Senior Applied AI Researcher (Brazil)
Own and orchestrate end-to-end research programs using massively parallel agentic AI, including problem formulation, designing agent-driven experiment campaigns to explore model architectures, training regimes, data strategies, and evaluation criteria. Drive breakthrough domain-specific model quality through multi-stage training pipelines, domain adaptation, RL-based optimization, and training dynamics analysis, executing exhaustive ablations, hyperparameter sweeps, and failure-mode investigations in parallel. Design and train multimodal systems, knowledge graph pipelines, hybrid retrieval architectures, and structured reasoning systems, delegating exploration and prototyping across these areas. Architect agentic data and training infrastructure for domain-specific data curation, quality filtering, preprocessing, and large-scale training. Mentor AI Researchers in agentic paradigms to enhance their research capabilities. Compress the research-to-production cycle by developing production-ready systems with agentic CI/CD, automated testing, and continuous evaluation while collaborating with engineering, product, and domain experts. Build knowledge systems to document findings, publish at top-tier venues, and contribute to internal knowledge infrastructure. Continuously identify and solve bottlenecks in workflows to maximize human potential as a research output.
Applied AI Researcher (Brazil)
Architect and orchestrate massively parallel AI research workflows by designing experiments that utilize fleets of agentic AI systems to explore hypothesis spaces, hyperparameter landscapes, and architectural variations at large scale and speed. Design, train, and iterate on models across the full GenAI stack including LLMs, VLMs, embedding models, rerankers, and reward models using agentic pipelines that autonomously manage data preprocessing, training runs, evaluation sweeps, and result synthesis. Conduct rigorous, first-principles research into model architectures, training dynamics, reinforcement learning, and knowledge representation, using AI agents to accelerate literature reviews, ablation studies, and mathematical analysis. Work across disciplines and modalities such as NLP, computer vision, multimodal understanding, agentic reasoning, and domain science by delegating exploration, prototyping, and benchmarking to parallel agent systems to synthesize insights across fields simultaneously. Build and contribute to shared tooling, libraries, and platforms for orchestrating autonomous experiment pipelines, data processing workflows, and evaluation harnesses at scale. Collaborate with engineering, product, and domain experts to rapidly integrate research breakthroughs into production platforms using agentic CI/CD and automated integration testing to compress the research-to-deployment cycle. Document findings, publish at top-tier venues, and develop internal knowledge systems that agentic tools can index and reason over to amplify collective intelligence. Identify and address workflow bottlenecks for oneself and the team by designing or adopting efficient, scalable solutions, treating personal augmentation as a core research output.
Optical Engineer - Freelance AI Trainer
Contributors may design original optics problems that simulate real physics research workflows; ensure problems are computationally intensive and cannot be solved manually within reasonable timeframes; develop problems that require non-trivial reasoning in mechanics, electromagnetism, thermodynamics, and quantum mechanics; base problems on real research challenges or practical applications from optics and physics practice; document problem statements clearly and provide verified correct answers.
Senior ML Operations (MLOps) Engineer
The Senior ML Operations (MLOps) Engineer at Eight Sleep is responsible for introducing and implementing cutting-edge ML technologies, owning the design and operation of robust ML infrastructure including scalable data, model, and deployment pipelines to ensure reliable model delivery to production. They collaborate cross-functionally with R&D, firmware, data, and backend teams to ensure reliable and scalable ML inference on Pods. They optimize ML systems for cost, scalability, and performance across training and inference, and develop tooling, microservices, and frameworks to streamline data processing, experimentation, and deployment. The role requires effective communication in a remote work environment.
Safety Engineer
The AI Safety Engineer is responsible for designing and building scalable backend infrastructure for content moderation, abuse detection, and agents guardrails by deploying AI/ML models into production systems. They will architect robust APIs, data pipelines, and service architectures to support real-time and batch moderation workflows. The role includes implementing comprehensive monitoring, alerting, and observability systems, establishing SLIs, SLOs, and performance benchmarks. The engineer will collaborate with ML engineers to translate research models into production-ready systems and integrate them across the product suite. Additionally, they will drive technical decisions and contribute to the vision for the safety roadmap to build next-generation platform guardrails for scale and precision.
Senior AI Engineer - San Mateo, CA
The role involves training, evaluating, and monitoring new and improved LLMs and other algorithmic models. The engineer will test and deploy content moderation models in production and iterate based on real-world performance metrics and feedback loops. They are expected to develop medium to long-term vision for content understanding-related R&D, collaborating with management, product, policy & operations, and engineering teams. The position requires taking ownership of results delivered to customers, advocating for changes in approach where needed, and leading cross-functional execution.
Customer Success Solution Architect (Brazil)
The Solution Architect is responsible for developing detailed and scalable architectural designs to address client needs using Articul8 products and third-party libraries and tools. They run pilot programs with customers to demonstrate the feasibility and advantages of proposed solutions, including introducing new product features or building prototypes. The role requires working closely with clients to understand business challenges and technical requirements through workshops, meetings, and presentations. They optimize solutions for performance, reliability, and cost-effectiveness, selecting appropriate instance types, auto-scaling configurations, and storage options. Ensuring solutions comply with security best practices and regulatory requirements is necessary, including implementing identity and access management, data encryption, and other security measures. The architect also creates comprehensive documentation and provides training on solution implementation and management. Collaboration with cross-functional teams such as Applied Research, Engineering, Quality Assurance, and Customer Success is required to incorporate innovation and maintain product leadership. Additionally, the role involves mentoring and guiding junior team members and helping to build a culture of rapid innovation.
Backend Engineer - (Python) Brazil
Design, develop, test, deploy, maintain, and improve scalable, secure, and high-performance backend systems with a focus on high availability, low latency, and cost-effectiveness. Serve as the subject matter expert in infrastructure for designing new products and introducing new technology to existing product lines. Collaborate closely with engineering and research teams to integrate infrastructure components with product features, ensuring optimal system performance and user experience. Design event-driven architectures and develop APIs and microservices to support real-time processing and analytics. Ensure system reliability, performance, and scalability through monitoring, logging, and error handling mechanisms. Stay updated with emerging trends, technologies, and methodologies to enhance infrastructure capabilities. Participate in code reviews, contribute to open-source projects, and mentor junior engineers.
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.
Finance Platform Engineer
Use proprietary software applications to provide input and labels on defined projects. Support and ensure the delivery of high-quality curated data. Contribute to the training of new tasks by working closely with the technical staff to develop and implement cutting-edge initiatives and technologies. Interact with technical staff to improve the design of efficient annotation tools. Choose problems from economics fields that align with expertise, focusing on macroeconomics, microeconomics, and behavioral economics. Regularly interpret, analyze, and execute tasks based on given instructions. Provide services including labeling and annotating data in text, voice, and video formats to support AI model training, sometimes involving recording audio or video sessions.
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