Senior AI Engineer
The responsibilities include building agent-driven enrollment and parent communication pipelines that scale significantly without proportional headcount growth; creating and managing parallel simulations of students testing curriculum to identify gaps and generate improvements; developing automated culture and community agents for engagement, onboarding, and retention at machine scale; constructing real-time operational dashboards to provide leadership with visibility into various business aspects such as enrollment, academic progress, parent satisfaction, and campus operations; designing AI-first workflows for guides, advisors, and operational staff to reduce administrative burdens and refocus on students; building systems called Brainlifts to capture and compound institutional knowledge over time; and integrating these capabilities into Alpha's broader AI ecosystem including EPHOR, Alpha GPTs, and Fleet/Swarm infrastructure.
Clinical AI Engineer
Build end-to-end AI features by architecting and shipping fullstack solutions from React frontends to Python backend services that leverage voice AI and large language models to automate clinical workflows; implement and fine-tune audio processing pipelines ensuring accurate performance of Automatic Speech Recognition (ASR) and LLM agents in diverse medical environments; translate complex clinical feedback into technical solutions by rapidly prototyping and deploying improvements to model behavior, prompting strategies, and audio handling; optimize fullstack performance for real-time audio streaming and token generation to minimize latency for seamless clinician interaction; partner with implementation and clinical teams to shorten the feedback loop by shipping critical integrations and feature requests from concept to production quickly.
Data Strategy Associate
Design and build intuitive web interfaces for robot data annotation, datasets visualization, and experiment tracking. Utilize data-driven techniques to optimize interfaces for efficiency and fast iteration cycles. Integrate AI models to automate manual tasks. Work together with AI researchers, robot operators, and annotators to support new user experiences.
DevOps Engineer, Infrastructure & Security
The role involves taking full accountability for the long-term performance and reliability of AI use cases deployed across international government agencies. Responsibilities include overseeing the end-to-end health of the platform to ensure seamless integration between the AI core and all full-stack components, from APIs to UI, maintaining a responsive and production-ready environment. The job also requires building automated systems to monitor model performance and data drift across geographically dispersed environments, managing the technical lifecycle within diverse regulatory frameworks, leading the response for production issues in mission-critical environments, ensuring rapid resolution and prevention of future issues. Additionally, the role requires translating deep technical performance metrics into clear insights for senior international government officials and partnering with Engineering and ML teams to ensure lessons learned in the field influence the technical architecture and decisions of future use cases.
Full Stack Engineer
Build and maintain features for the web-based property management platform using TypeScript, React, Node.js, PostgreSQL, and AWS. Contribute to a monorepo architecture, working within two-week sprint cycles to deliver high-quality code. Implement integrations including DocuSign, Plaid, Stripe, and ownership group payout systems. Optimize platform performance and user experience by replacing legacy systems. Build and integrate AI agents using Claude and other AI APIs to automate organizational processes, developing API integrations and custom agents. Collaborate with the CEO on prioritizing automation opportunities. Take ownership of tasks, independently research and implement solutions to challenges, proactively identify and implement improvements, and contribute ideas to platform architecture and development priorities.
Copy of Member of Technical Staff - ML Engineering
Deploy, maintain, and optimize production and research compute clusters. Design and implement scalable and efficient ML inference solutions. Develop dynamic and heterogeneous compute solutions for balancing research and production needs. Contribute to productizing model APIs for external use. Develop infrastructure observability and monitoring solutions.
Product Manager, Agent Harness & Modelling
Define and own the roadmap for North's agent harness, including the agent loop, context engineering layer, tool orchestration, sandbox execution, and sub-agent delegation. Serve as the primary interface between North engineering and Cohere's Modeling team, ensuring new harness capabilities are validated before being built and that neither team limits future possibilities. Own North's agentic evaluation framework, ensuring evaluations are compatible with both the North harness and Modeling's training infrastructure, serving as a reliable bridge between product and research. Engage enterprise customers to identify real-world agentic failures and translate findings into product and model requirements. Stay current with the open-source and commercial agent ecosystem and drive adoption decisions that align North's architecture with emerging standards.
Research Engineer – Benchmarking, Evals & Failure Analysis
As a Research Engineer at Mercor, you will own benchmarking pipelines, evaluation systems, and failure analysis workflows that directly inform how frontier language models are trained and improved. You will design, implement, and maintain benchmarks and metrics for tool use, agentic behavior, and real-world reasoning, ensuring they scale with training and align with product and research goals. You will build and operate LLM evaluation systems including runs, scoring, dashboards, and reporting to allow tracking and comparison of model performance at scale. You will conduct systematic failure analysis on model outputs, categorize failure modes, quantify their prevalence, and use these insights to influence reward design, data curation, and benchmark design. Additionally, you will create and refine rubrics, automated evaluators, and scoring frameworks that influence training and evaluation decisions, balancing rigor and scalability. You will quantify data usability and quality, guide data generation, augmentation, and curation based on evaluations and failure analysis. Collaboration with AI researchers, applied AI teams, and data producers to align evaluations with training objectives and prioritize important benchmarks and failure analyses is expected. Finally, you will operate with strong ownership in a fast-paced, high-iteration research environment.
Aerodynamics Methodology and Software Engineer
Refactor research scripts and specialist tools into modular, high-performance, and maintainable Python/C++ libraries, implementing robust unit-testing and documentation standards, and ensuring the team follows code development structure. Architect agentic workflows and custom MCP servers to connect LLMs with internal CFD solvers and databases, codifying engineering knowledge into structured files to enable AI-driven code refactoring, automated simulation setup, and intelligent data analysis. Develop APIs and automated workflows to integrate tools like OpenVSP, XFoil, and OpenFOAM into seamless optimization loops. Manage and optimize Linux-based HPC clusters and/or Cloud computing infrastructure. Design the data architecture for storing and retrieving aerodynamic results to provide vehicle performance data as a single source of truth for GNC and flight physics teams.
Engineering Leader
As an Engineering Leader at Ema, you will build and lead a high-performance engineering organization by recruiting, hiring, and developing senior engineers across multiple sub-teams including cloud infrastructure, data platform, ML operations, and developer experience. You will establish engineering standards, a code review culture, on-call expectations, and promote a bias-toward-shipping mentality balanced with production rigor. You will coach and grow senior and staff engineers into technical leaders and manage engineering managers as the organization scales. Your responsibilities include setting the 6–18 month platform roadmap in partnership with engineering teams, making critical architectural decisions such as build versus buy and migration strategies, and driving cross-functional alignment with product, ML/AI research, and go-to-market teams. You will own production health for all platform services, including incident response, postmortems, SLO tracking, and capacity planning. Additionally, you will establish and refine engineering practices to maintain fast shipping without compromising reliability, and participate in executive-level reviews related to infrastructure spend, system health, and engineering velocity.
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