---
title: 'Research Engineer, AI Models at EnCharge AI'
canonical: 'https://feeny.ai/job/research-engineer-ai-models-encharge-ai-germany-5kv1pnxdmybd'
type: 'job'
last_seen: '2026-09-12'
---

# Research Engineer, AI Models at EnCharge AI

- **Company:** EnCharge AI
- **Location:** Germany
- **Posted:** 2026-06-29
- **Last confirmed live:** 2026-09-12
- **Apply:** https://job-boards.greenhouse.io/enchargeai36/jobs/4300106009

## Job description

Research Engineer, Applied AI

Location: Germany

About EnCharge AI:

EnCharge AI is building the next generation AI platform. Our novel in-memory-computing architecture delivers a 10x step-function improvement in compute energy efficiency and performance for AI inference workloads. As the demands of artificial intelligence move beyond today's models, we believe fundamental underlying infrastructure must evolve. We are an experienced team of AI researchers, silicon & systems engineers, and architects backed by leading investors, poised to become the essential platform for the next wave of AI innovation.

The Opportunity:

Modern AI workloads—from large language models to diffusion-based generators to multimodal systems—represent some of the most compute-intensive frontiers in AI, and some of the most promising applications for our hardware’s energy efficiency advantages. We’re building a vertically integrated AI stack that will showcase the transformative potential of our silicon while delivering real value to customers today.

We are seeking a Research Engineer to push the boundaries of AI model capability, quality, and efficiency. You’ll build fine-tuning and post training pipelines, develop rigorous benchmarking frameworks, and work at the intersection of ML research and hardware-aware optimization—ensuring our models run beautifully on our silicon.

This is a role for someone who thrives at the boundary between research and engineering. You’ll read papers, implement techniques, and ship production-quality code—all in service of making AI inference faster, cheaper, and better.

Key Responsibilities:

- Algorithmic Acceleration: Research and implement state-of-the-art techniques to accelerate AI inference—quantization, sparsity, distillation, speculative decoding, caching strategies, and architectural modifications. Systematically characterize tradeoffs between model quality, latency, throughput, and power consumption to find optimal operating points across different use cases.
- Hardware Co-Design: Partner closely with hardware, compiler, and quantization teams to ensure algorithmic improvements translate to real gains on our silicon. Identify optimizations aligned with our architecture's strengths—maximizing throughput while minimizing power. Shape the feedback loop between model development and hardware.
- Evaluation: Build profiling tools and comprehensive benchmarking frameworks to understand compute bottlenecks, measure model quality across standard and domain-specific evals, and track efficiency metrics.
- Applied Research: Build robust fine-tuning workflows for modern AI models, enabling rapid experimentation with LoRA, adapters, and full fine-tuning. Stay current with the rapidly evolving landscape—evaluate new architectures, implement promising techniques, and contribute insights that inform technical and go-to-market strategy.

Qualifications:

- 5+ years of experience in ML research, applied ML, or ML systems
- Strong fundamentals in Python and PyTorch
- Hands-on experience with transformers, diffusion models, state space models etc.
- Experience fine-tuning large models and building training/evaluation pipelines
- Deep understanding of transformers, attention mechanisms, & optimization techniques
- Comfort reading and implementing techniques from research papers

Nice to Have:

- Experience with efficient inference techniques (KV cache optimization, attention variants, MoE routing, flow matching)
- Background in hardware-aware ML optimization or quantization
- Familiarity with profiling tools (PyTorch Profiler, Nsight, custom instrumentation)
- Publications in generative modeling, efficient inference, or ML systems
- Contributions to open-source ML projects

The salary range for this position is €116,000 to €154,000 EUR per year. Actual compensation offered will be determined based on job-related knowledge, skills, and experience.

