Research Engineer

About the Role

Deloitte has a new AI-first effort, backed by $1B in committed investment, building reasoning models and agentic systems to rebuild how the healthcare system decides. This role focuses on designing, training, evaluating, and aligning models that reason about healthcare across the full post-training lifecycle. This is not a prompt-engineering role; it requires deep understanding of how to improve and shape model behavior through advanced post-training.

Key Responsibilities

• Post-training & Alignment
• Design and execute post-training pipelines: supervised fine-tuning (SFT), preference optimization, and reinforcement learning / alignment workflows.
• Build and optimize training using techniques such as SFT, RLHF, PPO, DPO, GRPO, RLAIF, and Constitutional AI.
• Understand how each technique affects reasoning quality, safety, latency, cost, and reliability.
• Train reasoning models for healthcare decisioning using verifiable-reward RL, designing reward signals and verifiers grounded in clinical guidelines, policy and criteria, and adjudicated outcomes.
• Reward Modeling & Data
• Develop reward models and preference datasets to improve reasoning quality, factuality, safety, policy adherence, and task performance.
• Curate, clean, synthesize, and evaluate large-scale instruction, preference, and domain-specific datasets, with rigorous filtering, deduplication, and quality control.
• Build verification and reward pipelines from proprietary clinical, claims, and operational data and from clinical-expert labeling.
• Efficient Fine-tuning, Training & Inference Infrastructure
• Implement efficient fine-tuning strategies including LoRA, QLoRA, PEFT, and adapter-based approaches.
• Build scalable distributed training using DeepSpeed, FSDP, Megatron-LM, Ray, or equivalent.
• Optimize inference performance (latency, throughput, quantization, and deployment efficiency) for production, including frameworks such as vLLM, TensorRT-LLM, or TGI.
• Small Language Models & Open-Weight Models
• Train and optimize open-weight models such as Llama, Qwen, Mistral, or DeepSeek.
• Build specialized small language models (SLMs) for on-premise and cloud-hybrid deployment with strong performance-per-dollar.
• Evaluation, Safety & Red Teaming
• Design evaluation frameworks covering reasoning, hallucination detection, factuality, instruction following, structured outputs, and domain-specific metrics.
• Build healthcare-grade evaluation, co-designed with clinical experts.
• Apply PHI/HIPAA-aware data handling and produce model documentation suitable for regulated clinical use.
• Perform red teaming and adversarial testing to identify alignment failures, unsafe behaviors, jailbreak vulnerabilities, and regression risks.

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. Genuine depth including SFT and at least one preference-optimization or RL method.
• Hands-on experience with reasoning-model training and/or verifiable-reward (RLVR) workflows.
• Strong understanding of modern post-training techniques: SFT, RLHF, PPO, DPO, GRPO, RLAIF, and preference optimization workflows.
• Experience with open-weight foundation models such as Llama, Qwen, Mistral, DeepSeek, or equivalent architectures.
• Strong expertise in PyTorch and modern deep-learning tooling.
• 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.
• 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.
• Ability to travel 0-50%, on average.
• Additional Experience
• Experience building or optimizing reasoning models, agentic models, or tool-using LLM systems.
• 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.
• 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.