
},
"aliasNames": []
}
],
"screeningRequirements": [],
"requisitionDescription": "<div><div><p style='margin:0in;font-size:16px;font-family:\"Arial\",sans-serif;margin-bottom:3.0pt;' data-pasted=\"true\"><strong>Clearance Level: </strong>Public Trust (Secret Eligible)</p><p style='margin:0in;font-size:16px;font-family:\"Arial\",sans-serif;margin-bottom:3.0pt;'><strong>US Citizenship: </strong>Required</p><p style='margin:0in;font-size:16px;font-family:\"Arial\",sans-serif;margin-bottom:3.0pt;'><strong>Job Classification: </strong>Full Time</p><p style='margin:0in;font-size:16px;font-family:\"Arial\",sans-serif;margin-bottom:3.0pt;'><strong>Location: </strong>Remote</p><p style='margin:0in;font-size:16px;font-family:\"Arial\",sans-serif;margin-bottom:3.0pt;'><strong>Years of Experience: </strong>10+ years of relevant experience</p><p style='margin:0in;font-size:16px;font-family:\"Arial\",sans-serif;margin-bottom:3.0pt;'><br></p><p style='margin:0in;font-size:16px;font-family:\"Arial\",sans-serif;margin-bottom:3.0pt;'><strong>Education Level: </strong>Advanced degree (MS or PhD) in Electrical Engineering, Computer Science, Applied Mathematics, or a closely related quantitative field. Experience may be considered in place of education requirement.</p><p style='margin:0in;font-size:16px;font-family:\"Arial\",sans-serif;margin-bottom:3.0pt;'><br></p><p style='margin:0in;font-size:16px;font-family:\"Arial\",sans-serif;margin-top:12.0pt;margin-right:0in;margin-bottom:6.0pt;margin-left:0in;'><strong><u>Briefly Describe the Work:</u></strong></p><p style='margin:0in;font-size:16px;font-family:\"Arial\",sans-serif;margin-bottom:6.0pt;'>GITI is seeking a Principal Scientist to serve as the senior technical authority on an R&D program focused on passive RF emitter identification and network analysis from real-time sensor data streams. The Principal Scientist leads independent, hands-on analysis of NDF (Network Description File) sensor datasets, provides technical direction across parallel research threads, and serves as the primary technical advisor to the government sponsor. The role spans the full research lifecycle: formulating hypotheses, writing and executing analytical code in Python and Jupyter notebooks, interpreting and validating results, and communicating findings to both technical peers and non-specialist stakeholders. This is a deeply technical, hands-on position — the Principal Scientist conducts analysis directly and does not delegate technical work as a substitute for personal proficiency. The candidate will work within a small, distributed team operating in air-gapped Linux environments on resource-constrained tactical edge hardware, with no cloud computing.</p><p style='margin:0in;font-size:16px;font-family:\"Arial\",sans-serif;margin-bottom:3.0pt;'><br></p><p style='margin:0in;font-size:16px;font-family:\"Arial\",sans-serif;margin-top:12.0pt;margin-right:0in;margin-bottom:6.0pt;margin-left:0in;'><strong><u>Responsibilities:</u></strong></p><div style='margin:0in;font-size:16px;font-family:\"Arial\",sans-serif;'><ul style=\"margin-bottom:0in;margin-left: 0in;\"><li style=\"margin:0in;font-size:16px;font-family: initial;\">Conduct independent, hands-on data analysis on RF sensor datasets using Python and Jupyter notebooks — formulating hypotheses, writing and running analytical code, interpreting results, and producing findings that directly advance program research objectives</li><li style=\"margin:0in;font-size:16px;font-family: initial;\">Provide technical advice and research direction across a multidisciplinary team; define analytical objectives, review and validate technical outputs from AI/ML engineers and software developers, and ensure coherence across parallel research threads</li><li style=\"margin:0in;font-size:16px;font-family: initial;\">Serve as primary technical advisor to the government sponsor: translate operational requirements into research objectives, communicate findings clearly to non-specialist stakeholders, and maintain program alignment with sponsor priorities through written reports and technical presentations</li><li style=\"margin:0in;font-size:16px;font-family: initial;\">Design and execute analytical investigations into RF sensor data quality, emitter behavior, and attribution reliability — including characterizing error sources, identifying systematic artifacts, and developing methods to distinguish real physical signatures from sensor or processing artifacts</li><li style=\"margin:0in;font-size:16px;font-family: initial;\">Produce technical documentation — working notes, research findings, monthly status reports, and briefing materials — that accurately represent the scope and confidence level of analytical results</li></ul></div><p style='margin:0in;font-size:16px;font-family:\"Arial\",sans-serif;margin-bottom:3.0pt;'><br></p><p style='margin:0in;font-size:16px;font-family:\"Arial\",sans-serif;margin-bottom:6.0pt;'>Expert-level career professional recognized as a technical authority in RF systems, signals intelligence, or a closely related applied domain. Exercises broad independent judgment in defining research approach, evaluating methods, and interpreting results. Operates with minimal supervision; accountable for the scientific integrity and practical relevance of program research outputs. Advanced degree (MS or PhD) with 10+ years of hands-on applied R&D experience.