Resumen del rol

Senior, ML Engineer- Offline Perception

Requisitos y responsabilidades

Contenido del rol extraído en secciones para revisar más rápido.

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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FocoMachine Learning EngineerÁrea del rol
Señal de senioritySeniorNivel del candidato
StackAWS, Docker, PythonSkills principales
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