> Markdown version of [/jobs/ext/198036-software-engineer-genai-inference](https://www.wearedevelopers.com/jobs/ext/198036-software-engineer-genai-inference). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Software Engineer - GenAI inference - **Company:** Databricks - **Location:** San Francisco, CA, United States - **Salary:** $142,200.0 - $204,600.0 - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Cloud Computing, Profiling, Nvidia CUDA, Shard (Database Architecture), Distributed Systems, Memory Management, Fault Tolerance, Machine Learning, Open Source Technology, Software Engineering, Graphics Processing Unit (GPU), Large Language Models, Gpu Programming, Information Technology, Low Latency, Free and Open-Source Software, Machine Learning Operations, Software Version Control, Databricks - **Published:** May 31, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=65b5962ec5a79ba0 ## About the Role Do you have experience in Tooling?, Do you have a Bachelor's degree?, * BS/MS/PhD in Computer Science, or a related field * Strong software engineering background (3+ years or equivalent) in performance-critical systems * Solid understanding of ML inference internals: attention, MLPs, recurrent modules, quantization, sparse operations, etc. * Hands-on experience with CUDA, GPU programming, and key libraries (cuBLAS, cuDNN, NCCL, etc.) * Comfortable designing and operating distributed systems, including RPC frameworks, queuing, RPC batching, sharding, memory partitioning * Demonstrated ability to uncover and solve performance bottlenecks across layers (kernel, memory, networking, scheduler) * Experience building instrumentation, tracing, and profiling tools for ML models * Ability to work closely with ML researchers, translate novel model ideas into production systems * Ownership mindset and eagerness to dive deep into complex system challenges * Bonus: published research or open-source contributions in ML systems, inference optimization, or model serving Pay Range Transparency ## Description As a software engineer for GenAI inference, you will help design, develop, and optimize the inference engine that powers Databricks' Foundation Model API. You'll work at the intersection of research and production, ensuring our large language model (LLM) serving systems are fast, scalable, and efficient. Your work will touch the full GenAI inference stack - from kernels and runtimes to orchestration and memory management., * Contribute to the design and implementation of the inference engine, and collaborate on model-serving stack optimized for large-scale LLMs inference * Collaborate with researchers to bring new model architectures or features (sparsity, activation compression, mixture-of-experts) into the engine * Optimize for latency, throughput, memory efficiency, and hardware utilization across GPUs, and accelerators * Build and maintain instrumentation, profiling, and tracing tooling to uncover bottlenecks and guide optimizations * Develop and enhance scalable routing, batching, scheduling, memory management, and dynamic loading mechanisms for inference workloads * Support reliability, reproducibility, and fault tolerance in the inference pipelines, including A/B launches, rollback, and model versioning * Integrate with federated, distributed inference infrastructure - orchestrate across nodes, balance load, handle communication overhead * Collaborate cross-functionally: with platform engineers, cloud infrastructure, and security/compliance teams * Document and share learnings, contributing to internal best practices and open-source efforts when possible ## Related Videos - [Coffee with Developers - Stephen Jones - NVIDIA](https://www.wearedevelopers.com/videos/1303-coffee-with-developers-stephen-jones-nvidia) - [Cutting LLM Costs Without Cutting Quality: How to Beat Proprietary LLMs with Fine-Tuned Open Source](https://www.wearedevelopers.com/videos/100151-cutting-llm-costs-without-cutting-quality-how-to-beat-proprietary-llms-with-fine-tuned-open-source) - [Profiling Symfony & PHP apps with Blackfire](https://www.wearedevelopers.com/videos/265-profiling-symfony-php-apps-with-blackfire) - [Your Next AI Needs 10,000 GPUs. 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