Research Engineer

About the Role

Deloitte has a new AI-first effort, backed by $1B in committed investment, to rebuild how the healthcare system decides across payers, providers, and life sciences. This role involves designing, training, evaluating, and aligning models that reason about healthcare, working across the full post-training lifecycle.

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, and understand their effects on 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, 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, turning guidelines, policy, and adjudicated outcomes into checkable reward signals.
• 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 equivalents.
• 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, including held-out clinical benchmarks, deployment regression gates, calibration and uncertainty, factuality against ground truth, and bias/fairness evaluation across patient populations and subgroups, 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.
• Collaborate with agentic and application teams to improve tool use, grounding, and long-horizon reasoning.

Requirements & Tech Stack

• Post-training & alignment
• 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.
• Models & Architectures
• Experience with open-weight foundation models such as Llama, Qwen, Mistral, DeepSeek, or equivalent architectures.
• Deep understanding of transformer architectures, tokenization, attention mechanisms, decoding strategies, and model scaling trade-offs.
• Infrastructure & Optimization
• 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.
• Familiarity with inference optimization frameworks such as vLLM, TensorRT-LLM, TGI, or Ollama.
• Experience using large-scale GPU clusters and distributed compute.
• Data & Evaluation
• 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.
• Software Engineering
• Strong Python engineering skills and production-grade software practices.
• Ability to work through ambiguous, highly complex technical problems in fast-moving environments.
• Other
• Ability to travel 0-50%, on average.

Qualifications

• Education
• Bachelor's degree in Computer Science, Machine Learning, Artificial Intelligence, Applied Mathematics, Computational Linguistics, or a related field.
• Experience
• Experience building or optimizing reasoning models, agentic models, or tool-using LLM systems.
• Experience with multimodal models, speech models, or domain-specific foundation models.
• 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.

Compensation

• The estimated wage range for this role is $110,700-$379,200.