Montreal, QC, Canada Role Overview As Data Acquisition & Infrastructure Engineer, you will be the foundation of MAI's data capabilities. Your primary focus will be the development and operation of automated data collection pipelines that aggregate publicly available information from across the art market ecosystem. You will also own the design and maintenance of the underlying database infrastructure — built on PostgreSQL and AWS — that stores and serves this data. This is a high-ownership role. The infrastructure is partially built; you will take it to production scale. You will work closely with AI engineers, a computer vision engineer, a product manager, full-stack developers, and the Head of Art Research, reporting directly to the Head of AI Engineering. Key Responsibilities DATA PIPELINE & COLLECTION Architect and maintain automated pipelines that collect, normalize, and ingest publicly available art market data from web-based sources Build reliable, maintainable collection systems using Python (Scrapy, BeautifulSoup, Playwright, or equivalent), with a strong emphasis on resilience, scheduling, and data freshness Manage pipeline orchestration and scheduling using tools such as Apache Airflow, AWS EventBridge, or cron Navigate the practical challenges of large-scale public data collection, including access patterns, rate constraints, and source reliability Handle messy, inconsistent real-world datasets — clean, transform, and standardize data for downstream consumption DATABASE ENGINEERING Design, build, and maintain relational database schemas in PostgreSQL (hosted on Amazon RDS) to support complex, multi-entity art market data — artists, works, transactions, provenance, and valuation history Develop and optimize queries, indexes, and data models to ensure performance at scale Establish and enforce data quality standards, validation rules, and integrity constraints across the database Collaborate with AI engineers and the computer vision team to ensure the data layer supports model training and inference requirements INFRASTRUCTURE & OPERATIONS Deploy and manage pipeline workloads on AWS (Lambda, EC2, S3, RDS) Monitor pipeline health, data freshness, and system reliability — proactively address failures Contribute to infrastructure-as-code practices as the team scales Core Requirements 3–5 years of professional experience in data engineering or a closely related discipline Proven experience building and maintaining automated data collection pipelines from web-based public sources using Python (Scrapy, BeautifulSoup, Playwright, or Selenium) Strong data cleaning and normalization skills, with demonstrated ability to handle heterogeneous, inconsistent real-world datasets Solid PostgreSQL experience: schema design, query optimization, and database maintenance Hands-on AWS experience: Lambda, EC2, S3, RDS Experience scheduling and orchestrating data pipelines (Apache Airflow, AWS EventBridge, or equivalent) Experience navigating the constraints of large-scale public data collection, including reliability, access patterns, and data freshness challenges Nice to Have Knowledge of data quality frameworks and validation pipeline design Experience with containerization (Docker) and infrastructure-as-code (Terraform, AWS CDK) Familiarity with ETL/ELT tooling (dbt, AWS Glue, or equivalent) Exposure to art market platforms (Christie's, Sotheby's, Artsy, Artnet) or understanding of how auction and gallery data is structured Background or genuine interest in the art world, collectibles, or alternative asset markets Experience in a startup or early-stage environment where ownership and adaptability are essential

Remote, Remote Contract hours: Full time Location: Remote (within 2 hours of London timezone) Salary: €25k - €30k depending on experience About Adzuna Adzuna is a job search engine that lists every job, everywhere. From our launch in the UK in 2011, we now have tens of millions of visitors a month and are busy conquering the world from our HQ in West London alongside our remote teams - helping millions of people find better, more fulfilling jobs. We already have a suite of AI-powered tools (such as ValueMyCV, Prepper and ApplyIQ) built by our talented team and their passion for building new AI-powered tech for job seekers. We're a commercially minded team that moves fast, takes ownership and loves what we do. About the Role This is a fantastic opportunity for a talented Junior Python Developer to join our fast moving AI & Innovation Team. You will play a vital role in building and maintaining our full stack - scaling robust backend services, while developing the user facing web components that power our cutting edge AI products and jobseeking tools. You’ll be working in a tight knit, cross-functional team alongside Product Managers, Data Scientists and Senior Engineers. This is a high growth role where you will learn how to transition machine learning prototypes into robust, production ready applications for millions of users, while simultaneously honing your coding, frontend and architecture skills. What You'll Do Fullstack Development: Write clean, testable, and efficient Python code to support our core AI products and automated