We are seeking a Deep Learning Engineer with experience manipulating large 2D and 3D media datasets. In this role, you will implement core algorithms that sit at the intersection of computer vision and computer graphics, helping us turn high dimensional data into high-fidelity content. 

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. 

• 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. 

This position is eligible for company sponsored benefits, including medical, dental and vision insurance, 401(k), paid leave, tuition reimbursement, and a variety of other discounts and perks. Learn more about the benefits offered by NBCUniversal by visiting the Benefits page of the Careers website. Salary range: $220,000 – 260,000 (bonus eligibility). 

As part of our selection process, external candidates may be required to attend an in-person interview with an NBCUniversal employee at one of our locations prior to a hiring decision. NBCUniversal's policy is to provide equal employment opportunities to all applicants and employees without regard to race, color, religion, creed, gender, gender identity or expression, age, national origin or ancestry, citizenship, disability, sexual orientation, marital status, pregnancy, veteran status, membership in the uniformed services, genetic information, or any other basis protected by applicable law. 

If you are a qualified individual with a disability or a disabled veteran, you have the right to request a reasonable accommodation if you are unable or limited in your ability to use or access nbcunicareers.com as a result of your disability. You can request reasonable accommodations by emailing [email protected].

For LA County and City Residents Only:  NBCUniversal will consider for employment  qualified applicants with criminal histories, or arrest or conviction records, in a manner  consistent with relevant legal requirements, including the City of Los Angeles' Fair Chance Initiative For Hiring Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act, where applicable.