{"schemaVersion":"jobsearcher.job.v1","id":"b8df7d9904be3f76f6735ebd","url":"https://jobsearcher.com/jobs/b8df7d9904be3f76f6735ebd","canonicalUrl":"https://jobsearcher.com/jobs/b8df7d9904be3f76f6735ebd","title":"Machine Learning Engineer","description":"Summary: Works on data to build, train and deploy next-generation LLM-based recommender systems. The role covers the full path from raw customer data to a deployed model.Responsibilities:Data to training sets: turn unstructured and structured traveler and catalog data into narrative text documents and synthetic data, at a scale of tens of millions of documents.Feature and context engineering: decide what information goes into each document and how to express it, such as market-relative price, tier and trip context.Large-scale distributed training on Amazon SageMaker AI, including HyperPod.Fine-tuning: LoRA/QLoRA adapters, supervised training, and reinforcement learning for ranking and reasoning.Deep learning modeling: model architecture, ranking heads, co-training and multi-task setups.Inference and deployment: low-latency, cost-efficient serving, including quantization, distillation and profiling.Evaluation and error analysis: build evaluation sets, run ablations and catch data leakage. Write up findings clearly for the customer and the AWS science team.Data privacy: handle customer data safely, including removing perRequired skillsSpark (PySpark) and the Hadoop ecosystem (Hive, HDFS) on corpora of tens of millions of documents.Amazon SageMaker AI: processing, training, distributed training (HyperPod), deployment.LLM fine-tuning (LoRA/QLoRA) and reinforcement learning (e.g. GRPO/PPO-style methods).Strong deep learning fundamentals in PyTorch: architectures, training dynamics, co-training.Large-scale batch LLM inference and production inference (vLLM, Amazon Bedrock batch).Data privacy and security on AWS: personal-data removal, IAM, KMS, Lake Formation.Clear written communication with customer and science stakeholders.Preferred skillsAWS Trainium / Inferentia (Neuron SDK).Recommender systems: ranking metrics (NDCG, MRR), sequential and generative recommenders.Embedding quantization or semantic IDs.Experience in a customer-facing or embedded role.","company":"Arkhya Tech","rawCompany":"arkhya tech","city":"Denver","state":"CO","isRemote":false,"isActive":false,"createdAt":"2026-11-04T09:22:13.085Z","occupations":[{"code":"15-2051.00","title":"Data Scientists","slug":"data-scientists"},{"code":"15-1252.00","title":"Software Developers","slug":"software-developers"},{"code":"15-1221.00","title":"Computer and Information Research Scientists","slug":"computer-and-information-research-scientists"}],"industries":[{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"},{"code":"541511","title":"Custom Computer Programming Services","slug":"custom-computer-programming-services"},{"code":"518210","title":"Computing Infrastructure Providers, Data Processing, Web Hosting, and Related Services","slug":"computing-infrastructure-providers-data-processing-web-hosting-and-related-services"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Machine Learning Engineer","description":"Summary: Works on data to build, train and deploy next-generation LLM-based recommender systems. The role covers the full path from raw customer data to a deployed model.Responsibilities:Data to training sets: turn unstructured and structured traveler and catalog data into narrative text documents and synthetic data, at a scale of tens of millions of documents.Feature and context engineering: decide what information goes into each document and how to express it, such as market-relative price, tier and trip context.Large-scale distributed training on Amazon SageMaker AI, including HyperPod.Fine-tuning: LoRA/QLoRA adapters, supervised training, and reinforcement learning for ranking and reasoning.Deep learning modeling: model architecture, ranking heads, co-training and multi-task setups.Inference and deployment: low-latency, cost-efficient serving, including quantization, distillation and profiling.Evaluation and error analysis: build evaluation sets, run ablations and catch data leakage. Write up findings clearly for the customer and the AWS science team.Data privacy: handle customer data safely, including removing perRequired skillsSpark (PySpark) and the Hadoop ecosystem (Hive, HDFS) on corpora of tens of millions of documents.Amazon SageMaker AI: processing, training, distributed training (HyperPod), deployment.LLM fine-tuning (LoRA/QLoRA) and reinforcement learning (e.g. GRPO/PPO-style methods).Strong deep learning fundamentals in PyTorch: architectures, training dynamics, co-training.Large-scale batch LLM inference and production inference (vLLM, Amazon Bedrock batch).Data privacy and security on AWS: personal-data removal, IAM, KMS, Lake Formation.Clear written communication with customer and science stakeholders.Preferred skillsAWS Trainium / Inferentia (Neuron SDK).Recommender systems: ranking metrics (NDCG, MRR), sequential and generative recommenders.Embedding quantization or semantic IDs.Experience in a customer-facing or embedded role.","datePosted":"2026-11-04T09:22:13.085Z","dateModified":"2026-11-04T09:22:13.085Z","hiringOrganization":{"@type":"Organization","name":"Arkhya Tech","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Denver","addressRegion":"CO","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"b8df7d9904be3f76f6735ebd"},"url":"https://jobsearcher.com/jobs/b8df7d9904be3f76f6735ebd"}}