Senior Machine Learning Engineer
Confirmed on the employer's own hiring board on Oct 17, 2026. First seen by Alion on Aug 29, 2026.
Join our small, production-minded ML team as a Senior Machine Learning Engineer. You will collaborate with engineers and researchers to develop and deploy vision models for tasks like semantic/instance segmentation and object/damage detection across 2D and 3D data. Your responsibilities will include implementing training loops, curating datasets, driving high-priority experiments, and partnering with cross-functional teams. You should have proficiency in Python and relevant tech stack, deep ML/CV fundamentals, and experience with Python services, Docker, and AWS.
Missions
- Collaborate with engineers and researchers to develop, evaluate, and deploy vision models for tasks like semantic/instance segmentation and object/damage detection.
- Implement training loops, curate datasets, drive high-priority experiments, and partner with cross-functional teams to close feedback loops from edge cases.
- Contribute to datasets by helping define labeling guidelines, curating splits, running quality checks, and maintaining data versioning.
Profil recherché
- Strong communication skills with the ability to write tidy PRs, experiment logs, and short design notes to ensure reproducibility- AI-Empowered, Not AI-Dependent: We strongly encourage leveraging AI tools (Copilot, ChatGPT, Claude) to maximize your efficiency. However, you must 100% understand the underlying details of the code you ship. We are looking for strong independent thinkers and debuggers, not someone who simply passes along AI outputs without deep comprehension
- Proficiency in Python and the relevant tech stack: PyTorch, torchvision, Detectron2 or MMDetection/Segmentation, and Hugging Face Transformers
- Working knowledge of transformer and LLM building blocks applied to vision, including self-attention, positional encodings, tokenization, and mapping these ideas to vision models (e.g., ViT, DETR, Mask2Former)
- Practical exposure to 3D/depth data, including familiarity with point clouds, camera geometry (intrinsics/extrinsics), basic calibration, and multi-view geometry
- Deep ML / CV Fundamentals: You need hands-on experience training and evaluating deep models for segmentation and detection (PyTorch). You must understand how Transformer/LLM building blocks map to vision (ViT/DETR/Mask2Former) and have practical exposure to 2D/3D data, point clouds, and camera geometry
- Experience with Python services (FastAPI/Flask), Docker, and AWS services (S3, Batch/EC2, ECR) is preferred
- Curiosity & Strict Attention to Detail: You are obsessed with corner cases. You have a sharp eye for data anomalies, run rigorous ablations, keep meticulous experiment logs, and can clearly communicate trade-offs