</p><p style='margin:0in;font-size:16px;font-family:\"Arial\",sans-serif;margin-bottom:3.0pt;'><br></p><p style='margin:0in;font-size:16px;font-family:\"Arial\",sans-serif;margin-top:12.0pt;margin-right:0in;margin-bottom:6.0pt;margin-left:0in;'><strong><u>Required Skills:</u></strong></p><div style='margin:0in;font-size:16px;font-family:\"Arial\",sans-serif;'><ul style=\"margin-bottom:0in;margin-left: 0in;\"><li style=\"margin:0in;font-size:16px;font-family: initial;\">10+ years of hands-on applied R&D experience in RF systems, signals intelligence, electronic warfare, or related domains.</li><li style=\"margin:0in;font-size:16px;font-family: initial;\">Proven ability to quickly acquire domain knowledge; specifically in the areas of wireless digital communications and military techniques, tactics, and procedures</li><li style=\"margin:0in;font-size:16px;font-family: initial;\">Demonstrated ability to independently develop and execute data analyses in Python or equivalent tools on real sensor datasets; must be capable of writing production-quality analytical code, not merely directing others to do so</li><li style=\"margin:0in;font-size:16px;font-family: initial;\">Experience addressing common problems with large quantities of real-world data, such as imputation, noise, bias, and errors</li><li style=\"margin:0in;font-size:16px;font-family: initial;\">Track record of working effectively on constrained-hardware edge systems — no cloud, no discrete GPU — with attention to computational efficiency and multi-core, multi-thread performance on x86 platforms</li></ul></div><p style='margin:0in;font-size:16px;font-family:\"Arial\",sans-serif;margin-bottom:3.0pt;'><br></p><p style='margin:0in;font-size:16px;font-family:\"Arial\",sans-serif;margin-top:12.0pt;margin-right:0in;margin-bottom:6.0pt;margin-left:0in;'><strong><u>Desired Skills:</u></strong></p><div style='margin:0in;font-size:16px;font-family:\"Arial\",sans-serif;'><ul style=\"margin-bottom:0in;margin-left: 0in;\"><li style=\"margin:0in;font-size:16px;font-family: initial;\">Deep familiarity with RF signal characteristics, sensor phenomenology, and the interpretation of passive receiver data — including recognition of processing artifacts, attribution ambiguities, and the limits of sensor-derived measurements</li><li style=\"margin:0in;font-size:16px;font-family: initial;\">Hands-on experience applying machine learning — particularly metric learning, deep learning networks, or similarity-learning architectures — to RF or time-series signal data, including feature engineering, training pipeline development, and model validation</li><li style=\"margin:0in;font-size:16px;font-family: initial;\">Familiarity with TDMA network protocols, emitter identification techniques (CID/PID), and the signal processing challenges of dense, contested electromagnetic environments</li><li style=\"margin:0in;font-size:16px;font-family: initial;\">Experience with interferometric direction-finding, TDOA geolocation, or related passive geolocation methods, including practical knowledge of their failure modes and accuracy limitations</li><li style=\"margin:0in;font-size:16px;font-family: initial;\">Experience with binary serialization formats (FlatBuffers, Protocol Buffers) and high-throughput sensor data pipelines operating in near-real-time on resource-constrained hardware</li><li style=\"margin:0in;font-size:16px;font-family: initial;\">Background in statistical signal processing — error ellipses, bearing estimation uncertainty, feature reliability under noise — with the ability to distinguish statistically significant findings from artifacts of small sample size or improper normalization</li></ul></div><p style='margin:0in;font-size:16px;font-family:\"Arial\",sans-serif;margin-bottom:3.0pt;'><br></p><p style='margin:0in;font-size:16px;font-family:\"Arial\",sans-serif;margin-top:12.0pt;margin-right:0in;margin-bottom:6.0pt;margin-left:0in;'><strong><u>Relevant Certifications:</u></strong></p><div style='margin:0in;font-size:16px;font-family:\"Arial\",sans-serif;'><ul style=\"margin-bottom:0in;margin-left: 0in;\"><li style=\"margin:0in;font-size:16px;font-family: initial;\">Professional certifications in data science, signal processing, or related technical fields. Advanced academic credentials (PhD, MS) in a relevant quantitative discipline are strongly preferred and may substitute for certifications.