MCP & Tools Python Developer - Agent Evaluation Infrastructure
Develop and maintain MCP-compatible evaluation servers, implement logic to check agent actions against scenario definitions, create or extend tools used by writers and QAs to test agents, work closely with infrastructure engineers to ensure compatibility, and occasionally assist with test writing or debug sessions.
Software Engineer
The AI Architect is responsible for translating AI research into product by working with client-side researchers on post-training, evaluations, safety, and alignment to build necessary primitives, data, and tooling. They partner closely with core customers and frontier research labs to solve technical problems related to model improvement, performance, and deployment. They shape and propose model improvements by translating objectives into clear, technically rigorous proposals and execution plans. The role involves leading end-to-end delivery including discovery, writing PRDs and technical specifications, prioritizing trade-offs, running experiments, shipping initial solutions, and scaling pilots into repeatable offerings. They manage complex, high-stakes engagements by running technical sessions with senior stakeholders, defining success metrics, identifying risks, and driving measurable outcomes. The role requires collaboration across teams such as research, platform, operations, security, and finance to deliver production-grade solutions. Additionally, the AI Architect builds evaluation rigor through designing robust evaluation frameworks, ensuring data quality, providing feedback loops, and sharing learnings to elevate technical execution across accounts.
Research Scientist
The Research Scientist will engage in hands-on technical work across all phases of research projects, from early exploration to developing working prototypes for real-world applications. They will take a leadership role in identifying promising AI applications relevant to the company's mission, formulate and advocate for research programs, and drive impactful initiatives. Collaboration with cross-functional teams to share research findings, advocate for their practical use, and guide productization efforts is required. Additionally, the role includes documenting experimental results, contributing to peer-reviewed publications, participating in patenting innovations, and mentoring junior researchers to help them develop their independent research and communication skills.
Research Lead, Conversational AI
Lead, mentor, and grow a team of research scientists and engineers working on spoken conversational AI research initiatives. Be deeply involved in hands-on technical work in all aspects of research projects from early exploration to working prototypes to achieve successful real-world applications. Identify promising applications of AI relevant to ASAPP's core mission, formulate and advocate for research programs in those areas, and take ownership of driving impactful research initiatives. Collaborate with cross-functional teams to socialize research findings, advocate for their application, and guide the productization of those findings to improve customer experience solutions. Document significant experimental results, contribute to publishing peer-reviewed papers, and participate in patenting innovations. Mentor and guide junior researchers to develop their skills in independent research and communication of findings.
Research Engineer
Research Engineers at ASAPP design, develop, and evaluate novel techniques in AI to advance the capabilities and impact of ASAPP’s products. They collaborate with product and engineering teams to ensure project success during the research phase and afterward. They build and maintain research datasets, infrastructure, and toolkits. Additionally, they collaborate with other functions at ASAPP to identify research opportunities.
Corporate Counsel
Debug and fix issues in the platform and ship pull requests with fixes. Build internal tools and copilots powered by generative AI to enhance the team. Rapidly prototype proof-of-concept projects for customer use cases. Collaborate across Engineering, Product, and Solutions teams to resolve customer challenges and advance AI adoption.
Kerry Care - Sr. Tech Lead Full Stack & AI
Own the technical architecture including backend, frontend, infrastructure, and AI integration. Write code daily to build critical systems. Make stack and design decisions shaping the product for years. Set standards for code quality, testing, and engineering discipline. Integrate large language models (LLMs) and AI workflows into the platform. Work directly with founders on product direction and the technical roadmap. Create clarity and structure in an environment lacking perfect specifications. Mentor and guide other developers technically.
First-Line Supervisors of Food Preparation and Serving Workers - AI Trainer (Contract)
The responsibilities include evaluating what AI models produce related to the field of food preparation and serving work, assessing content related to the field of work, delivering clear and structured feedback to improve the AI model's understanding of workplace tasks and language, developing prompts for AI models that reflect the field, and evaluating AI responses. The work is performed remotely and asynchronously with flexible hours, and involves leveraging professional experience in food preparation and serving supervision to train AI models.
AI Solutions Engineer (Raleigh)
Debug and fix issues in the platform and ship pull requests with fixes. Build internal tools and copilots powered by generative AI to enhance the team. Rapidly prototype proof-of-concepts for customer use cases. Collaborate across Engineering, Product, and Solutions teams to unblock customers and advance AI adoption.
Member of Technical Staff - ML Research Engineer; Multi-Modal - Audio
Invent and prototype new model architectures that optimize inference speed, including on edge devices; build and maintain evaluation suites for multimodal performance across a range of public and internal tasks; collaborate with the data and infrastructure teams to build scalable pipelines for ingesting and preprocessing large audio datasets; work with the infrastructure team to optimize model training across large-scale GPU clusters; contribute to publications, internal research documents, and thought leadership within the team and the broader ML community; collaborate with the applied research and business teams on client-specific use cases.
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