Staff Machine Learning Engineer

SuperDial

About The Position

We are hiring a Machine Learning Engineer to fine-tune and improve LLMs for healthcare-specific agentic conversations and actions, with an initial focus on revenue cycle management (RCM). You do not need prior RCM expertise — we expect you to learn the domain on the job through close collaboration with product, engineering, operations, and healthcare subject-matter experts. This role is ideal for someone with hands-on experience fine-tuning open-source LLMs, especially using supervised fine-tuning (SFT) pipelines, and a strong interest in building reliable AI systems for real-world workflows.

Requirements

Hands-on experience fine-tuning open-source LLMs using supervised fine-tuning (SFT) pipelines.
Strong Python and PyTorch skills.
Experience with LLM tooling such as Hugging Face Transformers, PEFT, TRL, Axolotl, DeepSpeed, FSDP, vLLM, or similar frameworks.
Experience preparing, cleaning, labeling, and validating instruction-tuning datasets.
Familiarity with agentic LLM systems, including tool calling, structured generation, retrieval, or workflow execution.
Experience evaluating LLMs using offline benchmarks, human review, regression testing, or production feedback.
Strong debugging skills and the ability to systematically improve model behavior.
You have taken open-source LLMs through fine-tuning, evaluation, and iteration for a real product or workflow.
You are comfortable working across modeling, data, infrastructure, and product behavior.
You do not need prior healthcare revenue cycle management experience, but you should be motivated to learn the domain deeply and build reliable AI systems for complex operational environments.

Nice To Haves

Experience with RLHF, RLAIF, DPO, PPO, GRPO, reward modeling, or other preference-optimization methods.
Experience deploying or monitoring LLMs in production.
Experience with synthetic data generation, distillation, LoRA/QLoRA, or distributed training.
Prior exposure to healthcare, fintech, insurance, operations automation, or other high-accuracy domains.
Interest in learning healthcare revenue cycle workflows such as claims, denials, eligibility, prior authorization, and payer follow-up.

Responsibilities

Build, maintain, and improve supervised fine-tuning pipelines for open-source LLMs.
Fine-tune models for healthcare administrative workflows involving multi-turn conversations, tool use, structured outputs, and task execution.
Develop and refine training datasets from expert examples, workflow traces, synthetic data, user interactions, and model failure cases.
Create evaluation frameworks to measure task completion, instruction following, factual grounding, tool-use accuracy, and regression quality.
Analyze model failures and translate findings into improvements across data, training, prompting, retrieval, tooling, and product behavior.
Collaborate with healthcare and RCM experts to learn workflows and convert domain knowledge into model behavior.
Support production deployment, monitoring, and continuous model improvement.