Full-Time
DFT Engineer
Hardware
Updated on 9/3/2026
NVIDIA
10,001+ employees
Designs GPUs and AI HPC platforms
No salary listed
Bengaluru, Karnataka, India
Hybrid
Hybrid role; some on-site days in Bengaluru, India; occasional travel for milestones.
Bachelor's, Master's
 Hardware Engineering (1) |
|---|
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- BSEE or MSEE from reputed institutions or equivalent experience with 2+ years of experience
- You should be well versed with static timing Analysis, ECO, ASIC/Logic Design Flow, HDL and Digital logic design.
- Experience in RTL and Gates verification and simulation.
- You need to be familiar with BIST architecture and JTAG/IEEE1149.1/IEEE1500.
- Strong DFT knowledge in Scan ATPG, compression techniques and memory test.
- Strong analytical and problem solving skills with good scripting knowledge (either Perl / Python)
- As a member in our team, you will be responsible for end-to-end DFT from RTL design to Gate level scan insertion to Verification (including JTAG, boundary scan, security mechanisms, and test clocking—across various test modes, including multiple ATPG configurations) to silicon bring-up.
- Your responsibility will also include verification and silicon bringup of Scan ATPG and other DFT features.
- In addition, you will help develop and deploy DFT methodologies for our next generation products.
- Be apart of innovation to strive improve the quality of DFT methods.
- You will also need to work with multi-functional teams to incorporate DFT features into the chip.
- Occasional travel and also some late hours online meetings involved during critical milestones.
NVIDIA designs and manufactures graphics processing units (GPUs) and computing platforms used for gaming, data centers, and artificial intelligence. These products work by using parallel processing to handle complex mathematical calculations much faster than standard computer processors, supported by a software ecosystem that allows developers to build and run AI models. Unlike competitors that may focus solely on hardware, NVIDIA integrates its chips with specialized software and cloud services to create a complete environment for high-performance tasks. The company’s goal is to provide the underlying technology necessary to power advanced computing, from realistic video game graphics to autonomous vehicles and large-scale data analysis.
Company Size
10,001+
Company Stage
IPO
Headquarters
Santa Clara, California
Founded
1993
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Simplify's Take
What believers are saying
- Amazon ordered 2 million GPUs for 2027-2028, validating hyperscaler demand.
- August 2026 Lancium investment secured 4 GW leased capacity and 15 GW pipeline.
- Blackwell Ultra and Vera Rubin shipments began; production ships in late 2026.
What critics are saying
- Taiwan prosecutors indicted NVIDIA staff August 24, 2026 over illegal China server exports.
- China’s antitrust probe still threatens fines, remedies, and slower mainland sales.
- China export controls and ASIC substitution can cut NVIDIA off from a trillion-dollar market.
What makes NVIDIA unique
- CUDA and NVLink lock developers into NVIDIA’s full-stack AI platform.
- FY2026 revenue hit $215.9 billion, with data center revenue $193.7 billion.
- Rubin launched February 2026, targeting 10x lower inference costs than Blackwell.
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NVIDIA has taken a strategic investment in Lancium, a Blackstone-backed infrastructure company, to deploy its AI platform across power-ready data centres. The deal provides NVIDIA access to 4 gigawatts of leased capacity and a development pipeline exceeding 15 gigawatts. The partnership addresses a critical bottleneck in AI infrastructure, where compute demand has outpaced available power-ready capacity at gigawatt scale. NVIDIA's DSX MaxLPS technology enables up to 40% more GPU density within the same power budget, improving deployment economics. Lancium is backed by Blackstone Energy Transition Partners and Blackstone Multi-Asset Investing. The investment signals NVIDIA is moving beyond hardware sales to secure physical capacity for customers, recognising that compute demand requires corresponding power and space infrastructure. The arrangement allows NVIDIA customers faster access to large-scale infrastructure built specifically for demanding AI workloads.
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