• Research, design and build high-fidelity proof-of-concepts for upcoming features • Serve as a bridge to the Product Engineering team, providing cryptographic guidance, code reviews, and troubleshooting for complex protocol-level issues • Uphold rigorous security standards and best-practice cryptographic principles before they transition to production • Own and maintain key R&D initiatives and drive community contributions • Work closely with clients and partners, helping them to navigate and implement post-quantum migrations • Monitor the evolving blockchain and post-quantum cryptography landscape, identifying emerging threats or new primitives for adoption • Translate complex concepts into digestible technical blog posts, documentation, and whitepapers solidifying P11's thought leadership in the space

This role is for one of our clients Compensation: $30-$80 per hour Experienced psychologists are invited to contribute to a high-impact AI research collaboration with a leading artificial intelligence research organization. This project focuses on evaluating and enhancing AI system performance across psychology-related domains, including clinical, cognitive, developmental, and social psychology. This opportunity is well-suited for professionals with strong academic training and practical expertise who can critically assess reasoning quality, conceptual depth, and ethical considerations in AI-generated content.

About Us Sequen AI is leading the charge for building frontier ranking models for search and recommendations. Sequen AI's technology specializes in designing end-user behavior for large consumer enterprises. The Role We’re looking for a Senior Research Engineer with expertise in search and recommendation modeling to help us design, develop, and scale innovative ranking, embedding and retrieval models that power our personalized discovery platform. In this role, you’ll have the opportunity to work on impactful projects, from enhancing recommendation ranking systems to building search retrieval models, all while contributing to the underlying data pipelines and infrastructure. This is an ideal role for someone eager to grow their skills, take ownership of complex systems, and be part of an agile, fast-paced environment. What You'll Do Develop & Optimize Ranking Models : Build, train, and deploy advanced search and recommendation models using Deep Learning techniques. Data Engineering : Design and implement data pipelines to process large-scale datasets, ensuring models are fed with high-quality, reliable data. Infrastructure : Work with tools like Kubernetes, Airflow, and AWS to manage scalable model deployment and experimentation environments. What We’re Looking For Experience : 5+ years of experience as an ML Engineer or similar role, with a focus on deep learning for search, recommendation, or related applications. Technical Expertise : Proficient in PyTorch, Deep Learning, and Spark, with a strong foundation in machine learning theory and algorithms. Data Engineering Skills : Hands-on experience with data pipelines and processing large-scale datasets. You'll design and optimize data pipelines using tools like Spark, Airflow, and dbt across GCP and AWS. Infrastructure Knowledge : Some familiarity with tools such as Kubernetes, Airflow, Terraform and cloud platforms like AWS for deployment and scalability. Problem Solver : Strong analytical and critical thinking skills, with a passion for tackling open-ended challenges. What We Offer A collaborative and innovative work environment at the forefront of ML and AI. Opportunities to make a direct impact on product and technology direction. Annual Salary : $280,000—$350,000 USD (based on experience) + Equity Health insurance, unlimited time off, awesome team If you’re excited about building next-generation search and recommendation systems and growing your expertise in a dynamic startup, we’d love to hear from you!

• Lead or co-lead technically complex ML engineering projects from initial customer discussions through implementation and delivery • Design, build, and improve LLM training and post-training pipelines, including data ingestion, preprocessing, fine-tuning, evaluation, and experiment tracking • Implement and optimize evaluation systems for LLMs and multimodal models, including offline benchmarks and task-specific test harnesses • Integrate human-in-the-loop and AI-augmented evaluation signals into model development workflows • Build robust infrastructure and tooling for reproducible experimentation, metrics logging, and regression monitoring • Diagnose model behavior and pipeline failures, including data issues, training instability, metric inconsistencies, and evaluation drift • Collaborate with Language Data Scientists and Applied Research Scientists to translate evaluation frameworks into executable systems • Work closely with customer technical stakeholders to understand goals, constraints, and success criteria; propose and implement technically sound solutions • Contribute to internal research and platform development, including benchmark frameworks, evaluation tooling, and post-training workflow improvements • Contribute to best practices and standards for LLM training, evaluation, and quality assurance across projects • Mentor junior engineers and contribute to technical design reviews, documentation, and engineering rigor across the team