EnCharge AI

EnCharge AI provides efficient, low‑power AI computing solutions that let businesses run advanced models from edge devices to the cloud. The company designs analog in‑memory computing chips and pairs…

AI ComputeEdge AIEfficiencyHardwareIn-Memory ComputingPerformanceSustainabilityTCOenchargeai.com

EnCharge AI

AI ComputeEdge AIEfficiencyHardwareIn-Memory ComputingPerformanceSustainabilityTCOenchargeai.com

HQSanta Clara, US

Team Size85

Open Jobs611

Total Funding$163M

Latest Fundraiselast year

TL;DR

Founded: 2022

Headquarters: Santa Clara, California

Tech focus: Analog in-memory AI accelerators and software for edge-to-cloud

Recent funding: Series B > $100M (announced Feb 2025)

Company Overview

Problem Domain

Energy- and space-constrained AI inference acceleration for edge-to-cloud deployments.

Founded

2022

Industry

Data and Analytics

Tech Stack

Analog in-memory compute chips

Hardware systems for dense matrix operations

Software stack for inference/model deployment

Funding Track Record

Seed / Series A- 2022-12-14

21700000

Emergence from stealth with $21.7M

Undisclosed round- 2023-12-05

22600000

Raised $22.6M to commercialize chips

Series B- 2025-02-13

100000000+

Series B announced as more than $100M

Investor Signal

“Includes both financial and strategic investors such as Tiger Global, Samsung Ventures, CTBC/HH-CTBC, AlleyCorp, and others”

Founders

What we do

Join the Team

Physical Design Engineer (From Tier1 colleges like IITs,NITs,IIITs)

HybridGreater Bengaluru Area, IN

Hybrid • Greater Bengaluru Area, IN

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(www.enchargeai.com)

Physical Design Engineer (From Tier1 colleges like IITs,NITs,IIITs)

Location: Greater Bengaluru Area (Hybrid)

Physical Design Engineer (2-4 Years Experience)

EnCharge AI is a leader in advanced AI hardware and software systems for edge-to-cloud computing. EnCharge’s robust and scalable next-generation in-memory computing technology provides orders-of-magnitude higher compute efficiency and density compared to today’s best-in-class solutions. The high-performance architecture is coupled with seamless software integration and will enable the immense potential of AI to be accessible in power, energy, and space constrained applications. EnCharge AI launched in 2022 and is led by veteran technologists with backgrounds in semiconductor design and AI systems.

Physical Design Engineer (2-4 Years Experience)

We are looking for a high-caliber Physical Design Engineer to join our fast-paced startup team. This role is designed for "top 1%" talent—engineers from tier-1 universities (IIT/NIT/BITS or equivalent) who possess a relentless "go-getter" attitude and the critical thinking skills required to solve complex, next-generation silicon challenges.

If you are eager to move beyond standard digital flows into the future of AI-driven automation , this is your playground.

Key Responsibilities

End-to-End Ownership: Drive RTL-to-GDSII implementation, including floorplanning, placement, CTS, routing, and physical verification (LVS/DRC/ERC).

Qualifications & Requirements

Contact

Uday

muday_bhaskar@yahoo.com

Mulya Technologies

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Performance Optimization: Execute high-performance design closure focusing on PPA (Power, Performance, Area) targets in advanced process nodes ( 3nm or below)

Static Timing Analysis (STA): Perform comprehensive timing closure, including signal integrity, crosstalk analysis, and CPPR (Common Path Pessimism Removal) optimization.

Power Integrity: Conduct IR-drop analysis (Static/Dynamic) and EM (Electromigration) checks to ensure robust power delivery.

Innovation: Develop and integrate AI/ML-based automation scripts to optimize the physical design flow and reduce turnaround time.

Education: B.Tech/M.Tech in Electrical/Electronics Engineering from a Top Tier University .

Experience: 2–4 years of hands-on experience in Physical Design within the semiconductor industry.

Tool Expertise: Expert-level proficiency in industry-standard tools (Preferred Innovus ).

Technical Depth: Strong understanding of multi-corner multi-mode (MCMM) closure, low-power design techniques (UPF/CPF), and physical verification.

Mindset: A proven "go-getter" who is diligent, detail-oriented, and capable of taking complete ownership of blocks under tight deadlines.

The "Plus": Proficiency in Python/Tcl and experience using AI/ML models for EDA tool automation or predictive PPA analysis.