Machine Learning Integration & Deployment Engineer

Key Skills

Aartificial intelligence, Analytical and Problem solving, C Programming, C++, Deep Learning, Effective communication skills, Machine learning techniques, MATLAB Programming, Python Programming, PyTorch, TensorFlow

Job Description

Join the leader in entertainment innovation and help us design the future. At Dolby, science meets art, and high tech means more than computer code. As a member of the Dolby team, you’ll see and hear the results of your work everywhere, from movie theaters to smartphones. We continue to revolutionize how people create, deliver, and enjoy entertainment worldwide. To do that, we need the absolute best talent. We’re big enough to give you all the resources you need, and small enough so you can make a real difference and earn recognition for your work. We offer a collegial culture, challenging projects, and excellent compensation and benefits.

Play a key role as part of Dolby's new R+D Center in Bangalore as an ML Optimization Engineer in our Advanced Technology Group ATG. ATG is the research and technology arm of Dolby Labs. It has multiple competencies that innovate technologies in audio, video, AR/VR, gaming, music, and movies. Many areas of expertise related to computer science and electrical engineering, such as AI/ML, computer vision, image processing, algorithms, digital signal processing, audio engineering, data science & analytics, distributed systems, cloud, edge & mobile computing, natural language processing, knowledge engineering and management, social network analysis, computer graphics, image & signal compression, computer networking, IoT are highly relevant to our research.

What You’ll Do

A Machine Learning Integration and Deployment Engineer is responsible for designing and implementing optimization strategies for the refinement and deployment of deep learning models. This may include researching and implementing model compression techniques such as quantization, pruning and distillation. The engineer may also work on developing custom deployment techniques for specific inference engines and optimizing model performance in-situ in a deployed product environment. Additionally, the engineer may work with other team members, such as data scientists and software engineers, to integrate the optimized models into production systems and to refine and scale the methods used for assessing the functional performance of models.

What You Need To Succeed

Competencies

• Technical depth: Necessary technical knowledge in web technologies for real time just in time processing. Basic knowledge on Audio/Video formats.
• Explore new technologies: Research and stay up to date with the latest machine learning technologies, frameworks and inference engines.
• Invent & Innovate: Develop short and long-term technologies, algorithms and software tools that will help make Dolby a world leader in enhancing the sight and sound associated with digital content consumption. Then influence and collaborate with BG partners to put the technology into production.
• Work with a sense of urgency: Respond aggressively to changing trends and new technologies and create new approaches to capitalize on them. Take appropriate risks to be ahead of the competition and the market.
• Collaborate: Collaborate with and influence peers in developing industry-leading technologies. Work with external trendsetters and technology drivers in academia and in partner enterprises.

Key Responsibilities:

• Optimize models for deployment in a wide range of target architectures including desktop, cloud, browsers and mobile devices
• Develop and implement algorithms and software for efficient real-time and offline inference
• Monitor and evaluate the performance of models in a production context and optimize them for accuracy, speed and compute resource efficiency
• Design and implement custom tooling and strategies for the development and deployment of optimized deep learning models
• Research and implement appropriate techniques to optimize deployment for product
• Work closely with cross-functional teams, including product managers, engineers and researchers, to understand their workflows and design and implement optimized model deployment techniques

Desired Background

• PhD. in engineering or other technical field (computer science, engineering, or equivalent) or
• Masters (or equivalent) in relevant degree plus 4 years experience
• Strong background in deep learning and optimization techniques. Good understanding of theoretical foundations of AI algorithms.
• Publication record in AI technologies
• 3 years of experience with frame works such as PyTorch/Onnx/TensorFlow/etc
• 2 years of experience optimization ML models for Inference using hardware acceleration
• 3 years of experience with programming languages such as Python, C/C++, or Matlab
• Experience with Embedded systems, Computer Architecture, high-performance computing
• Experience with optimizing ML model inference time performance in constrained deployment environments is a plus
• Strong analytical and problem-solving skills
• Strong communication skills and ability to work well in a team environment