Please submit your CV in English and indicate your level of English proficiency. Mindrift connects specialists with project-based AI opportunities for leading tech companies, focused on testing, evaluating, and improving AI systems. Participation is project-based, not permanent employment. What this opportunity involves While each project involves unique tasks, contributors may: - Design graduate- and industry-level automotive engineering problems grounded in real practice; - Evaluate AI-generated solutions for correctness, assumptions, and engineering logic; - Validate analytical or numerical results using Python (NumPy, SciPy, Pandas); - Improve AI reasoning to align with first principles and accepted engineering standards; - Apply structured scoring criteria to assess multi-step problem solving. What we look for This opportunity is a good fit for automotive engineers with an experience in python open to part-time, non-permanent projects. Ideally, contributors will have:  - Degree in Automotive Engineering or related fields, e.g. Mechatronics, Manufacturing Engineering, Mechanical Engineering, Aerospace Engineering, etc. - 3+ years of professional automotive engineering experience - Strong written English (C1/C2) - Strong Python proficiency for numerical validation - Stable internet connection  Professional certifications (e.g., PE, CEng, PMP) and experience in international or applied projects are an advantage. How it works Apply → Pass qualification(s) → Join a project → Complete tasks → Get paid Project time expectations For this project, tasks are estimated to require around 10–20 hours per week during active phases, based on project requirements. This is an estimate, not a guaranteed workload, and applies only while the project is active. Compensation On this project, contributors can earn up to $55 per hour equivalent, depending on their level and pace of contribution. Compensation varies across projects depending on scope, complexity, and required expertise. Please note that other projects on the platform may offer different earning levels based on their requirements.

Please submit your CV in English and indicate your level of English proficiency. Mindrift connects specialists with project-based AI opportunities for leading tech companies, focused on testing, evaluating, and improving AI systems. Participation is project-based, not permanent employment. What this opportunity involves While each project involves unique tasks, contributors may: - Design graduate- and industry-level automotive engineering problems grounded in real practice; - Evaluate AI-generated solutions for correctness, assumptions, and engineering logic; - Validate analytical or numerical results using Python (NumPy, SciPy, Pandas); - Improve AI reasoning to align with first principles and accepted engineering standards; - Apply structured scoring criteria to assess multi-step problem solving. What we look for This opportunity is a good fit for automotive engineers with an experience in python open to part-time, non-permanent projects. Ideally, contributors will have:  - Degree in Automotive Engineering or related fields, e.g. Mechatronics, Manufacturing Engineering, Mechanical Engineering, Aerospace Engineering, etc. - 3+ years of professional automotive engineering experience - Strong written English (C1/C2) - Strong Python proficiency for numerical validation - Stable internet connection  Professional certifications (e.g., PE, CEng, PMP) and experience in international or applied projects are an advantage. How it works Apply → Pass qualification(s) → Join a project → Complete tasks → Get paid Project time expectations For this project, tasks are estimated to require around 10–20 hours per week during active phases, based on project requirements. This is an estimate, not a guaranteed workload, and applies only while the project is active. Compensation On this project, contributors can earn up to $55 per hour equivalent, depending on their level and pace of contribution. Compensation varies across projects depending on scope, complexity, and required expertise. Please note that other projects on the platform may offer different earning levels based on their requirements.

• You'll lead the technical direction of the AI agents that turn natural language into production-ready applications. • This means shaping how we work with LLMs to solve our hardest problems: maintaining context across large codebases, orchestrating multi-step workflows that feel intuitive, and handling everything from simple UI tweaks to complex architectural decisions. • You'll define the patterns and systems that govern how AI reasons about and generates full-stack applications, driving initiatives that span multiple teams and influence our broader AI strategy. • Your work shapes the experience of millions of users building real products with Bolt every day. • Lead the design and evolution of our AI agent systems, establishing patterns, frameworks, and standards that teams across the organization adopt. • Shape our approach to leveraging models from providers such as OpenAI (GPT series), Anthropic (Claude), and Google (Gemini). • Build relationships with provider teams to influence roadmaps and beta-test new capabilities. • Design the foundational systems that enable AI agents to call external tools and APIs safely and effectively. • Partner with teams across engineering, product, and design to align AI initiatives with business objectives. • Mentor senior and mid-level engineers, raising the bar for AI engineering practices across the organization. • Define the methodology for collecting, curating, and analyzing datasets from agent responses and multi-turn conversations. • Stay at the forefront of NLP and LLM research, identifying and championing novel techniques that provide competitive advantage.

• Design and implement multi-step AI agents and orchestration workflows in Python • Build using LangGraph, LangChain, n8n, AWS Bedrock, and similar tools • Integrate and evaluate top models (Gemini, GPT, Claude, etc.) • Connect agents to real systems (APIs, SaaS apps, webhooks, queues, events) • Implement memory, retrieval, and planning for reliable agent behavior • Take prototypes to production — with CI/CD, containers, and cloud deployment • Build internal SDKs and reusable agent templates to speed up delivery • Instrument everything — logging, tracing, guardrails, evaluation • Work directly with clients to capture requirements, run experiments, and iterate fast • Apply best practices for security, privacy, and safety in all deployments