Torc Robotics

Torc Robotics

Senior, ML Engineer - 3D Reconstruction

Remote - US, Ann Arbor, MI · Senior · Full-time

Sponsorship not specified$199k-$299kDetected 19 days ago
PythonAlgorithmsGitGitHub ActionsMachine LearningDeep LearningPyTorchPandasComputer VisionMLOpsAccessibilityRoboticsElectrical EngineeringSensorsGISLeadershipMentoring

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odds of building a lasting career here

64Sponsors, lottery-bound
Cap-exempt (no lottery)0
Sponsors this role100
Entry-level history0
PERM / green-card track0
Lottery odds (Level IV)94
Fits your clock70

Sponsors, but it's cap-subject — you still face the weighted lottery (~61% per draw at Level IV). Good if you win; have a cap-exempt backup on your list.

Lottery odds assume a STEM candidate.

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H-1B wage level

the lottery is wage-weighted — each level is one more entry

Level IV · 4×
Level I$87,9011 entry
Level II$106,8912 entries
Level III$125,9023 entries
Level IV$144,8934 entries

This range already reaches Level IV — the maximum four lottery entries.

DOL prevailing wage, 2026-27 wage year · Software Developers (15-1252) · Ann Arbor, MI. Wage level is derived by USCIS from the offered wage, occupation and worksite; the occupation shown is inferred from the job title.

Employer immigration record

from this employer's Department of Labor filings

Green-card filing pattern in this occupation

Context, not a finding about this posting: of this employer's 5 green-card filings in this occupation, 100% were for a worker who already held the job.

Files H-1B transfers

11 transfer filings in the last year, covering 11 workers. Median labor-condition decision: 7 days. An employer that already files transfers is one that can take over an existing H-1B.

Sourced from Department of Labor LCA, PERM and prevailing-wage disclosure data. Employer matching is by name, so figures may be split across an employer's legal entities. Absence of a filing means none appears in our copy of the data, not that none exists.

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About the role

  • The annotations include 2D, 3D bounding boxes, classes, trajectories, lane lines, segmentations, depths, and high-definition map elements.
  • Even if you don't meet 100% of the qualifications listed for this opportunity, we encourage you to apply.

Responsibilities

  • Own the design, implementation, and deployment of offline 3D reconstruction, lane line detection, and automatic mapping/map creation systems that generate high-quality annotations on Cloud Services from logged sensor data (Cameras, Lidars, Radars, GPS/IMU).
  • Lead lane line network reconstruction efforts, rebuilding topologically correct lane line graphs from noisy, partial, or conflicting detections across drives.
  • Design, train, and iterate on lane line detection and creation models, pushing state-of-the-art performance and driving model architecture and training strategy decisions for the team.
  • Develop methods for creating lane line maps in previously unmapped areas, and for detecting and propagating map updates as road geometry changes over time.
  • Own pose correction for sensor data, and lead the diagnosis and mitigation of calibration and sensor errors (extrinsic/intrinsic miscalibration, drift, noise) that degrade reconstruction and mapping quality.
  • Set technical direction and best practices for the team's lane line, mapping, and pose estimation pipelines, and lead architecture decisions with cross-team impact.
  • Serve as technical lead on high-complexity projects, guiding and mentoring other engineers across multiple facets of project execution.
  • Independently develop offline perception and mapping models or algorithms using disciplined software development processes, making recommendations for developing new code or re-using existing code, implementing version control, and maintaining documentation of created applications.
  • Define and implement ingestion, data preparation, curation, and governance of large, multi-faceted data sets supporting analytics models and workflows.
  • Scope of Influence: Expected to drive alignment across team and organizational boundaries. Sets technical direction, owns team technical solutions end-to-end, and is looked to as the authority for lane line, mapping, and pose estimation problems.

Requirements

  • Strong, demonstrated background in lane line detection and creation, including hands-on experience with lane line network reconstruction and training lane line creation models.
  • Proven experience creating lane line maps for previously unmapped areas, and handling ongoing map updates as road conditions and geometry change.
  • Strong background in pose correction, with direct experience diagnosing and correcting calibration and sensor errors across camera, lidar, radar, and GPS/IMU systems.
  • Development Tools & Eco-System (at scale) - Advanced proficiency in Python software development.
  • GIS (Geographic Information Systems) experience.

Nice to have

  • Bachelor's Degree in Computer Science, Robotics, Electrical Engineering or related technical field plus demonstrated competencies and technical proficiencies typically acquired through 6+ years of experience OR
  • Master's Degree in Computer Science, Robotics, Electrical Engineering or related technical field plus demonstrated competencies and technical proficiencies typically acquired through 3+ years of experience OR
  • PhD in Computer Science, Robotics, Electrical Engineering or related technical field plus demonstrated competencies and technical proficiencies typically acquired through 1+ years of experience.

Skills

  • Also, VDI and cloud-based development environments, CI Systems (GitHub Actions), and Docker.

Compensation

  • A competitive compensation package that includes a bonus component and stock options

Benefits

  • Develop guidelines and standards for analytics and machine learning models, their deployment, and associated processes.
  • Active Learning & Pseudo-labeling - Computer Vision, Deep Learning, Model training at an advanced level.
  • Distributed machine learning frameworks - PyTorch, Lightning, Ray.

Company info

  • At Torc, we have always believed that autonomous vehicle technology will transform how we travel, move freight, and do business.
  • A leader in autonomous driving since 2007, Torc has spent over a decade commercializing our solutions with experienced partners.
  • Now a part of the Daimler family, we are focused solely on developing software for automated trucks to transform how the world moves freight.
  • Join us and catapult your career with the company that helped pioneer autonomous technology, and the first AV software company with the vision to partner directly with a truck manufacturer.
  • Meet The Team:
  • The Pseudo-Labeling team's goal is to create high-quality annotations on sensor data (images, point clouds).
  • The annotations are then used by different downstream users - for example, perception teams use them to train various models, mapping teams use them to build and maintain HD maps, and simulation teams use them for generating new data.
  • This role owns the hardest problems in the team's lane line and mapping stack and is expected to set technical direction across lane line reconstruction, map creation, and pose accuracy.
  • About the Company
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This listing is sourced directly from Torc Robotics's careers page and normalized into a canonical job model.