Role Description A self-motivated A-player who is results-oriented, operates at a fast pace, and takes pride in delivering high-quality, trustworthy AI systems; an experienced generative AI practitioner with deep expertise in LLM-based system design, including prompt engineering, RAG architectures, fine-tuning, and evaluation; highly focused on accuracy and reliability, with a clear understanding that hallucinations and misinformation erode user trust and equipped with concrete strategies to prevent them; proficient in building and maintaining end-to-end production ML pipelines, from data preparation through deployment and monitoring; and a strong collaborator who works effectively across engineering, product, and customer-facing teams in a fully remote environment. Essential Functions - Generative AI System Design & Development - Design, build, and continuously improve LLM-powered systems across Amira's product and operations — from internal tools to customer-facing features. - Own RAG pipelines end-to-end: document ingestion, chunking strategy, embedding selection, retrieval tuning, and response synthesis. - Develop and enforce guardrails, grounding strategies, and confidence thresholds to mitigate hallucination and ensure output reliability. - Architect prompt chains and agent workflows that are robust, maintainable, and cost-effective at scale. - Fine-Tuning, Evaluation & Continuous Improvement - Design and operate evaluation frameworks to measure system accuracy, helpfulness, hallucination rate, and task completion across generative AI features. - Fine-tune and adapt foundation models for domain-specific tasks, including data curation, training pipeline setup, and performance benchmarking. - Implement automated and human-in-the-loop review processes to catch and correct problematic outputs. - Monitor production traffic, identify failure modes, and iterate rapidly on retrieval, prompting, and generation strategies. - Integration & Infrastructure - Integrate LLM-powered features with internal systems and third-party platforms (e.g., Salesforce, CRM tools) via APIs, connectors, and data sync workflows. - Contribute to shared ML infrastructure and tooling used across Amira's AI systems. - Help explore and implement solutions that make generative AI economically viable within the budget constraints typical of public schools and education SaaS. - Cross-Functional Collaboration - Partner with learning design, content, product, and customer success teams to ensure AI systems are grounded in accurate, up-to-date domain knowledge. - Translate business needs into well-scoped generative AI solutions and communicate tradeoffs clearly to non-technical stakeholders. Qualifications - 2+ years of hands-on experience building and deploying LLM-based systems in production. - Deep familiarity with RAG architectures: embedding models, vector databases, retrieval strategies, and response grounding. - Demonstrated experience with evaluation and benchmarking of LLM outputs — including hallucination mitigation, confidence filtering, output validation, and fallback strategies. - Practical experience with prompt engineering, prompt chaining, and/or agent orchestration frameworks (LangChain, LlamaIndex, or similar). - Proficiency in Python and experience working with LLM APIs (open-source, Anthropic, OpenAI, etc.). - Experience building and maintaining ML or data pipelines in AWS or similar cloud infrastructure (Lambda, S3, RDS, etc.). - Degree in computer science or a related technical field, or equivalent practical experience. Preferred Qualifications - Experience fine-tuning foundation models or running RLHF / preference-based feedback loops for domain-specific improvement. - Experience in education SaaS or with education-sector customers (districts, schools, state agencies). - Familiarity with Salesforce or similar CRM platforms and their API/data ecosystems. - Experience with evaluation tooling, custom eval harnesses, or LLM-as-judge approaches. - Background working with conversational AI, chatbots, or customer-facing generative AI features. - Proven ability to operate in a fast-paced, goal-oriented startup environment and manage multiple concurrent workstreams. 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.

