bluedoor data·Job Postings API·bluedoor.sh ↗

HomeCompaniesCareers Jobyaviation Icims ComStaff Flight Research Machine Learning Engineer

Staff Flight Research Machine Learning Engineer

Careers Jobyaviation Icims Com · Santa Cruz, CA, US · Active · $224,000–$245,000 / year · iCIMS

Job facts

FieldValue
CompanyCareers Jobyaviation Icims Com
TitleStaff Flight Research Machine Learning Engineer
Normalized title-
Department / teamFlight Research
LocationSanta Cruz, CA, United States
Work model-
Employment typeFull Time
Salary$224,000–$245,000 / year
Statusactive
ATS provideriCIMS
Posted / first seen2024-06-06 / 2026-06-06
Changed / last seen2026-06-06 / 2026-06-06

Related slices

PageWhat it containsOpen
Company jobsActive postings from Careers Jobyaviation Icims Com.Open
Company breakdownsRole, location, ATS, and work model facets for this company.Open
ATS provider jobsActive postings observed through iCIMS.Open
Provider filtered searchThe same provider as a filtered job collection.Open
City jobsActive postings in Santa Cruz.Open
Department jobsActive postings in Flight Research.Open
Lifecycle eventsOpen, update, close, and reopen events for this posting.Open
Original postingCanonical source or apply URL captured from the ATS.Open

Linked records

CompanyCareers Jobyaviation Icims Com
Source8e5cd43b-3573-4a2e-a00f-07f733c827a1
ATS provideriCIMS

Description

Company Overview Joby Flight Research designs, develops, and flight-tests novel aircraft using a software-first autonomy approach. We build and deploy autonomy, perception, planning, and radar systems across conventional, electric, and hydrogen-electric aircraft in both CTOL and VTOL configurations. Overview Joby Flight Research is seeking a Staff Machine Learning Engineer to design and build state-of-the-art perception and reasoning algorithms into the Superpilot™ autonomy stack, enabling autonomous aircraft and associated ground systems to safely and autonomously navigate their complex environment, and define the standard of autonomous flight. In this role, you will be responsible for training models that enhance current Superpilot™ algorithms while investigating the application of cutting-edge research to expand autonomous flight capabilities. Your responsibilities will include filtering sensor data from flights, architecting dependable training infrastructure for datasets and models, and performing ongoing evaluations within our Operational Design Domain (ODD). By focusing on these tasks, you will play a vital part in achieving the detection and localization standards necessary for ensuring the safety of autonomous aviation. We are a small, high-impact team that values curiosity, technical initiative, and the ability to operate independently. You will collaborate deeply with controls, and flight software engineers to build a foundation that accelerates our path to safe, autonomous flight. The right candidate is a strong ML engineer who cares deeply about system integration, experiment reproducibility and traceability, and is able to contribute to different parts of the data pipeline where needed. Responsibilities Own the development of a Superpilot™ application (e.g. ground hazard avoidance, vision-based landing, detect and avoid) Lead product requirements and design architecture with multi-disciplinary teams—including controls, systems, and flight testing—to seamlessly embed and validate algorithms within our operational design domain Architect and deploy sophisticated algorithms for aircraft environment detection and tracking. By integrating deep learning with geometric computer vision, you will utilize multi-sensor inputs—including lidar, radar, and varied camera systems—to establish comprehensive 3D situational awareness Construct high-performance model training pipelines and extensive evaluation systems. These frameworks must ensure reliability by identifying performance nuances in complex edge cases and rare operational scenarios Drive continuous improvement of perception stacks through simulation. You will focus on optimizing latency and robustness to ensure peak performance during demanding flight conditions Engineer specialized diagnostic and visualization tools to extract meaningful insights from field data. This involves facilitating swift root-cause analysis and resolving perception challenges in active deployments Shape the future of the Superpilot™ autonomy system. Your contributions will define a perception architecture that sets new benchmarks for safety and reliability in the autonomous aviation industry Required At least 8 years of experience developing and implementing advanced perception frameworks for autonomous platforms, including aircraft, vehicles, or robotics Extensive practical knowledge of cutting-edge models for tracking and detecting objects within real-world applications Comprehensive understanding of geometric vision methods such as visual odometry, structure-from-motion, and stereo vision to facilitate accurate 3D estimation Competency in C++, Python, and PyTorch, along with various deep learning inference and training ecosystems Ability to rapidly prototype code during experimentation, and support deployment by shipping scalable and high quality code A creative approach to engineering that includes a history of mitigating performance delays and addressing complex operational edge cases Direct experience maintaining commercial autonomous systems by diagnosing technical hurdles and improving live system dependability Strong interpersonal skills necessary to succeed within a fast-paced, multidisciplinary team focused on safety-critical engineering Desired Expertise in developing supporting data curation and model experimentation pipelines to support ML experimentation Experience with ROS 2 or other robotics middlewares Experience with recent generative AI world simulation tooling (e.g. NVIDIA Isaac Sim, Omniverse) Experience deploying models onto GPUs and other accelerators for production-level embedded systems Experience with autonomous vehicles Experience in processing aircraft data (GPS, inertial, air data, radio data, etc.) Expert-level software engineering: deep expertise in architecting and writing clean, scalable, and maintainable code Experience with version control and CI/CD platforms, able to manage your software through its entire lifecycle (development, testing, deployment) Experience deploying ML models in a production environment using modern MLOps principles and tools (e.g., MLflow, Kubeflow) Experience mentoring small teams of perception engineers up to 5 people Compensation at Joby is a combination of base pay and Restricted Stock Units (RSUs). The target base pay for this position is $224,000 - $245,000/yr. The compensation package will be determined by job-related knowledge, skills, and experience. Joby also offers a comprehensive benefits package, including paid time off, healthcare benefits, a 401(k) plan with a company match, an employee stock purchase plan (ESPP), short-term and long-term disability coverage, life insurance, and more. Additional Information Joby Aviation is an equal opportunity employer.

