Applied AI Engineer — Clinical ML for Behavioral Health

Sarah Smith Fund; San Francisco, Northern (CA, KY); Hybrid; USD 150,000 - 200,000; Full time

Benefits offered by this job

Remote-first culture

Direct access to founders

Professional development budget

Job summary

Soulside AI in San Francisco (US On-Site) is seeking an Applied AI Engineer to own the model layer that makes our clinical documentation trustworthy. You will fine-tune open‑source models, stand up the infrastructure to serve them, and build evaluation sets that show measurable improvements in quality and safety.

This hands‑on role sits at the intersection of applied ML and product, requiring collaboration with clinical teams and rapid iteration.

Qualifications

• 3+ years in applied ML / AI engineering, or a Master's degree in a related field, with hands-on experience taking LLM-based systems into production.
• Practical experience with post-training / fine-tuning open-source models (e.g., Llama, Qwen, Mistral) 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 (or comparable inference/training infra).
• Demonstrated ability to build evaluation frameworks for LLM tasks with measurable quality.
• Strong Python and familiarity with PyTorch and Hugging Face.
• Solid grounding in prompt engineering and structured-output validation.

Responsibilities

• Build post-training pipelines on open-source models—supervised fine-tuning, preference optimization, 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.
• Design and maintain rigorous evaluation sets for high-stakes tasks like clinical reasoning and AI note generation.
• Turn eval results into a fast, trustworthy iteration loop and quantify model changes.
• Optimize the full LLM pipeline—prompting, retrieval, validation, latency, and cost.
• Collaborate with clinical experts to translate documentation and compliance requirements into model behavior and eval criteria.
• Monitor models in production for quality, drift, and failure modes.

Skills

Applied ML experience

Python

LLM deployment

Remote startup vibe

Education

Master's degree in a related field

Tools

PyTorch

Hugging Face

Fireworks AI

Baseten

Together AI

Job description

Soulside AI in San Francisco (US On-Site) is seeking an Applied AI Engineer to own the model layer that makes our clinical documentation trustworthy. You will fine-tune open‑source models, stand up the infrastructure to serve them, and build evaluation sets that show measurable improvements in quality and safety.

This hands‑on role sits at the intersection of applied ML and product, requiring collaboration with clinical teams and rapid iteration.