{"id":1314920,"url":"https://alion.io/job/internationalrecruit-ai-engineer-model-training-inference-infra","title":"AI Engineer, Model Training, Inference & Infra","company":{"id":3827146,"name":"Internationalrecruit","domain":"internationalrecruit.net","url":"https://alion.io/company/internationalrecruit","size_band":null,"is_staffing_agency":true,"employer_type":"agency","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["San Jose, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":156000,"max_usd":300000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":1332},"experience_years_min":8,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"AWQ","optional":false},{"name":"C++","optional":false},{"name":"CUDA","optional":false},{"name":"CUTLASS","optional":false},{"name":"DeepSpeed","optional":false},{"name":"Fine-tuning","optional":false},{"name":"FSDP","optional":false},{"name":"Function Calling","optional":false},{"name":"GPTQ","optional":false},{"name":"InfiniBand","optional":false},{"name":"JAX","optional":false},{"name":"Knowledge Distillation","optional":false},{"name":"Kubernetes","optional":false},{"name":"KV Cache","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"Megatron-LM","optional":false},{"name":"Model Distillation","optional":false},{"name":"NCCL","optional":false},{"name":"Platform Engineering","optional":false},{"name":"Post-training","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Quantization","optional":false},{"name":"Ray","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"Reward Modeling","optional":false},{"name":"RLAIF","optional":false},{"name":"RLHF","optional":false},{"name":"Rust","optional":false},{"name":"SFT","optional":false},{"name":"SGLang","optional":false},{"name":"SLURM","optional":false},{"name":"Speculative Decoding","optional":false},{"name":"Synthetic Data","optional":false},{"name":"TensorRT","optional":false},{"name":"TensorRT-LLM","optional":false},{"name":"TGI","optional":false},{"name":"Triton","optional":false},{"name":"vLLM","optional":false}],"status":"live","first_seen_at":"2026-09-25T21:44:46Z","employer_posted_date":null,"last_verified_at":"2026-09-25T21:44:46Z","board_verified":false,"closed_at":null,"days_open":38,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":38},"description":"Full-time · On-site · San Jose, CA · Austin, TX or Taiwan\nAbout Agentrys\nAgentrys is building the next generation of design automation for the semiconductor industry.\nOur mission is to enable every engineering organization to build its own self-improving agentic design workforce. Agentrys Studio combines AI agents, engineering knowledge, agent-native tools, advanced models, and continuous learning to automate complex chip-design workflows.\nOur team brings deep experience in artificial intelligence, electronic design automation, semiconductor design, GPU-accelerated computing, and production software systems. We work closely with leading semiconductor companies to turn advanced research into technology that improves engineering productivity, design quality, and time to market.\nThe Role\nWe are looking for an exceptional AI Engineer to own the model training, inference, and infrastructure that power Agentrys' agentic design workforce.\nYou will drive the full model lifecycle: data pipelines, pretraining and post-training, reinforcement learning, evaluation, and high-performance serving. You will build and operate the GPU training and serving stack - on the compute substrate our infrastructure team provides - that keeps large-scale training reliable and low-latency inference efficient at production scale. You will work on evaluation systems, model training, and the self-improving, self-evolving learning loops that let our models and agents get better over time from real execution feedback.\nYour work directly determines how capable, fast, and cost-effective our agents are. This role is ideal for someone who combines strong research ability with exceptional systems and performance engineering skills, and who wants the models they train and serve deployed in real semiconductor design environments-not left in notebooks or benchmarks.\nWhat You'll Do\nTrain, post-train, and fine-tune large language models for agentic engineering workflows, including supervised fine-tuning, RLHF/RLAIF, reinforcement learning, and distillation.\n\nBuild scalable data pipelines for pretraining, post-training, and evaluation, including sparse, private, and domain-specific engineering data.\n\nDesign and operate distributed training on multi-node GPU clusters, using data, tensor, pipeline, and sequence parallelism (for example FSDP, DeepSpeed, or Megatron-style approaches).