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

We are building the next generation of AI agents capable of navigating the complex consulting tasks. To achieve this, we are looking for consultants with 5+ years of experience at McKinsey & Company, Boston Consulting Group, or Bain & Company. The role is not client-facing and does not involve stakeholder management. Instead, the consultant will use their direct experience from real consulting projects to: - Recreate realistic consulting project environments - Design structured problem-solving tasks - Define what high-quality outputs should look like - Evaluate AI-generated outputs against consulting standards Why an MBB Background Matters Practical knowledge of how consulting projects actually work in detail is required. Specifically: - How ambiguous client questions are translated into structured workplans - How hypotheses are formed and tested - How data gaps are handled in real projects - What “good” looks like in slide decks, models, and executive summaries - How trade-offs and recommendations are developed under uncertainty The AI must be trained on environments that reflect real consulting complexity. Only someone who has worked on multiple MBB projects can reliably recreate this level of realism. While we value all analytical backgrounds, this role specifically requires first-hand MBB project experience. What You’ll Do Design realistic consulting environments - Create detailed project scenarios (industry context, financials, constraints, incomplete information) - Develop structured tasks such as market sizing, due diligence, cost reduction, growth strategy, or operational diagnosis Define what “good” looks like - Set clear evaluation criteria for high-quality consulting outputs - Distinguish between superficial answers and consulting-grade reasoning Evaluate AI outputs - Review analyses and identify logical errors, weak assumptions, and structural gaps - Provide precise, actionable feedback to improve system performance Continuously improve training quality - Refine scenarios, edge cases, and evaluation frameworks - Increase realism based on real project experience Skills & Requirements - 5+ years at McKinsey, BCG, or Bain - Strong structured problem-solving skills - Ability to translate vague problems into clear analytical steps - High attention to logical consistency - Independent working style Basic technical familiarity required: - Reading structured formats (e.g., JSON or YAML) - Basic Git/GitHub usage - Clear written English (B2+) What This Role Is Not - No client interaction - No stakeholder management - No sales or business development - No team leadership responsibilities This is a focused analytical role for consultants who want to apply their problem-solving skills to advanced AI systems.