Location: Ottawa, Ontario (Hybrid / Remote-First with periodic work at the Ottawa facility)
Position Type: Full-Time
Compensation: Salary & Stock Options
Benefits: Comprehensive Health & Dental package, starting 3 weeks paid annual leave
Contact: If you have issues with your application, please email [email protected]

About Obruta

Founded in 2019, Obruta Space Solutions is enabling the in-space economy by developing the advanced autonomy systems required for in-orbit satellite servicing, logistics, and active debris removal. Today’s satellites are single-use assets that cannot be refueled, inspected, or repaired. Obruta is eliminating this barrier with our flagship product: the RPOD Kit —a turnkey, vision-guided Guidance, Navigation, and Control (GNC) subsystem that makes autonomous spacecraft docking commonplace.

Backed by leading venture capital and top-tier space innovation programs, Obruta is executing cutting-edge perception and navigation projects with the Canadian Space Agency (CSA), National Research Council (NRC), and international commercial partners.

The Role

Obruta is seeking a motivated Junior Computer Vision & ML Infrastructure Engineer (current graduates welcome) to take our computer vision training, evaluation, and benchmarking pipelines to the next level of engineering rigor.

In this role, you will be the engine behind how we train, validate, and verify our 3D vision and 6-Degrees of Freedom (DOF) pose estimation models. Instead of manually running training scripts, you will design and implement robust MLOps automation for large-scale training runs across cloud and local GPU compute.



You will build comprehensive automated benchmarking suites to evaluate model accuracy, latency, and failure modes across both synthetic Unreal Engine/Polar datasets and real-world test data from our 6-DOF dual-arm robotics laboratory.

You will also help benchmark and validate algorithms operating under challenging space conditions—including extreme high-contrast lighting, noisy sensor feeds, and novel neuromorphic (event-based) data streams—establishing reproducible performance baselines that give our flight software and GNC teams complete confidence.

Key Responsibilities

- Automated Training Pipelines (MLOps): Build, automate, and maintain scalable training, data preprocessing, and model checkpointing workflows in Python and PyTorch across local workstations and cloud GPU instances.
- Rigorous Benchmarking Suites: Develop automated evaluation harnesses to benchmark 3D vision and 6-DOF pose estimation models against standard metrics (e.g., ADD-S, translation/rotation errors, edge-case failure rates, inference latency).
- Sim-to-Real & Robustness Testing: Design regression testing frameworks that continuously evaluate algorithm performance and domain generalization across synthetic datasets (Unreal Engine/Polar) and empirical optical/neuromorphic imagery collected in our dual-arm robotics lab.
- Dataset Management & Curating:



Automate data ingestion, augmentation, format conversion, and quality-screening pipelines for synthetic data streams and benchmark datasets.
- Perception Algorithm Development: Support the design and fine-tuning of 3D computer vision models, classical feature-matching pipelines, and novel event-camera algorithms under the mentorship of senior technical leads.
- Model Export & Deployment Awareness: Validate standard model exports (e.g., ONNX, TensorRT) against ground-truth baselines to ensure precision is maintained, keeping downstream embedded edge targets and compute budgets in mind during model design.
- Verification Reporting: Generate automated, repeatable test reports and performance visualizations to support technical deliverables and commercial verification packages.

Required Qualifications

- Bachelor's or Master's degree in Computer Science, Computer Engineering, Electrical Engineering, Robotics, or a related discipline (recent graduates encouraged to apply).
- Strong programming proficiency in Python and familiarity with deep learning frameworks (preferably PyTorch ).
- Hands‑on academic, personal, or internship project experience building, training, and evaluating computer vision or machine learning models.
- Solid grasp of software engineering fundamentals: modular code design, Git version control, testing best practices, and Linux environments.
- Foundational understanding of linear algebra, 3D geometry, coordinate transformations, and camera fundamentals.
- Applicants must be legally authorized to work in Canada. Successful candidates must able

📌 Junior Computer Vision (Canada)
🏢 Obruta Space Solutions
📍 Canada