Torc Robotics

Senior, ML Engineer- Offline Perception

Remote Machine Learning Engineer role with clear candidate location fit.

PostedJul 4, 2026

Eligible countries2 accepted countries

Seniority signalSenior

Work settingRemote

Accepted candidate locations

CanadaUSA

AWS Docker Python REST

Role overview

Senior, ML Engineer- Offline Perception

Requirements and responsibilities

Readable role content extracted into sections for faster review.

What You’ll Do:

Design, implement, test and deploy offline object detection, tracking and fusion modules to automatically create annotations on Cloud Services from logged sensor data (Cameras, Lidars, Radars)
Demonstrated project management skills, serving as project lead guiding less experienced team members in multiple facets of project execution.
Stay up to date with the latest developments in AI and ML for autonomous driving.
Independently develop offline perception 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.
Proactively assess current capabilities to identify areas for improvement proposing solutions that align with core strategy and operation.
Measure and track auto labeling quality to meet internal customer requirements.
Guide and produce information products, supporting visualization and data accessibility in a customer-centric manner.
Evaluate and make recommendations regarding technical advances that improve productivity and quality, reduce flow times, and enhance operational surety.
Develop guidelines and standards for analytics and machine learning models, their deployment, and associated processes.
Provides technical guidance or business process expertise, technical leadership, coaching and mentoring to team members.

What You’ll Need to Succeed:

Considered highly skilled and proficient in discipline; conducts complex, important work under minimal supervision and with wide latitude for independent judgment.
Scope of Influence: Expected to drive alignment across team interfaces to the rest of the organization. Designs, maintains and owns team technical solutions and drives consensus. Mentors and guides engineers within the group.
Bachelor’s Degree in Computer Science, Robotics, Electrical Engineering or related technical field plus demonstrates competences 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 demonstrates competences and technical proficiencies typically acquired through 3+ years of experience OR;
Doctorate Degree in Computer Science, Robotics, Electrical Engineering or related technical field plus demonstrates competences and technical proficiencies typically acquired through 1+ years of experience.
Required Qualifications (some combination of the following skills): Active Learning & Pseudo-labeling - Computer Vision, Deep Learning, Model training.Two of the following: 2D/3D Object Detection, Tracking, Sensor Fusion, Semantic Segmentation, SLAM, BEV.Scaled ML Operations (MLOps) and Tooling – ML Frameworks, experiment tracking, model registry, MLFLow, Weights and Biases, ML Metrics and Evaluation / Quality. Distributed machine learning frameworks - PyTorch, Lightning, Ray. Model Data Curation - Parquet data processing (PyArrow, Daft, Pandas, etc). Development Tools & Eco-System (at scale) - Proficiency in Python software development. Also, VDI and cloud-based development environments, CI Systems (GitHub Actions), and Docker.
Active Learning & Pseudo-labeling - Computer Vision, Deep Learning, Model training.
Two of the following: 2D/3D Object Detection, Tracking, Sensor Fusion, Semantic Segmentation, SLAM, BEV.
Scaled ML Operations (MLOps) and Tooling – ML Frameworks, experiment tracking, model registry, MLFLow, Weights and Biases, ML Metrics and Evaluation / Quality.
Distributed machine learning frameworks - PyTorch, Lightning, Ray.
Model Data Curation - Parquet data processing (PyArrow, Daft, Pandas, etc).
Development Tools & Eco-System (at scale) - Proficiency in Python software development. Also, VDI and cloud-based development environments, CI Systems (GitHub Actions), and Docker.

Details

Active Learning & Pseudo-labeling - Computer Vision, Deep Learning, Model training.
Two of the following: 2D/3D Object Detection, Tracking, Sensor Fusion, Semantic Segmentation, SLAM, BEV.
Scaled ML Operations (MLOps) and Tooling – ML Frameworks, experiment tracking, model registry, MLFLow, Weights and Biases, ML Metrics and Evaluation / Quality.
Distributed machine learning frameworks - PyTorch, Lightning, Ray.
Model Data Curation - Parquet data processing (PyArrow, Daft, Pandas, etc).
Development Tools & Eco-System (at scale) - Proficiency in Python software development. Also, VDI and cloud-based development environments, CI Systems (GitHub Actions), and Docker.

Bonus Points!

Data operations and management at scale - Schema design, AWS storage and processing infra, vector databases / LanceDB, file formats (MCAP, parquet, etc).
Data Visualization - Integration with tooling such as OpenGL, 3.js, foxglove, 51, tableau.
Cloud Development – Python (proficient level), Terraform, AWS Managed Services (eg S3, ECS, Lambda, Dynamo, Step Functions, Athena).
Cloud-based orchestration and resource management - AWS Hyperpods, Anyscale, Etc. Model Inference Orchestration.

Perks of Being a Full-time Torc’r (Canada)

A competitive compensation package that includes a bonus component and stock options
Medical, dental, and vision for full-time employees
RRSP plan with a 4% employer match
Public Transit Subsidy (Montreal area only)
Flexibility in schedule and generous paid vacation
Company-wide holiday office closures
Life Insurance

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