Deloitte

Deloitte

Research Engineer — Post-Training & Small Language Models (SLMs), Healthcare AI

Arlington, Virginia

Sponsorship not specified$111k-$379kDetected 7 days ago
PythonCloud PlatformsMachine LearningPyTorchNLPLLMsAgentic AIComplianceHIPAAResearch

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Cap-exempt (no lottery)0
Sponsors this role0
Entry-level history0
PERM / green-card track0
Lottery odds (Level IV)94
Fits your clock70

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About the role

  • It is a ground-up rebuild of the decision-making machinery behind American healthcare, at national scale.
  • This is resourced to do real post-training at scale - committed investment in GPU compute and training infrastructure, not toy fine-tunes.
  • Healthcare decisioning is one of the cleanest verifiable-reward domains outside math and code: the problems are hard.

Requirements

  • Hands-on experience with reasoning-model training and/or verifiable-reward (RLVR) workflows.
  • Experience with open-weight foundation models such as Llama, Qwen, Mistral, DeepSeek, or equivalent architectures.
  • experience with distributed training frameworks such as DeepSpeed, FSDP, Megatron-LM, or Ray.
  • Experience implementing efficient fine-tuning techniques such as LoRA, QLoRA, PEFT, and quantization-aware workflows.
  • experience with large-scale and synthetic datasets, filtering, deduplication, and quality-control pipelines.
  • ability to work through ambiguous, highly complex technical problems in fast-moving environments.
  • Ability to travel 0-50%, on average, based on the work you do and the clients and industries/sectors you serve.
  • Familiarity with inference optimization frameworks such as vLLM, TensorRT-LLM, TGI, or Ollama.
  • Experience with multimodal models, speech models, or domain-specific foundation models
  • experience using large-scale GPU clusters and distributed compute.

Compensation

  • $111k-$379k

Company info

  • Deloitte brings together AI researchers, modeling and platform engineers, architects, clinical and domain specialists, and product leaders to build, deploy, and operate verticalized AI systems across software, data, models, and cloud infrastructure - engineered for one of the most complex operating environments in the world.
  • The work spans the healthcare industry - payers, providers, and life sciences - and involves genuinely hard reasoning problems, nuanced operational workflows, and a high bar for reliability, with little tolerance for shallow or unreliable outputs.
  • We pair frontier AI research with production-grade engineering, and we ship into real clinical and operational settings rather than leaving models in the lab.
  • You can go deep.
  • The team sub-specializes across post-training research, data and reward engineering, and training and inference infrastructure - you won't be expected to own all of it alone.
  • Qualifications - Required Skills And Experience
  • Bachelor's degree in Computer Science, Machine Learning, Artificial Intelligence, Applied Mathematics, Computational Linguistics, or a related field.
  • Demonstrated depth training and post-training large transformer-based language models in production or research - this is your craft, not coursework or a one-off fine-tune. Genuine depth including SFT and at least one preference-optimization or RL method, evidenced by shipped models, releases, or research.
  • Strong understanding of modern post-training techniques: SFT, RLHF, PPO, DPO, GRPO, RLAIF, and preference optimization workflows.
  • Strong expertise in PyTorch and modern deep-learning tooling; experience with distributed training frameworks such as DeepSpeed, FSDP, Megatron-LM, or Ray.
  • Deep understanding of transformer architectures, tokenization, attention mechanisms, decoding strategies, and model scaling trade-offs.
  • Strong grasp of LLM evaluation methodologies, benchmarking, reward modeling, and alignment trade-offs; experience with large-scale and synthetic datasets, filtering, deduplication, and quality-control pipelines.
  • Strong Python engineering skills and production-grade software practices; ability to work through ambiguous, highly complex technical problems in fast-moving environments.
  • Limited immigration sponsorship may be available.
  • Experience building or optimizing reasoning models, agentic models, or tool-using LLM systems.
  • Experience with multimodal models, speech models, or domain-specific foundation models; experience using large-scale GPU clusters and distributed compute.
  • Contributions to open-source AI projects, research publications, benchmark development, or model releases.
  • Familiarity with safety, governance, and responsible-AI practices; experience in regulated or high-stakes industries such as healthcare, finance, insurance, or public sector.\
  • Wages and Salary
  • The wage range for this role takes into account the wide range of factors that are considered in making compensation decisions including but not limited to skill sets; experience and training; licensure and certifications; and other business and organizational needs.
  • The disclosed range estimate has not been adjusted for the applicable geographic differential associated with the location at which the position may be filled.
  • At Deloitte, it is not typical for an individual to be hired at or near the top of the range for their role and compensation decisions are dependent on the facts and circumstances of each case.
  • A reasonable estimate of the current range is $110,700-$379,200.
  • This position is aligned with the Core Talent Model.
  • To view the associated benefit package, please reference this document: https://resources.deloitte.com/:b:/r/sites/dnet-tod-us/Shared Documents/Benefits/USBenefitsJourneyC...

Visa & Work Authorization

  • Limited immigration sponsorship may be available
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This listing is sourced directly from Deloitte's careers page and normalized into a canonical job model.