Applied AI Engineer role at Soulside AI focused on owning the model layer for trustworthy behavioral health documentation, reporting to the CTO.

Build post-training pipelines for open-source models, including SFT, DPO/RLHF 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; make build-vs-buy decisions to match each workload.
Design and maintain rigorous evaluation sets for high-stakes outcomes like clinical reasoning and AI note generation, including metrics, gold-standard data curation, and automated plus human-in-the-loop eval harnesses.
Implement a fast iteration loop from eval results to training changes and releases, identifying regressions early and quantifying impact from model or prompt updates.
Optimize the complete LLM pipeline: prompting, retrieval, structured output validation, plus latency and cost.
Work 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, then feed insights back into training data and evaluation sets.

3+ years in applied ML/AI engineering, or a Master’s degree in a related field, with hands-on experience bringing LLM-based systems into production.
Practical experience with post-training and fine-tuning open-source models (e.g., Llama, Qwen, Mistral) using SFT, LoRA/PEFT, or preference-based methods.
Experience serving and/or fine-tuning on managed platforms such as Fireworks AI, Baseten, or Together AI (or similar inference/training infrastructure).
Proven ability to build evaluation frameworks for LLM tasks, focusing on measurable quality.
Python proficiency and familiarity with modern ML tooling (including PyTorch and Hugging Face).
Strong grounding in prompt engineering and structured-output validation.
Ability to thrive in a fast-paced, remote-first startup environment and communicate clearly with technical and clinical stakeholders.
Visa sponsorship available for the right candidate, including H-1B and O-1.

Salary: USD 150,000–200,000 per year, plus equity with significant upside potential as a founding team member.
Comprehensive health, dental, and vision insurance.
Flexible, remote-first culture.
Direct access to founders and influence on technical direction.
Professional development budget and conference attendance.
Opportunity to build AI that measurably improves mental health care at scale.

Experience with healthcare, clinical NLP, or other high-stakes regulated domains.
Familiarity with HIPAA and handling sensitive clinical data.
RAG systems, retrieval quality tuning, or long-context document workflows.
Experience with LLM observability, monitoring, and drift detection in production.
Experience with data pipelines and labeling workflows for curating high-quality training and eval sets.
Open-source contributions in the ML/LLM ecosystem.

Send your resume and a short note to [email protected].
Include a model or pipeline you took to production and explain how you validated it was actually working.

Applied AI Engineer

Job Description