Applied AI Engineer — Remote Behavioral Health ML

Soulside AI; San Francisco, Northern (CA, KY); Hybrid; USD 150,000 - 200,000; Full time

Benefits offered by this job

Health insurance

Dental insurance

Vision insurance

Remote-friendly culture

Founders access

Professional development budget

Conference attendance

Equity

Job summary

Soulside AI on-site in San Francisco is seeking an Applied AI Engineer to own the model layer that makes our documentation trustworthy. You will build post-training pipelines, fine-tune and deploy open-source models, and establish rigorous evaluation sets to measure clinical note quality and medical necessity.

Working at the intersection of applied ML and product, you will collaborate with clinical experts, optimize the end-to-end ML stack, and drive measurable improvements in our clinical

Qualifications

• 3+ years in applied ML / AI engineering, or a Master's degree in a related field.
• Hands-on experience taking LLM-based systems into production.
• Experience with post-training / fine-tuning open-source models using SFT, LoRA/PEFT, or preference-based methods.
• Experience serving or fine-tuning models on managed platforms such as Fireworks AI, Baseten, or Together AI.
• Strong Python and familiarity with the modern ML tooling ecosystem (PyTorch, Hugging Face, etc.).
• Solid grounding in prompt engineering and structured-output validation.

Responsibilities

• Build post-training pipelines on open-source models—supervised fine-tuning, preference optimization (DPO/RLHF), LoRA/adapters, and distillation—for domain-specific clinical tasks.
• Fine-tune, deploy, and serve models across managed inference and fine-tuning platforms such as Fireworks AI, Baseten, and Together AI, and make pragmatic build-vs-buy calls on where each workload should run.
• Design and maintain rigorous evaluation sets for high-stakes tasks like clinical reasoning and AI note generation—defining metrics, curating gold-standard data, and building automated and human-in-the-loop eval harnesses.
• Turn eval results into a fast, trustworthy iteration loop: catch regressions before they ship, and quantify the impact of every model or prompt change.
• Optimize the full LLM pipeline—prompting, retrieval, structured output validation, latency, and cost.
• Partner with clinical experts to translate documentation and compliance requirements into model behavior and evaluation criteria.
• Monitor models in production for quality, drift, and failure modes, and close the loop back into training data and evals.

Skills

Python

Applied ML

LLM deployment

Prompt engineering

Hugging Face

Education

Master's degree in a related field

Tools

PyTorch

Hugging Face

LoRA/PEFT

SFT

Baseten

Fireworks AI

Together AI

Job description

Soulside AI on-site in San Francisco is seeking an Applied AI Engineer to own the model layer that makes our documentation trustworthy. You will build post-training pipelines, fine-tune and deploy open-source models, and establish rigorous evaluation sets to measure clinical note quality and medical necessity.

Working at the intersection of applied ML and product, you will collaborate with clinical experts, optimize the end-to-end ML stack, and drive measurable improvements in our clinical