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About the Role

We're looking for an Applied AI Engineer to own the model layer that makes Soulside's documentation trustworthy. Your job is to build the post-training pipelines and evaluation systems that get our models clinically sound, defensible for medical necessity, and safe, 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.

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.

Requirements

• 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.
• 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.

Skills

• Applied ML
• AI Engineering
• LLM
• Post-training pipelines
• Evaluation systems
• Supervised fine-tuning
• Preference optimization
• DPO/RLHF
• LoRA/adapters
• Distillation
• Model serving
• Fireworks AI
• Baseten
• Together AI
• Prompt engineering
• Structured output validation
• Python
• PyTorch
• Hugging Face
• Clinical NLP
• HIPAA
• RAG systems
• LLM observability
• Monitoring
• Drift detection
• Data pipeline
• Labeling workflows

Location

• US On-Site

Work Type

• On-Site
• Remote

Experience Level

• 3+ years

Education Level

• Master's degree

Benefits

• Competitive salary with a meaningful equity component
• 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

About the Company

• 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.