Full job record

Job IDf2c6412bad4b698627ced1eda89dce21a09e4b88
Org IDfacea025-a75a-4397-bb0b-e37529b02821
Source ID8e5cd43b-3573-4a2e-a00f-07f733c827a1
Board ID8e5cd43b-3573-4a2e-a00f-07f733c827a1
Providericims
Provider Job Key5094
TitleStaff Flight Research Machine Learning Engineer
Normalized Title
Statusactive
Activeyes
Location TextSanta Cruz, CA, US
DepartmentFlight Research
Team
Employment Typefull_time
Workplace Type
Remote Policy
CountryUnited States
RegionCA
CitySanta Cruz
Salary RawCompany Overview Joby Flight Research designs, develops, and flight-tests novel aircraft using a software-first autonomy approach. We build and deploy autonomy, perception, planning, and radar systems across conventional, electric, and hydrogen-electric aircraft in both CTOL and VTOL configurations. Overview Joby Flight Research is seeking a Staff Machine Learning Engineer to design and build state-of-the-art perception and reasoning algorithms into the Superpilot™ autonomy stack, enabling autonomous aircraft and associated ground systems to safely and autonomously navigate their complex environment, and define the standard of autonomous flight. In this role, you will be responsible for training models that enhance current Superpilot™ algorithms while investigating the application of cutting-edge research to expand autonomous flight capabilities. Your responsibilities will include filtering sensor data from flights, architecting dependable training infrastructure for datasets and models, and performing ongoing evaluations within our Operational Design Domain (ODD). By focusing on these tasks, you will play a vital part in achieving the detection and localization standards necessary for ensuring the safety of autonomous aviation. We are a small, high-impact team that values curiosity, technical initiative, and the ability to operate independently. You will collaborate deeply with controls, and flight software engineers to build a foundation that accelerates our path to safe, autonomous flight. The right candidate is a strong ML engineer who cares deeply about system integration, experiment reproducibility and traceability, and is able to contribute to different parts of the data pipeline where needed. Responsibilities Own the development of a Superpilot™ application (e.g. ground hazard avoidance, vision-based landing, detect and avoid) Lead product requirements and design architecture with multi-disciplinary teams—including controls, systems, and flight testing—to seamlessly embed and validate algorithms within our operational design domain Architect and deploy sophisticated algorithms for aircraft environment detection and tracking. By integrating deep learning with geometric computer vision, you will utilize multi-sensor inputs—including lidar, radar, and varied camera systems—to establish comprehensive 3D situational awareness Construct high-performance model training pipelines and extensive evaluation systems. These frameworks must ensure reliability by identifying performance nuances in complex edge cases and rare operational scenarios Drive continuous improvement of perception stacks through simulation. You will focus on optimizing latency and robustness to ensure peak performance during demanding flight conditions Engineer specialized diagnostic and visualization tools to extract meaningful insights from field data. This involves facilitating swift root-cause analysis and resolving perception challenges in active deployments Shape the future of the Superpilot™ autonomy system. Your contributions will define a perception architecture that sets new benchmarks for safety and reliability in the autonomous aviation industry Required At least 8 years of experience developing and implementing advanced perception frameworks for autonomous platforms, including aircraft, vehicles, or robotics Extensive practical knowledge of cutting-edge models for tracking and detecting objects within real-world applications Comprehensive understanding of geometric vision methods such as visual odometry, structure-from-motion, and stereo vision