Estamos buscando un/a Ingeniero/a de Software Backend motivado/a y entusiasta para unirse a nuestra empresa fintech de rápido crecimiento en América Latina. Como Backend Engineer, desempeñarás un papel clave en la entrega de proyectos de software resilientes y sostenibles, desde el diseño hasta la implementación y despliegue. Contribuirás al desarrollo y mantenimiento de productos de software escalables y apoyarás su integración con servicios en la nube. Tendrás exposición a sistemas distribuidos, arquitecturas orientadas a eventos (EDA), bases de datos NoSQL y principios de diseño guiado por dominio (DDD). Este rol individual ofrece la oportunidad de aprender y aplicar mejores prácticas en desarrollo de software, pruebas unitarias, despliegue y monitoreo en un entorno productivo. Trabajarás bajo la guía de ingenieros senior, pero se espera que tomes iniciativa, actúes con urgencia y entregues trabajo de alta calidad que agregue valor al negocio y a los clientes. Responsabilidades clave: Entrega de software resiliente y sostenible: Diseñar, desarrollar e implementar soluciones backend de alta calidad y escalables desde la concepción hasta el despliegue. Ejecución de proyectos definidos: Trabajar en proyectos establecidos para lograr los objetivos del equipo, definiendo soluciones adecuadas de forma independiente o utilizando enfoques existentes para resolver problemas. Sentido de responsabilidad (ownership): Cumplir compromisos, asumir responsabilidad por tu trabajo y entregar a tiempo. Identificar proactivamente oportunidades de mejora en proyectos y procesos del equipo. Colaboración en equipo: Trabajar principalmente dentro del equipo, participando en revisiones de código y discusiones para mejorar la calidad del código. Adoptar las mejores prácticas establecidas por el equipo. Pruebas unitarias y mejores prácticas: Escribir pruebas unitarias para nuevas funcionalidades, asegurando confiabilidad y mantenibilidad. Aplicar mejores prácticas de desarrollo. Participación en procesos ágiles: Participar en sesiones de planificación, reuniones diarias (stand-ups) y retrospectivas con el equipo de desarrollo, el engineering manager y el product manager. Desarrollo y soporte de APIs: Diseñar e implementar APIs que serán consumidas por otros equipos, asegurando que sean robustas y escalables. Integración en la nube: Implementar e integrar soluciones en la nube, preferiblemente en AWS, utilizando arquitecturas serverless. Trabajo con sistemas distribuidos y EDA: Apoyar en la construcción de sistemas distribuidos escalables y resilientes utilizando patrones de arquitectura orientada a eventos. Uso de bases de datos NoSQL: Diseñar, implementar y gestionar soluciones de almacenamiento de datos utilizando bases de datos NoSQL como DynamoDB. Aplicación de DDD: Aplicar principios de diseño guiado por dominio para modelar dominios de negocio complejos de manera efectiva. Mentoría: Posiblemente brindar mentoría a nuevos ingresos, pasantes o ingenieros más junior. Mejora continua: Buscar retroalimentación de forma proactiva, enfocarse en el crecimiento personal y contribuir al desarrollo del equipo. Innovar aportando nuevas ideas y enfoques. Requisitos: Formación académica: Licenciatura en Ciencias de la Computación, Ingeniería o campo relacionado, o experiencia práctica equivalente. Experiencia: Más de 3 años de experiencia en desarrollo de software. Habilidades técnicas: - Dominio de Python. - Sólido entendimiento de APIs REST y métodos HTTP. - Experiencia con herramientas serverless de AWS como DynamoDB, Lambda, CloudWatch, API Gateway, y familiaridad con frameworks de Infraestructura como Código (IaC) como CDK o SAM. - Experiencia en pruebas unitarias y automatización de pruebas. - Conocimiento de herramientas y procesos CI/CD. Sistemas distribuidos: Comprensión de principios de computación distribuida y experiencia construyendo sistemas escalables. Arquitectura orientada a eventos: Experiencia con modelos de programación orientados a eventos y herramientas como AWS SNS/SQS, EventBridge, Kafka o similares. Bases de datos NoSQL: Dominio de bases de datos NoSQL como DynamoDB o MongoDB. Domain-Driven Design: Experiencia aplicando conceptos de DDD en proyectos. Metodologías ágiles: Familiaridad con Scrum o Kanban y capacidad de trabajar en procesos ágiles. Requisitos: Formación académica: Licenciatura en Ciencias de la Computación, Ingeniería o campo relacionado, o experiencia práctica equivalente. Experiencia: Más de 3 años de experiencia en desarrollo de software. Habilidades técnicas: - Dominio de Python. - Sólido entendimiento de APIs REST y métodos HTTP. - Experiencia con herramientas serverless de AWS como DynamoDB, Lambda, CloudWatch, API Gateway, y familiaridad con frameworks de Infraestructura como Código (IaC) como CDK o SAM. - Experiencia en pruebas unitarias y automatización de pruebas. - Conocimiento de herramientas y procesos CI/CD. Sistemas distribuidos: Comprensión de principios de computación distribuida y experiencia construyendo sistemas escalables. Arquitectura orientada a eventos: Experiencia con modelos de programación orientados a eventos y herramientas como AWS SNS/SQS, EventBridge, Kafka o similares. Bases de datos NoSQL: Dominio de bases de datos NoSQL como DynamoDB o MongoDB. Domain-Driven Design: Experiencia aplicando conceptos de DDD en proyectos. Metodologías ágiles: Familiaridad con Scrum o Kanban y capacidad de trabajar en procesos ágiles. Calificaciones deseables: - Experiencia con frameworks y librerías backend. - Conocimiento en ciencia de datos (deseable). - Conocimiento de arquitecturas serverless y computación en la nube. - Familiaridad con sistemas de control de versiones como Git. - Experiencia con herramientas y prácticas de revisión de código. - Participación en proyectos con sistemas distribuidos. - Experiencia práctica con sistemas orientados a eventos como AWS SNS/SQS, Kafka o similares. - Experiencia con bases de datos NoSQL en proyectos reales. - Aplicación práctica de principios DDD. - Participación en iniciativas para reducir deuda técnica y mejorar la eficiencia operativa.