## About EnCharge AI

## Company Overview
- **One-liner**: EnCharge AI develops analog in-memory computing hardware and software to deliver high-efficiency, low-cost AI inference from edge devices to cloud data centers.
- **Entity Type**: Private (Series B – II stage)
- **Headquarters**: Santa Clara, California, United States
- **Founded**: 2022
- **Founders**: Naveen Verma, Ph.D. (CEO) and Kailash Gopalakrishnan, Ph.D. (CTO)

## Core Business
- **Primary industry**: AI semiconductor hardware and embedded software products
- **Target customers**: B2B – enterprises deploying AI at scale, edge-device manufacturers, cloud service providers, and defense/industrial sectors
- **Mission**: Democratize advanced AI by enabling deployment beyond cloud infrastructure to local servers and mobile devices, while slashing costs and environmental impact

## Products & Services
- **Analog In-Memory Computing GPUs & Digital AI Accelerators**: Custom silicon (chiplets, ASICs) and standard-form-factor PCIe cards that perform AI inference using analog computation for orders-of-magnitude better energy efficiency and compute density.
- **Software Stack**: Seamless orchestration layer that supports on-device and cloud deployment, enabling model portability, quantization, and compiler optimizations across EnCharge’s hardware.
- **Edge-to-Cloud Platforms**: Integrated solutions for power-, space-, and cost-constrained environments, covering autonomous systems, smart devices, data center inference, and private on-premise AI.

## Market Standing
- **Valuation / Market Cap**: Not publicly disclosed
- **Key Metrics**: Annual Revenue ~$10M (LinkedIn estimate) | Total Funding $162.9M
- **Notable Investors & Partners**: Tiger Global Management (led $100M Series B, Mar 2025), Anzu Partners (led $21.7M Series A, Dec 2022), DARPA ($18.6M grant, Mar 2024), and others including corporate/institutional backers.
- **Growth Signals**: 
  - 58.6% YoY headcount growth (75 employees, as of mid-2025)
  - 39 active job openings spanning hardware, compiler, and AI architecture roles
  - 150+ patents granted, 300+ technical publications
  - Talent inflows from Intel, IBM, Cerebras, AMD, Meta, Microsoft

## Competitive Advantages
- **Analog In-Memory Computing Moa**t: A proprietary compute architecture that uses charge-domain analog circuits, validated silicon, and leverages existing semiconductor supply chains.
- **Record Efficiency**: Claims 20× higher TOPS/W, 9× higher compute density (TOPS/mm²), 10× lower total cost of ownership, and 100× lower CO₂ emissions compared to leading cloud/GPU alternatives.
- **Deep IP Portfolio**: 150+ granted patents and 300+ publications from 20+ years of foundational research by the founding team (Princeton, IBM, etc.).
- **End-to-End Solution**: Full-stack hardware and software (compiler, quantization, runtime) designed for seamless edge-to-cloud orchestration without vendor lock-in.

## Strategic Focus
- Scale production and customer deployments of their analog in-memory compute chips for high-volume edge and data center inference.
- Expand software ecosystem to support mainstream AI frameworks (PyTorch, TensorFlow, ONNX) and enable easy drop-in replacement for existing GPU-accelerated workloads.
- Deepen partnerships with defense (DARPA) and enterprise cloud providers, emphasizing data privacy, security, and sustainability.
- Double down on sustainability messaging (100× lower CO₂) to meet ESG mandates in enterprise AI procurement.

## Why Work Here
- **Mission-Driven Impact**: Opportunity to make AI accessible to the “99%” by dramatically reducing energy and cost barriers. Work is cited as a “once-in-a-lifetime” chance by leadership.
- **World-Class Team**: Colleagues include industry luminaries from Princeton, IBM, and top semiconductor houses; 57% of staff are in technical roles (engineering, research, compiler).
- **Culture of Collaboration & Innovation**: The careers page highlights integrity, growth, and collaborative problem-solving in a fast-paced startup environment.
- **Comprehensive Rewards**: Benefits package includes typical startup perks; equity likely offered given funding stage.
- **Work Policy**: Hybrid presence indicated by offices in Santa Clara, Canada, Germany, Norway, South Korea, and India. Remote flexibility likely for some roles.
- **Active Hiring**: 39 open positions across DFT, physical design, compiler engineering, and NPU architecture – strong signal of scaling and technical investment.

## Sources
1. [EnCharge AI Official Website](https://www.enchargeai.com/)
2. [EnCharge AI Careers Page](https://www.enchargeai.com/careers)
3. [EnCharge AI About Us (Leadership & Founders)](https://www.enchargeai.com/about-us)
4. [EnCharge AI LinkedIn Company Page](https://www.linkedin.com/company/encharge-ai)
5. [CB Insights Profile – EnCharge AI](https://www.cbinsights.com/company/encharge-ai)

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