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 We're building a dataset to evaluate AI coding agents — how well a model handles real-world developer tasks. You'll create challenging tasks and evaluation criteria within realistic simulated environments: - Build virtual companies following a high-level plan - codebase, infrastructure, and context (conversations, documentation, tickets) that form a realistic environment with development history - Assemble and calibrate tasks from intermediate states of the virtual company: craft the prompt, define evaluation criteria, and ensure the task is solvable and the evaluation is fair - Design tasks set in isolated environments - emulations of a developer's workstation: a Linux machine with development tools (terminal, CLI), MCP servers (repository, task tracker, messenger, documentation, etc.), and a real web application codebase - Write tests that accept all correct solutions and reject incorrect ones - neither too strict (breaking on valid approaches) nor too lenient (passing bad ones) - Iterate with an AI agent on tests - verifying they catch real problems, don't miss bad solutions, and don't break on good ones - Review code written by agents, analyze why an agent failed or succeeded, and design edge cases and adversarial scenarios - Iterate based on feedback from expert QA reviewers who score your work on quality criteria What this is NOT - Not data labeling - Not prompt engineering - Not writing code from scratch - the agent writes most of the code; you guide and evaluate A significant part of the work is done together with AI - it's very hard to create tasks that challenge frontier models without using frontier models. What we look for This opportunity is a good fit for experienced developers, software engineers, and/or test automation specialists open to part-time, non-permanent projects. Ideally, contributors will have: - Degree in Computer Science, Software Engineering, or related fields - 5+ years in software development, primarily Python (FastAPI, pytest, async/await, subprocess, file operations) - Background in full-stack development, with experience building React-based interfaces (JavaScript/TypeScript) and robust back-end systems - Experience writing tests (functional, integration — not just running them) - Docker containers, and familiarity with infrastructure tools (Postgres, Kafka, Redis) - CI/CD understanding (GitHub Actions as a user: triggers, labels, reading results) - English proficiency - B2 You don't need to be an expert in every item, but you should be comfortable reading and reasoning about code across the stack. Why this is hard - Frontier models are already good at coding. Creating a task that genuinely challenges the best models is non-trivial. You need to deeply understand where models fail and what scenarios reveal the difference between a good and a bad solution. - Tasks have many valid solutions. Writing tests that accept all correct solutions and reject incorrect ones is harder than it sounds. How it works Apply → Pass qualification(s) → Join a project → Complete tasks → Get paid Effort estimate Tasks for this project are estimated to take 20 hours to complete, depending on complexity. This is an estimate and not a schedule requirement; you choose when and how to work. Tasks must be submitted by the deadline and meet the listed acceptance criteria to be accepted. Compensation On this project, contributors can earn up to $50 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 We're building a dataset to evaluate AI coding agents — how well a model handles real-world developer tasks. You'll create challenging tasks and evaluation criteria within realistic simulated environments: - Build virtual companies following a high-level plan - codebase, infrastructure, and context (conversations, documentation, tickets) that form a realistic environment with development history - Assemble and calibrate tasks from intermediate states of the virtual company: craft the prompt, define evaluation criteria, and ensure the task is solvable and the evaluation is fair - Design tasks set in isolated environments - emulations of a developer's workstation: a Linux machine with development tools (terminal, CLI), MCP servers (repository, task tracker, messenger, documentation, etc.), and a real web application codebase - Write tests that accept all correct solutions and reject incorrect ones - neither too strict (breaking on valid approaches) nor too lenient (passing bad ones) - Iterate with an AI agent on tests - verifying they catch real problems, don't miss bad solutions, and don't break on good ones - Review code written by agents, analyze why an agent failed or succeeded, and design edge cases and adversarial scenarios - Iterate based on feedback from expert QA reviewers who score your work on quality criteria What this is NOT - Not data labeling - Not prompt engineering - Not writing code from scratch - the agent writes most of the code; you guide and evaluate A significant part of the work is done together with AI - it's very hard to create tasks that challenge frontier models without using frontier models. What we look for This opportunity is a good fit for experienced developers, software engineers, and/or test automation specialists open to part-time, non-permanent projects. Ideally, contributors will have: - Degree in Computer Science, Software Engineering, or related fields - 5+ years in software development, primarily Python (FastAPI, pytest, async/await, subprocess, file operations) - Background in full-stack development, with experience building React-based interfaces (JavaScript/TypeScript) and robust back-end systems - Experience writing tests (functional, integration — not just running them) - Docker containers, and familiarity with infrastructure tools (Postgres, Kafka, Redis) - CI/CD understanding (GitHub Actions as a user: triggers, labels, reading results) - English proficiency - B2 You don't need to be an expert in every item, but you should be comfortable reading and reasoning about code across the stack. Why this is hard - Frontier models are already good at coding. Creating a task that genuinely challenges the best models is non-trivial. You need to deeply understand where models fail and what scenarios reveal the difference between a good and a bad solution. - Tasks have many valid solutions. Writing tests that accept all correct solutions and reject incorrect ones is harder than it sounds. How it works Apply → Pass qualification(s) → Join a project → Complete tasks → Get paid Effort estimate Tasks for this project are estimated to take 20 hours to complete, depending on complexity. This is an estimate and not a schedule requirement; you choose when and how to work. Tasks must be submitted by the deadline and meet the listed acceptance criteria to be accepted. Compensation On this project, contributors can earn up to $50 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.