to facilitate accurate 3D estimation Competency in C++, Python, and PyTorch, along with various deep learning inference and training ecosystems Ability to rapidly prototype code during experimentation, and support deployment by shipping scalable and high quality code A creative approach to engineering that includes a history of mitigating performance delays and addressing complex operational edge cases Direct experience maintaining commercial autonomous systems by diagnosing technical hurdles and improving live system dependability Strong interpersonal skills necessary to succeed within a fast-paced, multidisciplinary team focused on safety-critical engineering Desired Expertise in developing supporting data curation and model experimentation pipelines to support ML experimentation Experience with ROS 2 or other robotics middlewares Experience with recent generative AI world simulation tooling (e.g. NVIDIA Isaac Sim, Omniverse) Experience deploying models onto GPUs and other accelerators for production-level embedded systems Experience with autonomous vehicles Experience in processing aircraft data (GPS, inertial, air data, radio data, etc.) Expert-level software engineering: deep expertise in architecting and writing clean, scalable, and maintainable code Experience with version control and CI/CD platforms, able to manage your software through its entire lifecycle (development, testing, deployment) Experience deploying ML models in a production environment using modern MLOps principles and tools (e.g., MLflow, Kubeflow) Experience mentoring small teams of perception engineers up to 5 people Compensation at Joby is a combination of base pay and Restricted Stock Units (RSUs). The target base pay for this position is $224,000 - $245,000/yr. The compensation package will be determined by job-related knowledge, skills, and experience. Joby also offers a comprehensive benefits package, including paid time off, healthcare benefits, a 401(k) plan with a company match, an employee stock purchase plan (ESPP), short-term and long-term disability coverage, life insurance, and more. Additional Information Joby Aviation is an equal opportunity employer.
Salary Min224,000
Salary Max245,000
Salary CurrencyUSD
Salary Periodyear
Source URLhttps://careers-jobyaviation.icims.com/jobs/5094/staff-flight-research-machine-learning-engineer/job
Apply URLhttps://careers-jobyaviation.icims.com/jobs/5094/staff-flight-research-machine-learning-engineer/job
First Seen At2026-06-06 08:30:36Z
Last Seen At2026-06-06 08:30:36Z
Last Checked At2026-06-06 08:30:36Z
Last Changed At2026-06-06 08:30:36Z
Inactive At
Source Posted At2024-06-06 08:30:30Z
Source Updated At2026-06-01 16:33:03Z
Raw Payload Uris3://job-postings-prod-raw-590183727216/raw/provider=icims/board=careers-jobyaviation.icims.com/date=2026-06-06/2026-06-06T08-30-29-544Z-2d3fa2815ddf817b0f35c2f7f676a2a595ebe46ce1cfe65573e9518e18310285.json
Event Fields
{
  "content_hash": "abc4bc0e5b46a85b05b86640db28d8181515ae07992e3c7f91fd7193ef8ed124",
  "source_hash": "2318811ccffa630fb2b998e14cf28932da5d43217e47fbf971f43ff58a6d4444",
  "last_changed_at": "2026-06-06T08:30:36.223Z",
  "active_status": "active"
}
Parsed Structured
{
  "language": "en",
  "location": {
    "raw": "Santa Cruz, CA, US",
    "city": "Santa Cruz",
    "region": "CA",
    "country": "United States",
    "is_remote": false,
    "confidence": 0.8
  },
  "salary_max": 245000,
  "salary_min": 224000,
  "inferred_at": "2026-06-06T08:30:36.101Z",
  "launch_scope": {
    "reason": "english_us_canada",
    "included": true,
    "language": "en",
    "location": {
      "raw": "Santa Cruz, CA, US",
      "city": "Santa Cruz",
      "region": "CA",
      "country": "United States",
      "is_remote": false,
      "confidence": 0.8
    },
    "countries": [
      "United States"
    ]
  },
  "remote_policy": null,
  "salary_period": "year",
  "workplace_type": null,
  "salary_currency": "USD"
}
Extensions
{}
Native Structured
{
  "json_ld": {
    "url": "https://careers-jobyaviation.icims.com/jobs/5094/staff-flight-research-machine-learning-engineer/job",
