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

Deloitte•Jersey City, NJ

122d•$110,700 - $379,200•Hybrid

About The Position

Deloitte has a new AI-first effort, backed by $1B in committed investment, building the reasoning models and agentic systems to rebuild how the healthcare system decides — across payers, providers, and life sciences, and for the patients they serve — so that care is faster, fairer, and far less wasteful. This is a ground-up rebuild of the decision-making machinery behind American healthcare, at national scale. This role is resourced to do real post-training at scale with committed investment in GPU compute and training infrastructure. As a Research Engineer on our post-training team, you will design, train, evaluate, and align the models that reason about healthcare — working across the full post-training lifecycle to shape model behavior for clinical and operational decisioning across the industry. Healthcare decisioning is one of the cleanest verifiable-reward domains outside math and code. We ground that reward in real signals — clinical policy and criteria, adjudicated outcomes, and clinical-expert judgment — so correctness is checkable rather than asserted. You will own the post-training stack for our clinical reasoning models end to end — from data and reward design through trained, evaluated models that ship. This is not a prompt-engineering role. We are looking for people who understand not just how to use LLMs, but how to improve and shape model behavior through advanced post-training. You do not need a healthcare background, as we pair every engineer with clinical and domain experts and teach you the domain. We hire on demonstrated depth, not years; the level you join at is determined through our interview process, based on the depth and judgment you demonstrate, not your years in a title.

Requirements

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.
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, based on the work you do and the clients and industries/sectors you serve.
Limited immigration sponsorship may be available.

Nice To Haves

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.

Responsibilities

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 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.
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 our proprietary clinical, claims, and operational data and from clinical-expert labeling — turning guidelines, policy, and adjudicated outcomes into checkable reward signals at scale.
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.
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.
Design evaluation frameworks covering reasoning, hallucination detection, factuality, instruction following, structured outputs, and domain-specific metrics.
Build healthcare-grade evaluation — 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.