Applied Scientist, AI Product Methods 

About Evolver 

Evolver is an AI technology company transforming professional services. We combine deep domain expertise, advanced AI, and continuous applied learning to automate complex enterprise workflows while keeping people involved where judgment matters. 

Our goal is to help organizations operate more efficiently, accurately, and intelligently. 

About the Role 

We are hiring Applied Scientists in Palo Alto to define how important AI capabilities should work inside our products – and to turn those definitions into something Engineering can ship. 

Starting from a product need or system limitation, you will design a methodology, prototype it, evaluate its performance, and partner with Product and Engineering to incorporate the validated approach into the product. 

Research is an input, not the final output. You will use relevant academic and industry findings where useful, then translate them into practical methods that can be implemented, evaluated, and improved in real enterprise workflows. Translation into product is the core of this role. 

What You'll Do 

• Define methodologies for capabilities such as reasoning, planning, retrieval, grounding, verification, and learning from feedback. 

• Turn product needs and system limitations into clear methodological questions. 

• Evaluate relevant research and industry techniques, identifying what should be adopted, adapted, or rejected. 

• Design methodologies with explicit assumptions, workflows, decision rules, evaluation criteria, and known limitations. 

• Build prototypes (Python, Cloud – Azure preferred) and reference implementations. 

• Create evaluations that measure effectiveness, reliability, and failure modes. 

• Compare alternative approaches and make evidence-based recommendations. 

• Convert validated methodologies into specifications, reference code, evaluation assets, and acceptance criteria. 

• Partner with Product, Engineering, and domain experts through integration, testing, and refinement. 

Qualifications 

• You have taken an AI idea from concept to something real: a prototype, internal tool, or product feature. 

• You design methods with clear assumptions, failure modes, and evaluation criteria. 

• Practical Python and Cloud infrastructure experience and the ability to build working prototypes others can run and extend. 

• Experience designing or owning experiments, evaluations, or benchmarks that informed a ship/change/kill decision. 

• The ability to understand research findings and determine how they should – or should not – be applied in a product. 

• A solid understanding of machine learning and modern AI systems (for example language models, retrieval, agents, verification, or feedback loops). 

• Clear technical writing: you can turn a validated approach into a specification, reference implementation, and acceptance criteria. 

• Comfort working across Product, Engineering, and domain teams in a fast-moving environment. 

• Experience with language models, retrieval systems and memory, agents, or production AI evaluation. 

• A thesis-based master’s degree or PhD in computer science, artificial intelligence, machine learning, control theory, robotics, autonomous systems, or a related field. 

• A peer-reviewed publication record (helpful signal). 

What Success Looks Like 

Your work produces a clear methodology, a working prototype, credible evaluation evidence, and practical implementation guidance that can be integrated into the product, measured in production or realistic workflows, and improved over time. 

Benefits 

• Competitive Compensation: Tailored to your experience and skill set. 

• Flexible Work Arrangements: Hybrid working model for work-life balance. 

• Career Growth: Opportunities for professional development and leadership roles. 

• Innovative Culture: Work on transformative technologies and make an impact in the AI space.