SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
Rula is seeking an Engineering Manager for AI/ML to lead its AI Engineering team through technical leadership spanning both applied product capabilities and foundational ML platforms. You will guide a team of Senior and Staff engineers to rapidly deliver user-facing features such as transcript summarization and search relevance, while architecting core AI/ML infrastructure including feature stores and AI observability platforms. The role requires balancing strong execution speed, coaching excellence, and rigorous clinical safety standards to ensure solutions scale safely in a regulated healthcare environment. You will shape Rula's AI engineering culture, drive high talent density, and directly impact how mental healthcare is delivered.
Rula is a remote-first, US-based company (not currently hiring in Hawaii). The company is Series C-funded and focused on making mental health treatment accessible and evidence-based.
Key Responsibilities:
- Lead and manage a team of Senior and Staff-level AI/ML engineers
- Guide delivery of user-facing AI features (transcript summarization, search relevance, LLM integrations)
- Architect and scale core ML infrastructure (feature stores, MLOps pipelines, model deployment, AI observability)
- Balance concurrent delivery of product features and backend platform work across multiple production cycles
- Apply clinical safety rigor and compliance standards (HIPAA) without sacrificing engineering velocity
- Foster a culture of high talent density and technological synergy
Required Qualifications:
- 8+ years of professional software engineering and machine learning experience
- 5+ years designing, scaling, and deploying distributed backend systems in cloud environments (AWS, GCP, etc.)
- 3+ years of direct engineering management experience with proven track record of hiring, retaining, and managing Senior and Staff-level engineers
- 3+ years of technical leadership or building core ML infrastructure (feature stores, MLOps pipelines, model deployment systems, AI observability platforms)
- 2+ years directing or developing production-grade applied AI features, specifically LLM integrations (NLP, transcript summarization, RAG) or search/ranking/relevance engines
- Proven operational track record managing concurrent delivery of user-facing product features and backend infrastructure platform work across multiple production release cycles
Preferred Qualifications:
- Experience shipping advanced AI systems in high-stakes, regulated environments (HealthTech or FinTech) with ability to balance privacy, compliance (HIPAA), and safety standards without compromising engineering velocity
- Deep, hands-on experience designing production-ready evaluation frameworks for LLMs (automated metrics, LLM-as-a-judge, Human-in-the-Loop processes) to mitigate hallucinations and bias
- Demonstrated philosophy on using AI to multiply engineering output; active deployment of cutting-edge AI dev tools (coding agents, advanced IDE integrations, automated testing bots)
- Clear framework for managing tension between short-term product delivery and long-term platform investments, with demonstrated prioritization of technical debt and infrastructure scaling against aggressive feature sprints