Sr. Distinguished Machine Learning Engineer (Remote-Eligible) Overview: At Capital One, we are creating responsible and reliable AI systems, changing banking for good. For years, Capital One has been an industry leader in using machine learning to create real-time, personalized customer experiences. Our investments in technology infrastructure and world-class talent - along with our deep experience in machine learning - position us to be at the forefront of enterprises leveraging AI. From informing customers about unusual charges to answering their questions in real time, our applications of AI & ML are bringing humanity and simplicity to banking. We are committed to continuing to build world-class applied science and engineering teams to deliver our industry leading capabilities with breakthrough product experiences and scalable, high-performance AI infrastructure. At Capital One, you will help bring the transformative power of emerging AI capabilities to reimagine how we serve our customers and businesses who have come to love the products and services we build. Team Description: The Consumer Engagement Platform organization at Capital One empowers rapid financial product innovation at scale and delivers developer joy, for all Capital One's consumer products and organizations, by providing well-managed, self-service, experimentation-driven, and personalized product development vehicles. Hyper Personalization org is building the intelligence and infrastructure that will enable Capital One to deliver truly individualized, real-time customer experiences at scale - turning every channel into a context-aware decisioning surface, from home feeds to marketing and servicing messages. The org's mission is to move Capital One to deliver always-on, cohort-of-one personalization, powered by resilient data foundations, production-grade ML and GenAI systems, and low-latency application platforms that make it easy for teams across the company to experiment, innovate, and serve the right experience to every customer at the right moment. What you'll do in the role: - Define and drive technical strategy and roadmap for our Personalization Platform that powers real-time, personalized product experiences and multi-channel targeted user messaging across all Capital One products and services. - Partner cross-functionally with Product, Data science, Cloud infrastructure, and Machine learning platform teams to align on and co-develop the advanced recommendation systems and algorithms serving our Capital One users. - Develop and maintain a flexible, scalable rules engine to enable business-driven personalization logic, allowing dynamic configuration of user segmentation, targeting rules, and real-time decisioning while integrating seamlessly with ML-driven recommendations. - Design, build and maintain robust ML infrastructure and pipelines to support end-to-end workflows including feature extraction, model training, testing, guardrails, model evaluation, deployment, and both real-time and batch inference - ensuring high performance, scalability, and reliability. - Architect low-latency, event-driven systems for enabling real-time dynamic personalization and decisioning based on streaming data, user behavior, and contextual signals. - Drive the evolution of MLOps practices by building automated metrics-backed deployment workflows, integration validation and testing systems, and scalable monitoring & observability. - Invent and introduce state-of-the-art LLM optimization techniques to improve the performance - scalability, cost, latency, throughput - of large scale production AI systems. - Leverage a broad stack of Open Source and SaaS AI technologies such as AWS Ultraclusters, Huggingface, VectorDBs, Nemo Guardrails, PyTorch, and more. - Provide organizational technical leadership to influence architecture, engineering standards, cross-team strategies, mentoring engineers and driving organization wide platform innovation. The Ideal Candidate: - You love to build systems, take pride in the quality of your work, and also share our passion to do the right thing. You want to work on problems that will help change banking for good. - Passion for staying abreast of the latest research, and an ability to intuitively understand scientific publications and judiciously apply novel techniques in production. - You adapt quickly and thrive on bringing clarity to big, undefined problems. You love asking questions and digging deep to uncover the root of problems and can articulate your findings concisely with clarity. You have the courage to share new ideas even when they are unproven. - You are deeply Technical. You possess a strong foundation in engineering and mathematics, and your expertise in hardware, software, and AI enable you to see and exploit optimization opportunities that others miss. - You are a resilient trail blazer who can forge new paths