Senior System Software Engineer, Speech AI
NVIDIA is hiring a Senior System Software Engineer, Speech AI in Pune, India. Level rates it ; you can apply on NVIDIA.
Level's read on this job
- Level rates this software engineer role Builds AI ●●●●: the job is building AI systems.
- The posting mentions CUDA, Python and C among its tools and skills.
- Level classes it as a senior role.
- NVIDIA has 1,800+ other open roles on Level.
AI in this role
Design and optimize GPU-accelerated speech AI systems like ASR, TTS, and conversational models for production deployment.
Widely considered to be one of the technology world’s most desirable employers, NVIDIA is an industry leader with groundbreaking developments in High-Performance Computing, Artificial Intelligence and Visualization. The GPU, our invention, serves as the visual cortex of modern computers and is at the heart of our products and services. GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, autonomous cars and conversational AI that can perceive and understand the world. Today, we are increasingly known as “the AI computing company.” We're looking to grow our company, and build our teams with the smartest people in the world. Join us at the forefront of technological advancement.
NVIDIA is looking for a Senior System Software Engineer with deep expertise in speech technologies to support enterprise and developer customers. This role involves hands-on technical engagement with customers to implement, troubleshoot, and optimize Automatic Speech Recognition (ASR), Text-to-Speech (TTS), Audio Language Models (ALM), and Speech-to-Speech (S2S) systems in production environments. If you're creative & passionate about solving real world conversational AI problems, come join our Speech AI Engineering team.
What you’ll be doing:
Work on cutting-edge GPU-accelerated AI systems deployed at scale
Tackle challenging problems in real-time streaming audio processing and low-latency inference
Troubleshoot and resolve complex issues across ASR, TTS, ALM, and S2S pipelines
Model Integration: Work alongside Model researchers to transition ASR, TTS and S2S models from research to production readiness.
Develop Core Speech Services: Build and enhance C++ & python backend implementations for ASR, TTS, and S2S pipelines, leveraging CUDA for GPU acceleration
Optimize Inference Performance: Improve streaming latency and throughput through advanced batching strategies, encoder caching, and multi-threaded pipeline optimizations
Feature Development: Add new capabilities such as advanced voice activity detection, speaker diarization, decoder implementations (CTC, WFST, Flashlight), and text post-processing
Client Libraries: Contribute to Python and C++ client SDKs and CLI tools for easy service integration
Assist with API integration, SDK usage, model deployment, and performance optimization
Provide advanced technical guidance to customers implementing speech technology solutions
What we need to see:
Masters or BE/BTech in Computer Science, computer architecture, or related field
6+ years of experience
Excellent C++ & Python programming and software design skills, including debugging, performance analysis, and test design.
Experience with inference pipelines for LLM, Speech Recognition & Speech Synthesis
Solid understanding of modern model architectures (Transformers, CNNs, RNNs)
Excellent debugging abilities spanning multiple software (storage systems, kernels and containers)
Experience building and deploying cloud services using HTTP REST, gRPC, Websockets and related technologies
Strong collaborative and interpersonal skills, specifically a proven ability to effectively guide and influence within a dynamic matrix environment
Ability to work independently, define project goals and scope and manage your own development effort.
Knowledge of real-time streaming audio systems and low-latency architectures
Experience with speech model fine-tuning or customization
Ways to stand out from the crowd:
Publications or contributions to ML optimization open-source projects.
Experience with embedded systems or edge deployment
NVIDIA is committed to fostering a diverse work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.
How we rate this
Senior System Software Engineer, Speech AI at NVIDIA rates 90 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Also common in software engineer jobs at this level
Pay
- Typical software engineer job
- $167k
This posting doesn't list pay. Use the typical software engineer figure when pay comes up. See software engineer jobs
Questions you could be asked
- Walk me through fine-tuning a model: what data did you use, and how did you check the result?
- What have you built with speech recognition or text-to-speech, and where did it break?
- Tell me about a project where speech recognition was part of your work. What did you do?
- Tell me about a project where text to speech was part of your work. What did you do?
- Tell me about a project where deep learning was part of your work. What did you do?
Adapt your resume
- List these exact terms on your resume: Fine-tuning, Speech, Speech Recognition, Text To Speech, and Deep learning. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.
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