    "@type": "JobPosting",
    "title": "Staff Flight Research Machine Learning Engineer",
    "@context": "http://schema.org",
    "datePosted": "2024-06-06T08:30:30.993Z",
    "description": "<h2>Company Overview</h2>\n<p>Joby Flight Research designs, develops, and flight-tests novel aircraft using a software-first autonomy approach. We build and deploy autonomy, perception, planning, and radar systems across conventional, electric, and hydrogen-electric aircraft in both CTOL and VTOL configurations.</p>\n<h2>Overview</h2>\n<p>Joby Flight Research is seeking a Staff Machine Learning Engineer to design and build state-of-the-art perception and reasoning algorithms into the Superpilot™ autonomy stack, enabling autonomous aircraft and associated ground systems to safely and autonomously navigate their complex environment, and define the standard of autonomous flight.</p>\n<p>In this role, you will be responsible for training models that enhance current Superpilot™ algorithms while investigating the application of cutting-edge research to expand autonomous flight capabilities. Your responsibilities will include filtering sensor data from flights, architecting dependable training infrastructure for datasets and models, and performing ongoing evaluations within our Operational Design Domain (ODD). By focusing on these tasks, you will play a vital part in achieving the detection and localization standards necessary for ensuring the safety of autonomous aviation.</p>\n<p>We are a small, high-impact team that values curiosity, technical initiative, and the ability to operate independently. You will collaborate deeply with controls, and flight software engineers to build a foundation that accelerates our path to safe, autonomous flight. The right candidate is a strong ML engineer who cares deeply about system integration, experiment reproducibility and traceability, and is able to contribute to different parts of the data pipeline where needed.</p>\n<h2>Responsibilities</h2>\n<ul>\n <li>Own the development of a Superpilot™ application (e.g. ground hazard avoidance, vision-based landing, detect and avoid)</li>\n <li>Lead product requirements and design architecture with multi-disciplinary teams—including controls, systems, and flight testing—to seamlessly embed and validate algorithms within our operational design domain</li>\n <li>Architect and deploy sophisticated algorithms for aircraft environment detection and tracking. By integrating deep learning with geometric computer vision, you will utilize multi-sensor inputs—including lidar, radar, and varied camera systems—to establish comprehensive 3D situational awareness</li>\n <li>Construct high-performance model training pipelines and extensive evaluation systems. These frameworks must ensure reliability by identifying performance nuances in complex edge cases and rare operational scenarios</li>\n <li>Drive continuous improvement of perception stacks through simulation. You will focus on optimizing latency and robustness to ensure peak performance during demanding flight conditions</li>\n <li>Engineer specialized diagnostic and visualization tools to extract meaningful insights from field data. This involves facilitating swift root-cause analysis and resolving perception challenges in active deployments</li>\n <li>Shape the future of the Superpilot™ autonomy system. Your contributions will define a perception architecture that sets new benchmarks for safety and reliability in the autonomous aviation industry</li>\n</ul>\n<h2>Required</h2>\n<ul>\n <li><p>At least 8 years of experience developing and implementing advanced perception frameworks for autonomous platforms, including aircraft, vehicles, or robotics</p></li>\n <li><p>Extensive practical knowledge of cutting-edge models for tracking and detecting objects within real-world applications</p></li>\n <li><p>Comprehensive understanding of geometric vision methods such as visual odometry, structure-from-motion, and stereo vision to facilitate accurate 3D estimation</p></li>\n <li><p>Competency in