Senior Machine Learning Engineer: Computer Vision & 3D

FitMatch is seeking a Senior Machine Learning Engineer to advance the computer vision and 3D reconstruction behind QuadraScan. The role owns end-to-end model development for human reconstruction, including data preparation, experimentation, evaluation, production integration, and collaboration with mobile and backend engineers to deliver accurate and efficient systems.

Responsibilities:

• Own model development for human reconstruction and related computer vision work, from data preparation and training through evaluation and production integration
• Select, adapt, and fine-tune pretrained models, modifying architectures or training objectives when a clear product need justifies it
• Design focused experiments with clear baselines, success criteria, and sensible time and compute limits
• Improve training data and capture coverage across synthetic and real imagery, maintaining reliable labels, dataset versioning, and train/eval subject separation
• Diagnose failures across the full pipeline, from capture quality and camera geometry to model predictions and reconstructed surfaces
• Evaluate geometric fidelity, regional errors, robustness, and failure rates, tying model metrics to scan and measurement quality
• Build maintainable training and inference code with reproducible experiments and practical regression checks
• Partner with mobile and backend engineers to deploy improvements, balancing accuracy, latency, memory, and cost
• Communicate findings clearly, including what the evidence supports, what remains uncertain, and the best next step

Requirements:

• 5+ years of professional experience in ML, computer vision, or related engineering, with substantial hands-on model development
• Proven track record taking a computer vision model from experimentation into production and improving it based on real-world performance and failure cases
• Strong Python and PyTorch skills, including building training pipelines, modifying model components and losses, and diagnosing training issues
• Practical experience with geometric computer vision (e.g., depth estimation, camera calibration, multi-view geometry, 3D perception, or reconstruction)
• Solid grasp of model evaluation, including overfitting, data leakage, distribution shift, and the limits of aggregate metrics
• Strong software engineering fundamentals: maintainable code, version control, testing, debugging, and reproducible environments
• Ability to turn ambiguous objectives into actionable technical plans and execute independently
• Clear communication and sound judgment in balancing model quality, speed, and resources
• Experience with human reconstruction, body shape, pose estimation, photogrammetry, or measurement-sensitive vision applications, including work with point clouds, meshes, or differentiable rendering
• Experience using synthetic data to improve transfer to real camera imagery, and adapting pretrained vision transformers or other large vision models to specialized tasks
• Familiarity with deploying models on Apple platforms (Core ML, MLX, Metal) or other constrained devices
• Experience delivering applied ML in a small company or a team with limited specialist support