Staff Software Engineer, Applied AI

Valence · San Francisco, CA · 2 wk ago

HybridEngineering$220k–$405k/yrFull-time

The Role

You’ll tackle real-world AI problems - from transforming enterprise data into actionable context, to optimizing conversational experiences, to shaping how AI engages with users in meaningful and responsible ways. Your work will directly impact how our platform performs in high-stakes enterprise deployments and how leaders around the world grow through AI-facilitated insights.

About Valence

We're the only company pioneering leadership coaching for large enterprises in an AI-first way. Our mission is to transform how the world's biggest companies approach learning and development, helping teams work better together through AI-powered personalization that adapts to individual goals and organizational culture using the latest advances in machine learning and natural language processing.

What You'll Do

Architect and build enterprise-grade AI and conversational systems that power coaching workflows and user experiences.
Develop, evaluate, and refine LLM-based components - balancing performance, scalability, and reliability in real use cases.
Integrate and manage diverse sources of structured and unstructured data to improve contextual understanding and output quality.
Partner closely with product, engineering, and design to translate user needs into impactful technical solutions.
Rapidly prototype and iterate on systems that span backend services, data pipelines, and frontend interactions as needed.
Build tooling, tests, and automation to support reliable model deployment, observability, and continuous improvement.
Help streamline data and science workflows, enabling fast experimentation and data-driven decisions.

What We're Looking For

Technical foundation: 8+ years of experience in software engineering, AI/ML, data-intensive systems, AI/ML development (ideally including a Master's or Ph.D. in Computer Science, ML, Data Science, or a related field).
AI systems mindset: Familiarity with language systems (e.g., NLP, conversational interfaces, IR) and comfort reasoning about model behavior, context, and evaluation - both theoretical and practical knowledge.
Data tooling & analysis: Experience with core data science tools such as NumPy, scikit-learn, Pandas, PySpark, plus SQL and common visualization tools (e.g., matplotlib, Seaborn, Plotly, or BI tools) to explore and communicate insights.
Cloud & deployment experience: Comfortable developing and deploying services in cloud environments (AWS, GCP, Azure) and working with containerization/orchestration (Docker, Kubernetes).
Engineering excellence: Strong software engineering skills, including writing maintainable code, debugging distributed systems, and collaborating in cross-functional teams.
Growth orientation: Eagerness to tackle unfamiliar problems, learn new technologies, and contribute to shaping our platform and culture.
Communication: Ability to explain technical ideas clearly and work effectively with both technical and non-technical stakeholders.

Compensation