Applied AI Engineer

APPLIED AI ENGINEER Soulside AI · US On-Site · Reports to the CTO ABOUT SOULSIDE Soulside AI is the specialist AI platform for behavioral health documentation and compliance. We generate audit-ready clinical documentation across individual and group sessions, virtual and in-person care, admissions, and treatment planning—and we embed real-time chart audits and payer-aligned compliance checks into everyday workflows. The result is immediate and measurable: higher-quality charts, stronger medical necessity, and hours given back to clinicians every week. We're backed by Counterpart Ventures, GreyMatter Capital, and One Mind, and we're a UCSF Rosenman Institute and One Mind Accelerator company. We've reached strong product-market fit and are scaling fast. THE ROLE We're looking for an Applied AI Engineer to own the model layer that makes Soulside's documentation trustworthy. In behavioral health, a note isn't just text—it has to be clinically sound, defensible for medical necessity, and safe. Your job is to build the post-training pipelines and evaluation systems that get our models there, and keep them there as we scale. This is a hands-on role for someone who lives at the intersection of applied ML and product. You'll fine-tune and adapt open-source models, stand up the infrastructure to serve them, and build the rigorous evaluation sets that tell us—objectively—whether a change made the product better or worse. WHY THIS ROLE MATTERS - Accuracy Isn't Optional: In behavioral health, a wrong or unsupported note has real clinical and financial consequences. The pipelines and evals you build are what let us ship model changes with confidence. - Own the Model Layer: You'll define how we post-train, evaluate, and deploy models end-to-end—not inherit someone else's stack. - Direct Clinical Impact: Every improvement in clinical reasoning or note quality directly reduces documentation burden and strengthens the charts clinicians and payers rely on. WHAT YOU'LL DO - 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. WHAT WE'RE LOOKING FOR - 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—you think in terms of measurable quality, not vibes. - Strong Python and familiarity with the modern ML tooling ecosystem (PyTorch, Hugging Face, etc.). - Solid grounding in prompt engineering and structured-output validation. - Ability to thrive in a fast-paced, remote startup and communicate clearly with technical and clinical teammates. - We're willing to sponsor visas, including H-1B and O-1, for the right candidate. BONUS POINTS - 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. - Data pipeline and labeling workflow experience for curating high-quality training and eval sets. - Open-source contributions in the ML/LLM ecosystem. WHAT WE OFFER - Salary range of $150,000–$200,000, 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 - The chance to build AI that measurably improves mental health care at scale HOW TO APPLY Send your resume and a short note to [email protected]. Tell us about a model or pipeline you took to production—and how you knew it was actually working.