</li></ul></div><p style='margin:0in;font-size:16px;font-family:\"Arial\",sans-serif;margin-bottom:3.0pt;'><br></p><p style='margin:0in;font-size:16px;font-family:\"Arial\",sans-serif;margin-top:12.0pt;margin-right:0in;margin-bottom:3.0pt;margin-left:0in;'><strong>Global InfoTek, Inc.</strong> is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, or disability.</p><p style='margin:0in;font-size:16px;font-family:\"Arial\",sans-serif;margin-bottom:3.0pt;'><br></p><p style='margin:0in;font-size:16px;font-family:\"Arial\",sans-serif;margin-bottom:3.0pt;'><strong>About Global InfoTek, Inc.</strong> Global InfoTek Inc. has an award-winning track record of designing, developing, and deploying best-of-breed technologies that address the nation’s pressing cyber and advanced technology needs. GITI has rapidly merged pioneering technologies, operational effectiveness, and best business practices for over two decades.</p><p><br></p></div></div>\n",
"sponsoredVisaTypeCodes": []
},
"list_job": {
"links": [],
"itemID": "9200920245062_1",
"postDate": "2026-04-22T15:35:00.000-04:00",
"payGradeRange": {
"maximumRate": {
"amountValue": 300000,
"currencyCode": "USD"
},
"minimumRate": {
"amountValue": 100000,
"currencyCode": "USD"
}
},
"workLevelCode": {
"shortName": "Full-time Regular"
},
"customFieldGroup": {
"codeFields": [
{
"nameCode": {
"codeValue": "SalaryType"
},
"codeValue": "AN",
"shortName": "Annually"
},
{
"nameCode": {
"codeValue": "SalaryRangeType"
},
"codeValue": "RANGE",
"shortName": "RANGE"
}
],
"dateFields": [
{
"nameCode": {
"codeValue": "PostingDate"
},
"dateValue": "2026-04-22T15:35Z"
},
{
"nameCode": {
"codeValue": "CurrentServerDateTime"
},
"dateValue": "2026-06-06T16:19Z"
}
],
"numberFields": [
{
"numberValue": 0,
"categoryCode": {
"codeValue": "ApplicantCount"
}
},
{
"categoryCode": {
"codeValue": "AwardAmount"
}
}
],
"stringFields": [
{
"nameCode": {
"codeValue": "ExternalJobID"
},
"stringValue": "543340"
},
{
"nameCode": {
"codeValue": "CareerCenterRefId"
}
},
{
"nameCode": {
"codeValue": "GuidelineOid"
}
},
{
"nameCode": {
"codeValue": "CurrencySymbolOrCode"
}
},
{
"nameCode": {
"codeValue": "HomeDepartment"
},
"stringValue": ""
},
{
"nameCode": {
"codeValue": "JobClass"
},
"stringValue": "Senior Management"
},
{
"nameCode": {
"codeValue": "SalaryRange"
},
"stringValue": "100000.00 To 300000.00 (USD) Annually"
}
],
"indicatorFields": [
{
"nameCode": {
"codeValue": "PriortyStatusFlag"
},
"indicatorValue": false
},
{
"nameCode": {
"codeValue": "InternalPostingFlag"
},
"indicatorValue": false
},
{
"nameCode": {
"codeValue": "MinValue"
},
"indicatorValue": true
},
{
"nameCode": {
"codeValue": "IsVsidApplicable"
},
"indicatorValue": true
},
{
"nameCode": {
"codeValue": "IsSassDlReqForExtPostFlag"
},
"indicatorValue": false
},
{
"nameCode": {
"codeValue": "IsSassDlReqForIntPostFlag"
},
"indicatorValue": false
},
{
"nameCode": {
"codeValue": "IsMonetaryFlag"
},
"indicatorValue": false
},
{
"nameCode": {
"codeValue": "IsNonMonetaryFlag"
},
"indicatorValue": false
}
]
},
"requisitionTitle": "Principal Scientist – AI/ML Specialization - WFH1651",
"clientRequisitionID": "1651",
"organizationalUnits": [],
"postingInstructions": [],
"additionalProperties": {},
"requisitionLocations": [
{
"address": {
"cityName": "Reston",
"postalCode": "20191",
"countrySubdivisionLevel1": {
"codeValue": "VA"
}
},
"nameCode": {
"shortName": " Reston, VA, US"
},
"aliasNames": []
}
],
"screeningRequirements": [],
"sponsoredVisaTypeCodes": []
},
"detail_meta": {
"url": "https://workforcenow.adp.com/mascsr/default/careercenter/public/events/staffing/v1/job-requisitions/543340?cid=5cd3ca7e-dea2-4f9c-ad9e-9fb7fa8d0232&ccId=182489917_1729&lang=en_US&locale=en_US",
"http_status": 200,
"content_type": "application/json;charset=UTF-8",
"response_bytes": 15369
},
"detail_errors": []
}Get this page with API
Rendered from the bluedoor Job Postings API. Reproduce it:
GET https://api.bluedoor.sh/job-postings/v1/jobs/0fcef12ec831c5e209ee830264846ae1cb2b1721?include=descriptionJSONGET https://api.bluedoor.sh/job-postings/v1/orgs/5b455315-28f9-4494-af24-558b05f2fc99JSONGET https://api.bluedoor.sh/job-postings/v1/sources/481c0487-24ee-4787-9f14-9a9b25a8d478JSONGET https://api.bluedoor.sh/job-postings/v1/jobs/0fcef12ec831c5e209ee830264846ae1cb2b1721/eventsJSON