5 дней назад
Staff Software Engineer (ML Infrastructure)
220 000 - 260 000$
Вакансия из Hirify Global, списка международных tech-компанийДля мэтча и отклика нужен Plus
Мэтч & Сопровод
Для мэтча с этой вакансией нужен Plus
Описание вакансии
Текст:
TL;DR
Staff Software Engineer (ML Infrastructure): Building training, experiment tracking, model export, and deployment infrastructure for computer vision models running across thousands of cameras, with an accent on PyTorch, AWS, and production-scale ML systems. Focus on designing train-to-deploy workflows, optimizing TensorRT and ONNX inference, establishing DevOps-for-ML practices, and preparing infrastructure for on-device inference.
Location: San Francisco, CA; hybrid work
Compensation: $220K–$260K base salary, plus annual bonus and equity
Company
develops computer vision and machine learning technology that uses existing security cameras to detect hazards and high-risk activities, improve workplace safety, and drive operational efficiency across industrial and commercial environments.
What you will do
- Set the technical direction for ML infrastructure, including architecture, framework selection, and build-versus-buy decisions.
- Architect and build scalable training infrastructure for concurrent computer vision experiments using PyTorch and AWS.
- Own the train-to-deploy handoff by exporting models to optimized inference formats such as TensorRT and ONNX and measuring accuracy and latency tradeoffs.
- Select and roll out experiment tracking and model lifecycle tooling such as Weights & Biases, MLflow, or ClearML.
- Establish DevOps-for-ML practices covering infrastructure as code, CI/CD, observability, and cost monitoring.
- Mentor Vision & AI engineers and architect infrastructure for future on-device inference.
Requirements
- 7+ years of experience building and shipping large-scale software systems, including at least 3 years focused on ML or large-scale data infrastructure.
- Demonstrated ownership of architecture, tool selection, framework choices, and build-versus-buy decisions.
- Deep experience with PyTorch, modern ML training stacks, reliable training pipelines, and experiment tracking.
- Strong Python skills and production experience with AWS ML workloads, including services such as S3, EC2, or EKS.
- Hands-on experience with model export and inference optimization using TensorRT, ONNX, or similar technologies.
- Experience with ML orchestration tools, GPU profiling and optimization, and computer vision model training.
Culture & Benefits
- Equity through ’s Equity Incentive Plan.
- Comprehensive health, dental, and vision insurance.
- Competitive paid parental leave.
- Unlimited PTO and flexible work arrangements.
- Daily meals in the office, team events, and an annual company onsite.
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