to achieve business goals when the route is unknown. Capital One is open to hiring a Remote Employee for this opportunity Basic Qualifications: - Bachelor's degree - At least 10 years of experience designing and building data-intensive solutions using distributed computing - At least 7 years of experience programming in C, C++, Python, or Scala - At least 4 years of experience with the full ML development lifecycle using modern technology in a business critical setting Preferred Qualifications: - 8+ years of experience deploying scalable, responsible AI solutions on major cloud platforms (AWS, GCP, Azure); Master's or PhD in Computer Science or a relevant technical field. - 5+ years of proven expertise in designing, implementing and scaling personalization platform and recommendation systems serving one or more areas of Feed Personalization/Ads Ranking/Targeted Marketing Messaging. - 5+ years of strong proficiency in Python, Java, C++, or Golang; hands-on experience with ML frameworks (PyTorch, TensorFlow) and orchestration tools (Databricks, Airflow, Kubeflow). - 5+ years of experience developing and applying state-of-the-art techniques for optimizing training and inference systems to improve hardware utilization, latency, throughput, and cost. - 5+ years of deep expertise in cloud-native engineering, containerization (Docker, Kubernetes), and automated CI/CD deployment. - Passion for staying on top of the latest AI research and AI systems, and judiciously apply novel techniques in production - Excellent communication and presentation skills, with the ability to articulate complex AI concepts to peers - Proven leadership in driving platform strategy, fostering cross-functional collaboration, and influencing technical direction across the company. Capital One will consider sponsoring a new qualified applicant for employment authorization for this position. The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. Remote (Regardless of Location): $286,200 - $326,700 for Sr Distinguished Machine Learning Engineer McLean, VA: $314,800 - $359,300 for Sr Distinguished Machine Learning Engineer Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days. No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections 4901-4920; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1-800-304-9102 or via email at RecruitingAccommodation@capitalone.com . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Careers@capitalone.com Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).

Duration: Six months Format: Full time (40 hrs/week), paid In the last year at Loka, our teams launched almost 200 GenAI projects for companies of all kinds, including the world’s Number 1 GenAI reading tutor, a startup that transforms homes into batteries and a leading cancer-fighting laboratory. And we did it all while enjoying every other Friday off 😎 As a Data - Machine Learning Intern, you'll gain professional experience supporting Loka’s certified specialists, technical experts and PhDs while elevating your skillset, building a portfolio and launching projects you’re proud of. The Role - Assist in designing, developing and maintaining data pipelines to ensure clean, reliable and timely data. - Collaborate with the team to implement and optimize ETL processes. - Integrate data from various sources into warehouses, data lakes and lakehouses. - Support data management tasks, including data cleaning, validation and transformation. - Understand business objectives and develop models that help achieve them, plus metrics to track their progress. - Implement ML systems using classical ML, DL and Foundation Models following best practices. - Participate in client communications by helping gather requirements and communicate deliverables. - Explore and visualize data with a careful eye for issues that require data cleaning as well as differences in data distribution that may affect performance after deployment. - Identify and analyze model errors. Required Hard Skills - Last year of a bachelor’s degree in Computer Science or related - Proficient in English - Basic knowledge of Python, ML, and Data libraries - Basic knowledge of Databases - Understanding of statistical, ML and deep learning algorithms - Experience visualizing and manipulating big datasets - Problem solving - Bonus: AWS knowledge, (Py)Spark, Airflow, Data Lakes and Data Warehouses Required Soft Skills - Curiosity: You’re ambitious to learn and grow in different industries utilizing a modern tech stack. - Autonomy and positivity: We’re a fully remote, globally distributed team. - Teamwork: Enjoy a collaborative approach. - Adaptability: Operate with a startup mindset and move at a startup pace. - Dependable: You can be trusted to deliver high-quality work. Benefits - Every other Friday off - Health Bonus - Remote and flexible - Paid sick days and local holidays Please submit your CV in English.