C++, Python, and PyTorch, along with various deep learning inference and training ecosystems</p></li>\n <li><p>Ability to rapidly prototype code during experimentation, and support deployment by shipping scalable and high quality code</p></li>\n <li><p>A creative approach to engineering that includes a history of mitigating performance delays and addressing complex operational edge cases</p></li>\n <li><p>Direct experience maintaining commercial autonomous systems by diagnosing technical hurdles and improving live system dependability</p></li>\n <li><p>Strong interpersonal skills necessary to succeed within a fast-paced, multidisciplinary team focused on safety-critical engineering</p></li>\n</ul>\n<h2>Desired</h2>\n<ul>\n <li>Expertise in developing supporting data curation and model experimentation pipelines to support ML experimentation</li>\n <li>Experience with ROS 2 or other robotics middlewares</li>\n <li>Experience with recent generative AI world simulation tooling (e.g. NVIDIA Isaac Sim, Omniverse)</li>\n <li>Experience deploying models onto GPUs and other accelerators for production-level embedded systems</li>\n <li>Experience with autonomous vehicles</li>\n <li>Experience in processing aircraft data (GPS, inertial, air data, radio data, etc.)</li>\n <li>Expert-level software engineering: deep expertise in architecting and writing clean, scalable, and maintainable code</li>\n <li>Experience with version control and CI/CD platforms, able to manage your software through its entire lifecycle (development, testing, deployment)</li>\n <li>Experience deploying ML models in a production environment using modern MLOps principles and tools (e.g., MLflow, Kubeflow)</li>\n <li>Experience mentoring small teams of perception engineers up to 5 people</li>\n</ul>\n<p><strong>Compensation at Joby is a combination of base pay and Restricted Stock Units (RSUs). The target base pay for this position is $224,000 - $245,000/yr. The compensation package will be determined by job-related knowledge, skills, and experience.</strong></p>\n<p> </p>\n<p><strong>Joby also offers a comprehensive benefits package, including paid time off, healthcare benefits, a 401(k) plan with a company match, an employee stock purchase plan (ESPP), short-term and long-term disability coverage, life insurance, and more.</strong></p>\n<h2>Additional Information</h2>\n<p>Joby Aviation is an equal opportunity employer. </p>",
    "directApply": true,
    "jobLocation": [
      {
        "@type": "Place",
        "address": {
          "@type": "PostalAddress",
          "postalCode": "95060",
          "addressRegion": "CA",
          "streetAddress": "333 Encinal St",
          "addressCountry": "US",
          "addressLocality": "Santa Cruz",
          "postOfficeBoxNumber": "UNAVAILABLE"
        }
      }
    ],
    "validThrough": "2027-06-06T08:30:30.993Z",
    "employmentType": "FULL_TIME",
    "hiringOrganization": {
      "name": "Joby Aviation",
      "@type": "Organization",
      "sameAs": "www.jobyaviation.com"
    },
    "occupationalCategory": "Flight Research"
  },
  "detail_meta": {
    "url": "https://careers-jobyaviation.icims.com/jobs/5094/staff-flight-research-machine-learning-engineer/job?in_iframe=1",
    "http_status": 200,
    "content_type": "text/html;charset=UTF-8",
    "response_bytes": 44096,
    "compact_response_bytes": 7343,
    "original_response_bytes": 44096
  },
  "sitemap_job": {
    "id": "5094",
    "url": "https://careers-jobyaviation.icims.com/jobs/5094/staff-flight-research-machine-learning-engineer/job",
    "slug": "staff-flight-research-machine-learning-engineer",
    "lastmod": "2026-06-01T12:33:03-04:00"
  },
  "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/f2c6412bad4b698627ced1eda89dce21a09e4b88?include=descriptionJSON
GET https://api.bluedoor.sh/job-postings/v1/orgs/facea025-a75a-4397-bb0b-e37529b02821JSON
GET https://api.bluedoor.sh/job-postings/v1/sources/8e5cd43b-3573-4a2e-a00f-07f733c827a1JSON
GET https://api.bluedoor.sh/job-postings/v1/jobs/f2c6412bad4b698627ced1eda89dce21a09e4b88/eventsJSON