• Own end-to-end integration of ML models into Camera Raw and related Adobe imaging products, from prototype to production. • Partner with ML researchers to evaluate new models for product readiness, including accuracy, latency, memory, and platform constraints. • Design and maintain scalable on-device AI inference pipelines, including model packaging, versioning, artifact management, and runtime integration. • Define integration practices and patterns that improve consistency, quality, and engineering velocity across the team. • Lead code reviews and provide technical direction on ML integration, performance, and long-term maintainability. • Resolve complex integration issues that span ML systems and production C++ codebases. • Mentor engineers and share expertise through design reviews and knowledge transfer sessions. • Collaborate with product, engineering, XD and research teams to align on model needs and integration priorities.

Role Description Design, deploy, and maintain automated educational evaluation systems leveraging speech recognition, natural language processing (NLP), and large language models (LLMs) to analyze and assess real-time user performance and feedback. - Integrate, fine-tune, and optimize third-party AI services, open-weight LLMs, and proprietary machine learning models to power conversational and adaptive learning experiences tailored to individual user proficiency. - Architect and build end-to-end machine learning pipelines encompassing data extraction, semantic analysis, multi-label classification, and predictive modeling for real-time educational content delivery. - Design, develop, and maintain AI/ML systems purpose-built for real-time, low-latency educational platforms serving concurrent users at scale. - Architect scalable Python backend services deployed on cloud infrastructure, engineered specifically for sub-second inference in production AI applications. - Conduct rigorous model validation, bias analysis, and performance optimization to ensure accuracy, scalability, and reliability of machine learning systems across diverse user populations. - Implement cloud-native data engineering solutions utilizing distributed systems and databases to process, transform, and serve large-scale educational datasets for model training and real-time inference. - Collaborate with cross-functional engineering, data science, and product teams to translate complex business requirements into robust, production-grade AI architectures. - Author technical design documents, algorithm specifications, and architecture decision records; communicate progress, risks, and trade-offs to technical and non-technical stakeholders. - Fully remote; work from any U.S. location (no relocation required). Qualifications - Master’s degree in Computer Science or related field. - 3+ years in ML engineering, AI systems development, or software engineering related occupation, in a startup environment. - Qualifying experience must include: - Designing, developing, and deploying machine learning and artificial intelligence systems for real-time, production-grade educational or adaptive learning platforms, including speech recognition, natural language processing, and large language model integration. - Architecting scalable Python backend services optimized for low-latency, high-throughput AI inference serving concurrent users on cloud infrastructure. - Applying machine learning techniques — including semantic analysis, multi-label classification, and predictive modeling — to build end-to-end automated evaluation and feedback systems. - Engineering large-scale distributed data systems and cloud-native pipelines for processing, transforming, and serving high-volume datasets for model training and real-time inference. - Conducting model validation, bias analysis, and performance optimization to ensure accuracy, scalability, and reliability across diverse user populations. - Programming in Python, including development of production ML pipelines, API services, and integration of third-party AI services with proprietary models. Benefits - Competitive Salary - Medical, dental, and vision benefits - 401(k) with company matching - Flexible time off - Stock option ownership - Cutting-edge work - The opportunity to help children around the world reach their full potential Commitment to Diversity Amira Learning serves a diverse group of students and educators across the United States and internationally. We believe every student should have access to a high-quality education and that it takes a diverse group of people with a wide range of experiences to develop and deliver a product that meets that goal. We are proud to be an equal opportunity employer.

• Design and iterate on our VLA model architecture—including the VLM backbone, action decoder, and multimodal fusion pipeline • Build and optimize large-scale training infrastructure (distributed training, data pipelines, mixed-precision, efficient fine-tuning) • Develop simulation-based evaluation and closed-loop training workflows using photorealistic neural rendering • Curate and manage multimodal training datasets spanning real-world driving and synthetic scenarios • Translate state-of-the-art research (diffusion/flow-matching action heads, reasoning-augmented VLAs, world models) into production-grade systems • Collaborate directly with vehicle systems and controls engineers to integrate model outputs into a real-time autonomous driving stack

• Design, build, and deploy scalable machine learning models for OCR, document understanding, and voice processing • Research and implement algorithms for extracting structured information from unstructured documents and audio • Optimize and fine-tune models for real-world performance and reliability • Work alongside product, data, and engineering teams to integrate ML solutions into production workflows • Analyze model outputs, perform error analysis, and iterate quickly based on results and feedback • Document methodologies, experiments, and best practices