Research Engineer, AI Models

EnCharge AI; Germany (OH); On-site; USD 132,329 - 175,678; Full time

Job summary

EnCharge AI is seeking a Research Engineer to enhance AI model capabilities, quality, and efficiency. You will implement advanced ML techniques while collaborating with our hardware and software teams to ensure optimal performance. Candidates should have at least five years of experience in ML research, strong skills in Python and PyTorch, and familiarity with modern AI models such as transformers.

This dynamic role offers a competitive salary range between €116,000 and €154,000 per year, based on expertise. Join us in shaping the future of AI!

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.

Responsibilities

• Research and implement state-of-the-art techniques to accelerate AI inference.
• Partner with hardware and compiler teams for algorithmic improvements.
• Build profiling tools and comprehensive benchmarking frameworks.
• Develop fine-tuning workflows for modern AI models.

Skills

Machine Learning research

Python

PyTorch

Transformers

Diffusion models

Job description

EnChargeAI is building the next generation AI platform. Our novel in-memory-computing architecture delivers a 10x step-function improvement incomputeenergy 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 ourhardware’senergy efficiency advantages.We’rebuilding a vertically integrated AI stack that willshowcasethe transformative potential of our silicon while delivering real value to customers today.

We are seeking a Research Engineer to push the boundaries of AI modelcapability,quality,and efficiency.You’llbuild fine-tuningand post trainingpipelines, develop rigorous benchmarking frameworks, and work at the intersection of ML research and hardware-aware optimization—ensuring our modelsrunbeautifully on our silicon.

This is a role for someone who thrives at the boundary between research and engineering.You’llread 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 implementstate-of-the-arttechniques 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 findoptimaloperating 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.Identifyoptimizations aligned with ourarchitecture'sstrengths—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 withLoRA, 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 andPyTorch
• Hands-on experience with transformers, diffusion models,state spacemodelsetc.
• 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,MoErouting, flow matching)
• Background in hardware-aware ML optimization or quantization
• Familiarity with profiling tools (PyTorchProfiler, 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.