search and apply tools. Model Integration: Collaborate closely with our Data Scientists to integrate machine learning models, LLM pipelines and agentic frameworks into our live production stack. API & Data Pipelines: Build and maintain scalable APIs and data workflows to handle large volumes of job vacancy data. Infrastructure & DevOps: Help manage and optimise our AWS infrastructure, ensuring high availability and smooth deployment processes. Testing & Quality: Write robust unit and integration tests to ensure system reliability and mitigate deployment risks. Tech Stack Includes Languages & Frameworks: Python, Spark, LLMs & LLM coding tools Frontend: React, HTML, CSS (Tailwind) AWS Ecosystem: EC2, S3, Lambda, Step Functions, RDS, Athena, Kinesis Databases & Search: PostgreSQL, MySQL, SOLR While we look out for all of these skills, we do not expect experience in all of them. We value a strong foundation in core programming principles and a genuine eagerness to learn. Experience: 1–2 years of commercial software development experience using Python Location: Fully remote (must be located within a 2-hour timezone of London and able to work standard UK business hours). Right to Work: This is a long-term role structured via a contractor agreement for international remote compliance. Candidates must have the legal right to work remotely as an independent contractor in their country of residence. We are unable to provide visa sponsorship for this position now or in the future. Communication: Professional proficiency in English (both written and verbal) to collaborate effectively with a global team. Education: A technical degree (2:1 or equivalent) in Computer Science, Software Engineering, or a highly relevant field. Core Coding Skills: Solid understanding of Python fundamentals and object-oriented programming. Execution: Record of writing clean, maintainable Python code into live, user-facing environments. Databases: Good working knowledge of SQL and relational databases. Cloud & Tools: Familiarity with AWS services and Git version control. Mindset: A curious, proactive fast learner who loves solving technical puzzles and is excited by the AI/ML space. Nice to have Frontend Coding Skills: Familiarity with HTML, CSS (Tailwind) and React to assist in building fullstack user interfaces. AI Applications: Experience with coding LLMs into production applications, and effectively utilising LLM coding tools in your daily workflow. Browser Automation: Understanding of web scraping techniques, DOM parsing, and bypassing common bot-detection. Experience with browser automation and testing tools (e.g. Playwright, Selenium). Portfolio: An active GitHub profile showcasing examples of your work, or personal projects.

Remote, Remote, Remote Role Overview: We’re looking for a ML Developer to drive the design, development, and delivery of advanced machine learning solutions. The ideal candidate is not just a strong individual contributor but also a technical leader capable of setting direction, mentoring team members, and ensuring that ML initiatives align with business goals. Competitive ML experience (e.g., Kaggle, benchmarks) is a strong plus. What does day-to-day look like: Own end-to-end DS/ML solution development — from data pipelines and model design to deployment and monitoring. Translate business objectives into robust ML architectures that accurately capture business logic and context. Collaborate cross-functionally with Product, Engineering, and Business stakeholders to define problem statements and success metrics. Evaluate and optimize models for performance, scalability, and accuracy using state-of-the-art techniques. Stay current with advancements in AI/ML research and apply relevant innovations to improve outcomes Required Qualifications: Bachelor’s or Master’s degree in Computer Science, Machine Learning, AI, Statistics, or a related quantitative field. 4+ years of hands-on DS/ML development experience, Proficiency in key DS/ML areas and frameworks:Supervised and Unsupervised Learning Time-Series Forecasting Natural Language Processing (NLP) Computer Vision (CV) Statistical Modeling and Inference Expertise in Python and core libraries (Pandas, NumPy, Scikit-learn, etc.). Ability to understand and apply different models to real-world use cases Strong understanding of data preprocessing, feature engineering, model tuning, and evaluation metrics. Proven ability to design scalable, production-grade ML systems. Preferred Qualifications: Proven expertise in Deep learning (e.g., convolutional neural networks, recurrent neural networks, transformers). Experience with cloud data platforms (Databricks, AWS, etc.) Hands-on experience with PySpark and Databricks Platform Stay up-to-date with the latest advancements in machine learning and artificial intelligence. Bonus: Experience and knowledge in Kaggle competitions and Benchmarks, such as MLEBench Experimenting with new technologies and frameworks based on Research papers published in top conferences and journals Perks of Freelancing With Turing: Work in a fully remote environment. Commitments Required: At least 4 hours per day and minimum 20 hours per week with overlap of 4 hours with PST. (We have 3 options of time commitment: 20 hrs/week, 30 hrs/week or 40 hrs/week) Employment type : Contractor assignment (no medical/paid leave) Duration of contract : 3 month; [expected start date is next week] Location : India, Pakistan, Nigeria, Kenya, Egypt, Ghana, Bangladesh, Turkey, Mexico Evaluation Process (approximately 75 mins) : Two rounds of interviews as follows:Interview 1: Technical, 60 mins Interview 2: Onboarding & cultural discussion, 15 mins