• Lead the planning, execution, and delivery of multiple AI initiatives, ensuring projects meet scope, timelines, quality expectations, and business objectives • Mentor and support AI Engineers by setting clear direction, providing coaching, and building a high-performing project team • Partner with stakeholders across product, science, clinical, operations, and engineering to define requirements, priorities, milestones, risks, and success metrics • Translate stakeholder needs into technical roadmaps, delivery plans, and resourcing strategies for AI-powered products and platforms • Guide solution design, software development, model integration, testing, deployment, and operational support across the AI project portfolio • Drive strong AI research and engineering practices across the team • Coordinate dependencies, remove blockers, and communicate project status, risks, and tradeoffs clearly to leadership and stakeholders • Balance innovation and experimentation with execution discipline, scalability, reliability, and compliance considerations • Contribute to hiring, onboarding, team planning, and continuous improvement of delivery processes and engineering standards

Are you ready to shape the future of AI in clinical research? Join Clario, part of Thermo Fischer Scientific, as an AI Engineer, where your expertise in machine learning, generative AI, and software engineering will drive transformative solutions across the pharmaceutical and healthcare industries. You’ll be at the forefront of innovation - designing intelligent systems, fine-tuning models, and building scalable AI services that directly impact patient outcomes worldwide. What We Offer - Competitive compensation - Attractive benefits (security, flexibility, support and well-being) - Remote or hybrid working What You'll Be Doing - Design, develop, and maintain AI-powered applications with a strong focus on Agentic AI, AI Agents, and LLM-enabled workflows - Build software services, APIs, and orchestration layers that connect AI models and tools into scalable production solutions - Contribute to the design, training, fine-tuning, evaluation, and deployment of AI models, including large language models and supporting ML components - Develop and optimize data pipelines for model training, retrieval, evaluation, and monitoring - Implement testing strategies, benchmarks, and quality controls to ensure AI systems are reliable, accurate, secure, and performant - Collaborate with AI engineers, software developers, scientists, and product managers to translate business requirements into technical solutions - Participate in the full software development lifecycle: design, implementation, testing, deployment, and monitoring in cloud environments (AWS preferred) - Apply engineering best practices for code quality, documentation, maintainability, and operational excellence - Support experimentation with new AI techniques, frameworks, and tooling, and help mature promising ideas into production capabilities - Stay up to date with advancements in generative AI, agentic systems, multi-modal AI, and cloud engineering What We're Looking For - Bachelor's degree in Computer Science, Engineering, Data Science, or a related field (Master's a plus) - 3+ years of experience in software engineering, AI engineering, machine learning, or a related technical field - Strong proficiency in Python and experience with AI/ML frameworks such as PyTorch or TensorFlow - Experience building AI-enabled software applications and integrating machine learning or LLM capabilities into backend systems - Hands-on experience or strong interest in Agentic AI, AI Agents, tool-use workflows, or multi-step orchestration patterns - Familiarity with model training, fine-tuning, evaluation, prompt and workflow design, or model-serving concepts - Understanding of cloud-based architectures - Strong problem-solving, communication, and collaboration skills - Commitment to code quality, security, scalability, and performance - Eagerness to learn, adapt, and stay current with emerging AI technologies and engineering practices At Clario, our purpose is to transform lives by unlocking better evidence. It’s a cause that unites and inspires us. It’s why we come to work—and how we empower our people to make a positive impact every day. Whether you're advancing clinical science, building innovative technology, or supporting our global teams, your work helps bring life-changing therapies to patients faster.