Founding Applied AI Engineer defining AI-powered leadership coaching for enterprises. Collaborating with cross-functional teams to build intelligent systems at enterprise scale.
Responsibilities
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.
Requirements
3+ 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)
Familiarity with language systems (e.g., NLP, conversational interfaces, IR) and comfort reasoning about model behavior, context, and evaluation - both theoretical and practical knowledge
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
Comfortable developing and deploying services in cloud environments (AWS, GCP, Azure) and working with containerization/orchestration (Docker, Kubernetes)
Strong software engineering skills, including writing maintainable code, debugging distributed systems, and collaborating in cross-functional teams
Eagerness to tackle unfamiliar problems, learn new technologies, and contribute to shaping our platform and culture
Ability to explain technical ideas clearly and work effectively with both technical and non-technical stakeholders
Nice-to-have (but not required): experience with ML lifecycle tools (e.g., MLflow, Weights & Biases), familiarity with Cloud ML services, or past work building generative AI applications
Benefits
Competitive salary including base + bonuses
Comprehensive health coverage (medical, dental, vision) from day one
Generous PTO, company-wide R&R shutdowns, and paid parental leave
Retirement plan support for US and global employees
Meaningful ownership in a venture-backed company at a growth inflection point
Financial upside that comes from scaling fast
Top-up grants as we scale and you deliver exceptional performance — your compensation grows alongside your impact
A culture built for top talent: intensity to win, growth without limits, and a team that solves hard problems and celebrates big wins together
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