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 is focused on post-training research, shaping model behavior for clinical and operational decisioning across the industry. This is a ground-up rebuild of decision-making machinery at national scale, with a commitment to real post-training at scale, not toy fine-tunes.

Key Responsibilities

Post-training & Alignment

• Design and execute post-training pipelines: supervised fine-tuning (SFT), preference optimization, and reinforcement learning (RL) / alignment workflows.
• Build and optimize training using techniques such as SFT, RLHF, PPO, DPO, GRPO, RLAIF, and Constitutional AI, and understand how each 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.

Requirements & Tech Stack

Qualifications

• 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, evidenced by shipped models, releases, or research.
• 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 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 Qualifications (Familiarity/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.