Head of Policy & Security Research Lab
As a Production AI Ops Lead within Scale's Global Public Sector team, you will design and develop the production lifecycle of full-stack AI applications, supporting end-to-end system reliability, real-time inference observability, sovereign data orchestration, high-security software integration, and resilient cloud infrastructure for international government partners. You will take full accountability for the long-term performance and reliability of AI use cases deployed across international government agencies, oversee the end-to-end health of the platform ensuring seamless integration between AI core and full-stack components, build automated systems to monitor model performance and data drift in dispersed environments, manage the technical lifecycle within diverse regulatory frameworks, lead incident response for production issues in mission-critical environments, translate technical performance metrics into clear insights for senior government officials, and partner with Engineering and ML teams to ensure field lessons influence future technical architecture and decisions.
Senior Backend Engineer
Design, build, and maintain scalable backend services and APIs that power Chattermill’s core analytics platform. Architect reliable, maintainable distributed systems and contribute to the evolution of backend service design and infrastructure. Own end-to-end delivery of backend engineering workstreams, from technical scoping and architecture through to implementation, testing, observability, and production support. Integrate language models, agentic frameworks, and AI pipelines into core product and backend services. Drive performance, reliability, and observability across high-throughput distributed data systems, including logging, tracing, alerting, and incident response. Work with cloud infrastructure and distributed systems in GCP (preferred) or AWS environments. Collaborate closely with Product to define scope, shape technical solutions, and explore new platform capabilities and features. Contribute to engineering excellence through code reviews, architectural discussions, and continuous improvement of development standards across the team.
Materials Engineer & Python Expert - Freelance AI Trainer
Design computational material science problems to challenge a frontier AI model using specialized tools such as ObsPy, instaseis, pyrocko, MITgcm, flopy/MODFLOW, or others. Each problem must have an answer verifiable by code and run inside a sealed Linux container with the tool pre-installed and a programmatic judge that grades the model's answer. Pick an anchor tool and design a problem hinging on its waveform-processing kernels, geophysical inversion routines, sub-surface flow solvers, or community-validated data pipelines. Write Python reference solutions, supply input files and model or domain definitions as needed. Decide numerical answers and acceptable tolerance for correctness. Test and tune the problem difficulty against batches of the AI model's attempts until the agent succeeds only infrequently. Submit the task for senior review to ensure quality. Calibrate problems by rewriting scenarios, tightening parameters, and observing AI model behaviors to achieve a pass rate of 10–30%. Learn and deepen command of the anchor tool and gain intuition for how the AI navigates complex scientific problems.
Manager, AI Deployment Engineering - Codex
Lead, hire, and mentor a high-performing team of AI Deployment Engineers supporting Codex customers across strategic accounts. Own the operating model and engagement strategy for Codex deployment efforts, ensuring customers successfully move from pilot to production adoption. Guide teams in designing and implementing AI-enhanced development workflows, automations, and scalable deployment architectures. Act as the senior technical escalation point for complex customer implementations and deployment challenges. Partner with Sales, Product, Research, and Applied Engineering teams to align customer outcomes with product direction and roadmap priorities. Help establish repeatable deployment playbooks, technical patterns, and best practices that enable scaled adoption of AI coding tools. Coach engineers to operate as trusted advisors to engineering leadership and executive stakeholders. Synthesize insights from customer deployments and translate them into actionable feedback for internal teams. Champion safe, reliable, and effective adoption of AI-powered development workflows across industries.
Mechanical Engineer & Python Expert - Freelance AI Trainer
Design computational engineering problems that challenge a frontier AI model using specialized tools like Cantera, CoolProp, CalculiX, OpenFAST, or similar. Create problems that have verifiable answers through code and require these specialized solvers, simulation kernels, or domain-specific models. Write Python reference solutions, supply necessary input files and definitions, determine numerical answers with domain-appropriate tolerance for correctness, and test problems against the AI model in batches of parallel attempts. Tune problem difficulty so the model succeeds only in a small number of attempts, and then submit tasks to a senior reviewer for feedback. Calibrate the problem by rewriting thermodynamic cycles, adjusting material models and boundary conditions, and observing how the agents behave, aiming for a pass rate between 10–30%. Gain deeper command of the anchor tool and develop intuition for how the AI navigates complex thermal, structural, and fluid mechanics problems.
Senior Consultant - AI Training & Evaluation (MBB & Top-Tier Firms)
Build realistic consulting project environments by creating detailed project scenarios grounded in real engagement dynamics including industry context, financials, constraints, conflicting inputs, and incomplete information. Design structured consulting tasks for AI agents by breaking projects into discrete tasks that mirror real consulting work such as market sizing, commercial due diligence, cost optimization, growth strategy, operational diagnosis, benchmarking, and more. Define evaluation criteria and quality standards by developing grading frameworks, evaluation rubrics, and golden-answer solutions for each task, which are used to train and calibrate an LLM-based grading system that evaluates AI outputs at scale. This is a remote, project-based, individual-contributor role focused on analytical design and evaluation.
Member of Engineering (Pre-training / Data Research)
Follow the latest research related to Large Language Models (LLMs) and data quality, being familiar with relevant open-source datasets and models. Design and implement complex pipelines to generate large amounts of diverse data while optimizing available resources. Collaborate closely with teams such as Pretraining, Posttraining, Evals, and Product to ensure short feedback loops on the quality of models delivered. Suggest, conduct, and analyze data ablations or training experiments to improve the quality of generated datasets using quantitative insights.
Software engineer, generative AI
Design and develop robust, scalable, and secure generative AI services and applications using Python and modern frameworks to drive enterprise-wide transformation. Build and optimize high-performance, low-latency APIs and microservices for integrating advanced AI models and agentic workflows into the platform. Collaborate closely with product managers, data scientists, and cross-functional engineering teams to translate complex business needs into innovative AI solutions, from concept to production. Implement and maintain responsive user interfaces using technologies like React and TypeScript, primarily focused on backend enablement but including some frontend interaction. Partner with DevOps teams on continuous deployment, logging, and monitoring to ensure top-tier performance and reliability. Own key architectural components, ensuring best practices in code quality, security, and maintainability through rigorous testing and peer reviews.
Software engineer, generative AI (UK)
Design and develop robust, scalable, and secure generative AI services and applications using Python and modern frameworks to drive enterprise-wide transformation. Build and optimize high-performance, low-latency APIs and microservices for integrating advanced AI models and agentic workflows into the platform. Collaborate closely with product managers, data scientists, and cross-functional engineering teams to translate complex business needs into innovative AI solutions, from concept to production. Implement and maintain responsive user interfaces primarily focused on backend enablement, using technologies like React and TypeScript to deliver intuitive user experiences. Partner with DevOps teams building continuous deployment, logging and monitoring to ensure top-tier performance and reliability. Own key architectural components, ensuring best practices in code quality, security, and maintainability through rigorous testing and peer reviews.
Software engineer, agents (UK)
Design, implement, and maintain scalable, secure agent-driven services and systems that autonomously accomplish tasks using modern AI frameworks. Develop and enhance robust infrastructure and high-throughput APIs, focusing on core agent capabilities such as memory, communication channels, skills, intelligent decision logic, security and workflow management. Integrate agent capabilities with backend services, data stores, vector databases, search/retrieval systems, and external APIs. Collaborate with product managers, AI researchers, data engineers, and UX teams to translate high-level agent use cases into robust, production-ready software. Ensure reliability, monitoring, and observability for all agent components (metrics, logging, CI/CD, fault tolerance). Contribute to architectural design decisions and participate in rigorous code reviews to uphold quality and maintainability.
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