NBCUniversal
Staff Deep Learning Engineer
Vaga remota de Engineering com fit claro de localização do candidato.
Publicada2 de jul. de 2026
Países elegíveis1 país aceito
Sinal de senioridadeSenior
Modelo de trabalhoRemoto
Locais aceitos para candidatos
Estados Unidos
Resumo da vaga
Staff Deep Learning Engineer
Requisitos e responsabilidades
Conteúdo da vaga extraído em seções para revisão mais rápida.
Key Responsibilities
- Algorithm Implementation: Implement core deep-learning, computer vision, and (inverse-)procedural modeling algorithms in Python. You will rely on mathematical techniques from linear algebra, probability, and geometry to build these systems.
- Applied Research: Apply cutting-edge research in machine learning and computer graphics to solve real-world problems.
- Cross-Functional Coordination: Work closely with our cofounders to understand high-level product vision and translate customer requirements into technical milestones.
- Scaling & Deployment: Interact with remote machines via a Unix shell to deploy and test code on large-scale geospatial datasets, ultimately generating 3D content for our customers.
- Code Management: Use Git to manage source code and modularize complex implementation tasks into manageable, executable components.
Key Responsibilities
- Master's degree in Computer Science, Engineering, Mathematics, or a related field
- Minimum of 5+ years of relevant industry experience, ideally within a fast-paced, high-growth tech environment.
- Professional Experience: Proven experience as a DL Engineer or Applied Research Engineer in a fast-paced environment.
- Industry Context: Prior experience in industries with complex multi-disciplinary teams such as robotics, smart grids, precision agriculture, game development, or aerospace is highly valued.
- Technical Proficiency:
- Core Stack: Fluency with Python, Git, and the Unix shell.
- ML Experience: Proven experience training and debugging artificial neural networks or adjacent experience (e.g., gradient descent, nonlinear optimization, or classical machine learning).
- Math Foundations: A strong mathematical background covering linear algebra, statistics, probability, and numerical methods.
- Preferred prior experience with modern C++ to interface with data ingestion and product pipelines.
- Attributes:
- Communication: Effective collaboration and the ability to work closely with a founding team.
- Execution: High attention to detail and the ability to meet key R&D milestones in an early-stage startup environment.
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