Computer Vision Engineer
The Computer Vision Engineer will deliver hands-on computer vision work and architect technical solutions for complex project requirements. They will lead the technical delivery of computer vision projects and provide expert guidance to multidisciplinary teams throughout the development lifecycle. The role includes contributing expert computer vision insight to bids and identifying opportunities to integrate advanced visual intelligence into customer solutions. The engineer will stay at the forefront of the field by mastering State-of-the-Art developments and sharing best practices across the business unit. They will represent the organization internally and externally as a subject matter expert in computer vision, partner with leadership to define the technical strategy for computer vision work, take ownership of capability development within the Defence domain, and mentor and develop team members interested in computer vision, fostering a continuous learning and technical excellence environment.
Full-Stack Software Engineer, Mobile Apps
You will own features end-to-end, holding a piece of work from contract through rollout across whichever surfaces it needs to touch, including the TypeScript/Node backend, the iOS app, the Android app, or all three. You will deliver the entire feature rather than handing off parts outside your domain. Early tasks include shipping meaningful production code on one of the products, defining and defending API contracts with mobile and design teams before implementation, and iterating on those contracts. You are expected to develop a point of view on the team's workflow and address inefficiencies, working across stack boundaries to deliver complete features and get them in front of real users.
AI Deployment Engineer | Codex
Serve as the primary technical subject matter expert on OpenAI Codex for a portfolio of customers, embedding deeply with them to enable their engineering teams and build coding workflows. Partner directly with customers to design and implement AI-enhanced development workflows, from rapid prototyping through scalable production rollout. Build high-quality demos, reference implementations, and workflow automations, using Codex itself as part of the development process. Lead large-format workshops, technical deep dives, and hands-on enablement sessions that help engineering organizations adopt AI coding tools effectively and safely. Contribute technical content including examples, guides, patterns, and best practices to the OpenAI Cookbook to help the broader developer community accelerate their work with Codex. Gather high-fidelity product insights from real customer deployments and translate them into clear product proposals and model feedback for internal teams. Influence customer strategy and decision-making by framing how AI coding tools fit into their software development lifecycle, technical roadmaps, and organizational workflows. Serve as a trusted advisor on solution architecture, operational readiness, model configuration, security considerations, and best-practice adoption.
Member of Engineering (Post-training)
Research and experiment on ways to specialize foundational models to agentic use cases, build and maintain data and training pipelines, keep up with latest research and be familiar with state of the art in LLMs, alignment, synthetic data generation, and code generation, design, analyze, and iterate on training, fine-tuning, and data generation experiments, write high-quality and pragmatic code, and work as part of a team by planning future steps, discussing, and communicating clearly with peers.
AI Productivity Engineer
The AI Productivity Engineer will take clear ownership of rapid AI adoption across the engineering organization by building AI-powered tools and systems that improve engineering productivity, reducing friction, automating repetitive tasks, and embedding intelligence into workflows. Responsibilities include identifying high-friction areas in engineering workflows, designing and building production-grade AI-powered developer tooling for coding, testing, PR reviews, and debugging, building contextual AI assistants using internal data and tools, exploring, prototyping, and productionizing AI solutions, automating workflows across platforms like GitLab, Jira, CI/CD, Slack, and observability tools, designing and operating internal AI services and orchestration layers, owning solutions end-to-end from discovery to iteration, working hands-on with engineering teams to remove friction and enable tool usage, and measuring success through adoption, impact, and tangible time saved for engineers. The role explicitly excludes building AI features for customer-facing products, speculative AI research without clear outcomes, acting as general internal support, and owning generic ML infrastructure unrelated to developer productivity.
Partner AI Deployment Engineer - AWS
As a Partner AI Deployment Engineer focused on AWS, the role involves serving as the primary technical counterpart to AWS field leadership, shaping strategy, defining engagement models, and building scalable systems globally. Responsibilities include influencing joint account strategy and technical direction, leading technical strategy for large enterprise engagements, guiding customers from ideation through architecture design to production deployment, and acting as a technical decision-maker and escalation point. The role requires designing and communicating AI architectures using OpenAI and AWS services, building prototypes and reference implementations, establishing best practices for scalable and secure GenAI systems, and enabling AWS and partners through scalable technical motions such as workshops and playbooks. It also includes mentoring partner technical teams, scaling impact through GSIs, RSIs, and ISVs, collaborating cross-functionally with Alliances, Product, Engineering, GTM, and Enablement teams, delivering insights to inform product roadmaps, and contributing to internal knowledge systems and standards for the AI Deployment Engineering function.
Mathematics & Python Expert - Freelance AI Trainer
Contributors may design original computational mathematics problems that simulate real mathematical research workflows, create problems requiring Python programming to solve (using Numpy, SciPy, Sympy), ensure problems are computationally intensive and cannot be solved manually within reasonable timeframes (days/weeks), develop problems requiring non-trivial reasoning chains in areas like number theory, combinatorics, graph theory, and numerical analysis, base problems on real research challenges or practical applications from mathematical practice, verify solutions using Python with standard mathematical libraries, and document problem statements clearly while providing verified correct answers.
Member of Engineering (Evaluations)
Design and implement the infrastructure and tooling used by poolside researchers and engineers. Research and implement evaluations and benchmarks for base models and instruction following models. Collaborate with applied research and product teams to define meaningful metrics and evaluations that capture progress on real world software development skills. Work in a team setting to plan future steps, discuss, and communicate clearly with peers.
Senior software engineer, enterprise AI platform (UK)
Act as a whole-systems thinker by making meaningful system design decisions and owning the architecture of core platform components from initial design through production deployment. Develop secure generative AI services and applications using Python and modern frameworks to drive enterprise-wide transformation. Build and optimize high-performance and low-latency APIs and microservices for integrating advanced AI models and agentic workflows into the enterprise platform. Drive proactivity without red tape by clearly communicating changes, plans, and proposals to cross-functional teams without waiting for approval. Bridge the gap between backend services, infrastructure, and operations to ensure seamless deployments and scalable architecture.
AI Product Manager, London
As an AI Product Manager at Air Apps, you will lead the product development lifecycle for AI-driven features, working closely with engineers, designers, and data scientists to develop, launch, and scale AI-driven solutions. Responsibilities include defining and driving the AI product roadmap to ensure alignment with business objectives and user needs; collaborating with cross-functional teams including engineering, design, and marketing to develop and launch AI-powered features; conducting market research and analyzing user feedback to identify opportunities for AI integration; working closely with data scientists and machine learning engineers to optimize AI models for accuracy, performance, and user impact; defining key performance indicators (KPIs) to measure success and iterating based on data-driven insights; staying up to date with AI trends, emerging technologies, and best practices to ensure product competitiveness; and ensuring ethical AI usage and compliance with data privacy regulations.
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