\n\nBuild high-throughput, low-latency inference systems with continuous batching, KV-cache management, paged attention, quantization, and speculative decoding.\n\nWrite and optimize custom GPU kernels (CUDA, Triton) and profile end-to-end performance across CPUs and GPUs.\n\nBuild core model infrastructure: cluster orchestration, ML job scheduling, checkpointing, fault tolerance, reproducibility, model and environment management, observability, and cost and utilization tracking.\n\nBuild automated evaluation systems, benchmarks, and reward models that measure agent capability, reliability, and regression across complex engineering tasks, including problems where design data is private or customer-specific.\n\nDesign self-improving and self-evolving algorithms and learning loops, where models and agents learn from execution feedback, outcomes, and new data to improve continuously over time.\n\nIntegrate models with agent runtimes, tool use, retrieval, and the production serving stack.\n\nImprove reliability, throughput, and cost efficiency across the training and inference platform.\n\nTranslate promising research ideas into reliable, scalable product capabilities.\n\nCollaborate with research, product, platform, and solutions teams across San Jose, Austin, and Taiwan.\n\nContribute to patents, publications, technical presentations, and the broader development of Agentic Design Automation.\n\nWhat We're Looking For\nPhD or master's degree in Computer Science, Electrical Engineering, Computer Engineering, or a related field, or equivalent practical experience.\n\nStrong programming skills in Python and proficiency in at least one systems language such as C++ or Rust.\n\nDeep experience with machine learning frameworks such as PyTorch or JAX.\n\nHands-on experience with one or more of the following:\nLarge-scale or distributed model training\n\nHigh-performance model inference and serving\n\nGPU programming and performance optimization\n\nML infrastructure and platform engineering\n\nAutomated evaluation, reward modeling, or self-improving and continuous-learning systems\n\nAbility to take a model from data and problem formulation through training, evaluation, and production deployment.\n\nStrong analytical, software engineering, performance-optimization, and debugging skills.\n\nHigh ownership, intellectual curiosity, and willingness to work across research and product boundaries.\n\nClear written and verbal communication skills.\n\nParticularly Valuable Experience\nPretraining or post-training large language models at scale.\n\nDistributed training frameworks such as Megatron-LM, DeepSpeed, FSDP, or Ray.\n\nProduction inference engines such as vLLM, TensorRT-LLM, SGLang, or TGI.\n\nCustom kernel development with CUDA, Triton, or CUTLASS.\n\nInference optimization techniques such as quantization (FP8, GPTQ, AWQ), speculative decoding, or KV-cache optimization.\n\nReinforcement learning, RLHF, or reward-model training for LLMs.\n\nAutomated evaluation, benchmarking, or LLM-as-judge systems for agents.\n\nSelf-improving, self-evolving, or continuous-learning systems, including learning from execution feedback, automated curricula, or synthetic data generation.\n\nGPU cluster infrastructure with Kubernetes, Slurm, or Ray, and high-performance networking such as NCCL or InfiniBand.\n\nData pipelines and MLOps for training and continuous learning.\n\nExperience deploying AI systems in enterprise or security-sensitive environments.\n\nA strong record of implementation through research systems, open-source projects, production software, or technical competitions.\n\nWhy Agentrys\nAt Agentrys, you will have the opportunity to:\nHelp define a new category of semiconductor design technology.\n\nBuild the training and inference stack that powers autonomous engineering agents.\n\nDevelop GPU-accelerated systems that make large-scale training and low-latency serving practical and cost-effective.\n\nBuild the evaluation and self-improvement loops that let agents learn and get better from real engineering work.\n\nBuild AI systems that perform complex, consequential engineering work-not just generate recommendations.\n\nWork with real semiconductor workflows, tools, and private engineering knowledge.\n\nSee your models deployed directly with leading chip-design organizations.\n\nWork in a small, highly technical team where individual contributions can shape the product and company.\n\nCollaborate with colleagues across San Jose, Austin, and Taiwan.\n\nChange how chips are designed, rather than focus on only one design or one point tool.\n\nAgentrys is an equal opportunity employer. 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