Remote, Remote, Anywhere Job Description Job Title: Python Developer Job Type: Contract Location: Remote Job Summary: In this role, you'll apply your expertise to help train next-generation AI systems. Your work will shape how models learn, reason, and perform through high-quality, real-world input. No prior experience in AI is required — your domain knowledge is what matters. Key Responsibilities: Design, develop, and optimize REST and GraphQL endpoints for scalable APIs. Drive data validation, error handling, and security best practices within backend services. Plan and execute database migrations, optimizations, and schema changes as needed. Actively test new AI-powered models in Cursor, providing actionable feedback via incident reports, bug traces, and screenshots. Participate in 4-day intensive testing bursts, exploring how AI models impact real coding workflows. Engage thoughtfully with the research team in a dedicated Slack channel to discuss findings and propose improvements. Submit detailed post-burst surveys, delivering insights on model performance and developer experience. Required Skills and Qualifications: 3+ years of professional experience as a backend developer, with strong expertise in Python. Proficient in building and maintaining RESTful and GraphQL APIs. Advanced understanding of backend data validation, error handling, and API security. Hands-on experience with database migrations, performance tuning, and schema design. Extensive use of AI tools for coding; familiarity with Cursor is highly desirable. Outstanding written and verbal communication skills, with a knack for clear incident and bug reporting. Proven ability to thrive in fast-paced, confidential, and collaborative remote environments. Preferred Qualifications: Visible contributions to open source (e.g., GitHub stars or contributions). Experience designing or evaluating experimental tooling and developer workflows. Demonstrated enthusiasm for AI advancements in software development.

Glasgow, Scotland, United Kingdom THE OPPORTUNITY You will build and own the modelling-and-learning capability at the heart of LumiAIres: the simulation, encoding–decoding, and machine-learning layer that makes light compute. This is a durable company function: the digital twins, encoding schemes, and readout models you build become reusable assets across every product generation and customer programme. Your immediate focus is our current flagship programme, LUMINA, which leads the high-fidelity digital twin and encoding–decoding scheme, turning the twin into the customer-facing pre-evaluation environment that lets integrators assess our technology before silicon is in hand. Our photonic core behaves as a physical computing medium. Your job is to model how light computes inside it and to train the models that read the answer out. You will work closely with the Integrated Photonics Lead on physical validity, with our academic partners on encoding–decoding, with the Applied Photonics Engineer on validating predictions against measured silicon, and with the Full-Stack Software Engineer on productising the twin. You are the bridge between physics and learning, modelling how light computes inside our photonic core, and training the models that read the answer out. As we scale, this role can grow into ownership of LumiAIres' modelling and machine-learning function and the team behind it. CONTRACT & PROGRESSION CONTRACT TYPE: 18-Month Fixed Term Contract LOCATION: Glasgow, Scotland (Hybrid) SALARY: TBC per annum REPORTS TO: CTO (currently the CEO) POTENTIAL FOR PERMANENT ROLE: Yes — Glasgow or Paris (determined within the first 12 months) START DATE: To be agreed This role is offered initially as an 18-month Fixed Term Contract based in Glasgow. Given the portable nature of the modelling work, the permanent position offers location flexibility as the company scales. WHY LUMIAIRES Work at the frontier of photonic AI, technology with the potential to reshape how the world processes information. A founding team with deep expertise in photonics, neuromorphic computing, and commercial strategy. Exposure to global investors, strategic partners, and government stakeholders from day one. A genuinely values-led company building for the long term. Flexible, trust-based culture with real ownership and impact. HOW TO APPLY To express interest in this role, please send a CV and a short covering note describing your background in optical or photonic simulation and machine learning, any modelling or digital-twin work you have delivered, and what draws you to photonic computing. Applications and initial conversations are treated in strict confidence. KEY RESPONSIBILITIES The role is structured around four core pillars: Photonic & Optical Simulation Model the photonic core and full signal chain using established optical-simulation and numerical wave-propagation methods. Build, maintain, and validate the high-fidelity digital twin of the photonic processor. Quantify how design and fabrication choices affect computational behaviour, feeding insight back to the photonic design team. Encoding, Decoding & Core Behaviour Design and optimise input encoding and output decoding schemes for the photonic platform. Characterise how the photonic core behaves as a physical computing medium. Co-own encoding–decoding optimisation with our academic partners. Machine Learning & Readout Develop the readout models and associated training pipelines. Build the learning loop that improves inference over time and feeds our software tools. Optimise the mapping of machine learning inference onto the photonic hardware. Customer-Ready Twins & Validation Harden the digital twin into a pre-evaluation environment that integrators can run, with the Full-Stack Software Engineer and the Strategic Partners Lead. Validate twin predictions against measured silicon in partnership with the Applied Photonics Engineer. WHAT WE ARE LOOKING FOR A strong computational scientist or engineer with an advanced degree (MSc or PhD) in applied mathematics, computational or theoretical physics, electrical engineering, computer science, or a related quantitative field. Strong optical and photonic simulation skills, including transfer-matrix and numerical wave-propagation methods (or equivalent). A grounding in practical machine learning — model training, evaluation, and pipeline development (Python; NumPy / SciPy and PyTorch / JAX or similar). Strong scientific software practice and the ability to turn physics into validated, maintainable code. Familiarity with photonic neural networks, optical, or neuromorphic computing is a strong advantage. Experience with digital-twin or hardware-in-the-loop modelling, active-learning methods, or working alongside experimental and academic teams is welcome. Comfort in an early-stage, fast-moving environment where structure is built, not inherited.

Remote, Remote, Remote Role Overview: This position is within a project with one of the foundational LLM companies. The goal is to assist these foundational LLM companies in enhancing their Large Language Models. One way we help these companies improve their models is by providing them with high-quality proprietary data. This data serves two main purposes: first, as a basis for fine-tuning their models, and second, as an evaluation set to benchmark the performance of their models or competitor models. For example, for SFT data generation, you might have to put together or be provided a prompt which contains provided code and questions, you will then provide the model responses, and write corresponding Python code to solve the questions. For RLHF data generation, you may need to create a prompt yourself or use one provided by the customer, ask the model questions, and evaluate the outputs generated by two versions of the LLM. You'll compare these outputs and provide feedback, which is then used to fine-tune the models. Please note that this role does not involve building or fine-tuning LLMs. What does day-to-day look like: Design, develop, and maintain efficient, high-quality Python code to train and optimize AI models. Conduct evaluations (Evals) to benchmark model performance and analyze results for continuous improvement. Familiarity with Python frameworks and libraries Evaluate and rank AI model responses to user queries across diverse domains, ensuring alignment with predefined criteria. Develop comprehensive explanations and rationales for evaluations, showcasing excellent reasoning and technical expertise. Lead efforts in Supervised Fine-Tuning (SFT), including creating and maintaining high-quality, task-specific datasets. Collaborate with researchers and annotators to execute Reinforcement Learning with Human * Feedback (RLHF) and refine reward models. Design innovative evaluation strategies and processes to improve the model's alignment with user needs and ethical guidelines. Create and refine optimal responses to improve AI performance, emphasizing clarity, relevance, and technical accuracy. Conduct thorough peer reviews of code and documentation, providing constructive feedback and identifying areas for improvement. Collaborate with cross-functional teams to improve model performance and contribute to product enhancements. Continuously explore and integrate new tools, techniques, and methodologies to enhance AI training processes. Requirements: 3+ years of strong experience with Python programming language. Industry experience and knowledge of code quality, formatting, and best practices of software development Experience with Python’s testing ecosystem, including unit, integration, and property-based testing. Knowledge of multi-threading and asynchronous programming in Python. Ability to work with architectural patterns and refactor code without introducing regressions. Strong debugging skills, including fixing memory and concurrency issues. Fluent in conversational and written English communication skills