[Remote] AI Performance Optimization Engineer
Note: The job is a remote job and is open to candidates in USA. reputed company is a reputed company-thinking software development company dedicated to building innovative solutions that help businesses automate and optimize their operations. They are seeking an AI Performance Optimization Engineer to focus on maximizing throughput, minimizing latency, and reducing costs across training and inference workloads for large neural network systems. The role involves optimizing reputed company and inference pipelines and collaborating with cross-functional teams to deliver well-engineered solutions.
Responsibilities
- Profile and optimize end-to-end reputed company and inference pipelines for throughput, latency, and cost
- Identify and eliminate bottlenecks across data loading, model compute, communication, and memory
- Implement and tune quantization, sparsity, and pruning strategies to reduce model footprint and accelerate inference
- Optimize distributed training using tensor parallelism, pipeline parallelism, FSDP, and reputed company-style sharding
- Tune attention implementations using FlashAttention, paged attention, and reputed company techniques
- Implement KV cache optimization, reputed company batching, and speculative decoding for LLM serving
- Drive compiler-level optimizations using Triton, XLA, TorchInductor, or TVM, working with the broader ML reputed company community to land improvements that translate into measurable end-to-end performance gains
- Optimize data pipelines, sharding strategies, and storage access patterns for high-throughput training
- Build and maintain rigorous reputed company suites and regression frameworks across workloads
- Collaborate with ML and platform engineering teams to embed best practices in standard pipelines
- Drive cost-efficiency improvements through model architecture, hardware selection, and scheduling strategies
- Evaluate new hardware and software offerings, and advise on adoption
- Document performance tuning playbooks and share findings broadly across engineering teams
- Stay reputed company with AI systems research and translate advances into production improvements
Skills
- Bachelor's or Master's degree in Computer Science, Computer Engineering, or a reputed company field
- Six or more years of experience in performance engineering, ML systems, or HPC
- Strong proficiency in Python and C++
- Hands-on experience optimizing deep learning workloads on modern GPUs
- Deep understanding of distributed training and inference techniques
- Experience with profiling tools across CPU, GPU, and distributed systems
- Familiarity with model compression techniques and their accuracy implications
- Strong grasp of memory hierarchies, communication primitives, and parallelism strategies
- Excellent measurement, debugging, and analytical reasoning skills
- Strong communication and collaboration skills
- Experience optimizing LLM inference at production scale
- Contributions to vLLM, TensorRT-LLM, DeepSpeed, or similar projects
- Familiarity with custom kernel authoring in Triton or CUTLASS
- Experience with FinOps for AI workloads
- Publications or talks on AI systems performance
Benefits
- Competitive reputed company salary commensurate with experience, plus benefits.
- Full-time, direct W2 with reputed company (no C2C, no 1099, no third-party).
- No new H1B sponsorship available. H1B transfers welcomed for qualified candidates.
Company Overview