Machine Learning Engineer - Computer Vision

Geolava; Canada; Remote; CAD 130,000 - 180,000; Full time

Job summary

Geolava is seeking a Machine Learning Engineer specializing in Computer Vision to lead the development of their geospatial intelligence platform. The ideal candidate will oversee the entire ML lifecycle and have a rich background in remote sensing and deep learning. This position offers a competitive salary, excellent benefits, and the opportunity to pioneer innovations in the geospatial industry.

Benefits

Medical, dental, and vision insurance

401k retirement plan

Short & long term disability and life insurance

Competitive salary

Remote work flexibility

Qualifications

• 5–8 years as an ML Engineer in sensor-driven environments.
• Experience with LiDAR, radar, and similar technologies.
• Familiarity with deep learning frameworks, ideally PyTorch.

Responsibilities

• Own the end-to-end ML lifecycle; deploy models for Geolava’s platform.
• Develop remote sensing models including 3D LiDAR detection.
• Collaborate with cross-functional teams to define ML-powered features.

Skills

Deep learning frameworks

LiDAR and remote sensing expertise

Geospatial libraries

MLOps management

Job description

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This range is provided by Geolava. Your actual pay will be based on your skills and experience — talk with your recruiter to learn more.

Base pay range

CA$130,000.00/yr - CA$180,000.00/yr

About Geolava

Geolava turns the built world into live, actionable intelligence. Our spatial-reasoning platform reveals the current and future state of every property or critical asset, empowering owners, investors, and operators to make informed decisions.

We are backed by top-tier VCs and have been revenue-generating from day one, and we recently closed an oversubscribed round. This is your opportunity to join as a founding member and help define the future of spatial intelligence from the ground up.

Responsibilities (What You’ll Do)

• Own the end-to-end ML lifecycle: from ingesting messy, multi-source geospatial data to deploying production-grade models that power Geolava’s property intelligence platform.
• Develop and deploy remote sensing models: including semantic segmentation, 3D LiDAR detection, and vision-language fusion; extract geospatial and structural features across satellite, HAPS, drone, and street-level imagery using CNNs and transformers.
• Fuse multimodal data sources: such as imagery, LiDAR, parcel vectors, and zoning documents; generate unified embeddings using VLMs and cross-modal architectures to drive downstream APIs and analytics.
• Leverage self-supervised learning: train models on vast unlabeled EO archives; minimize manual labeling and scale across diverse geographies.
• Build ensemble and temporal modeling systems: support land use classification, change detection, and anomaly identification across time and location.
• Implement robust ML observability: integrate drift detection, performance alerting, and automated A/B testing; catch regressions before they impact users.
• Generate synthetic datasets: use GANs and diffusion models to augment scarce or rare-event imagery; enhance model generalization and scenario coverage.
• Optimize models for edge inference: deploy on constrained hardware (e.g., onboard drones or satellites); apply pruning, quantization, and platform-specific acceleration.
• Collaborate cross-functionally: work with product, engineering, and domain experts to turn ambiguous requirements into reliable, ML-powered features.
• Lead active learning and labeling operations: define annotation pipelines; guide auto-labeling tools; close the loop on human-in-the-loop feedback.
  
  

Skills Requirements (Who You Are)

• Experienced ML Engineer (5–8 years) who has deployed machine learning models in real-world, sensor-driven environments, particularly focused on understanding physical spaces.
• Demonstrated expertise in at least one of the following areas:
• Perception Science: Hands-on experience building and deploying models using LiDAR, radar, stereo cameras, or similar sensors, typical in autonomous driving or robotics applications.
• Remote Sensing: Expertise in satellite imagery analysis (optical, SAR, multispectral), aerial or drone-based imaging, land-use classification, or environmental mapping.
• Proficient in deep learning frameworks (ideally PyTorch) and comfortable working with geospatial libraries (e.g., GDAL, rasterio, PDAL, OpenCV).
• Skilled in managing machine learning model lifecycles (MLOps), including version control, containerization, CI/CD practices, and pipeline orchestration.
  
  

Preferred Qualifications

• Experience integrating multimodal sensor data (e.g., combining LiDAR point clouds with RGB imagery or fusing satellite optical and SAR data).
• Experience developing retrieval-augmented generation (RAG) systems or NLP pipelines for extracting structured information from text documents.
• Knowledge of cloud and edge computing environments (AWS services, Jetson or similar edge accelerators).
• Exposure to advanced remote sensing methods, including hyperspectral or thermal imagery.
• Understanding of security and compliance frameworks (SOC 2, ISO-27001).
  
  

Why Join Geolava?

• Join a rocketship! We are pioneers of a new market that we are creating
• Take a central and critical role at Geolava
• Work with, and learn from, top-notch talent
• Competitive salary
• Excellent benefits including medical, dental, and vision insurance, a 401k retirement plan, short & long term disability and life insurance.
• Remote first
  
  

Seniority level

• Seniority level

  Not Applicable

Employment type

• Employment type

  Full-time

Job function

• Job function

  Engineering and Information Technology

• Industries

  Space Research and Technology

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