Top Tech Jobs & Startup Jobs

Own Brain Co.’s developer platform, including development environments, monorepo tooling, build systems, CI/CD, code-review automation, and engineering workflows. Measure and reduce developer friction through instrumentation and user feedback. Improve reliability, onboarding, adoption, and delivery speed across cloud, regulated, and air-gapped environments. Integrate AI-assisted development tools, support migrations, and operate the platform through its full lifecycle.

Build and deploy production machine learning systems, including LLMs, agents, and RAG applications, for healthcare, government, energy, and other regulated industries. Responsibilities include developing scalable data pipelines, optimizing model performance, monitoring deployed models, translating research into practical solutions, collaborating with leaders and cross-functional teams, and contributing to software engineering practices. The role is designed for a 2027 bachelor's graduate with hands-on generative AI and machine learning framework experience.

Build backend services, data pipelines, APIs, distributed systems, developer tools, and shared platform capabilities for AI products. Develop ML experiment tracking, artifact management, and automated training and evaluation pipelines. Improve reliability, latency, and throughput while collaborating with Product and ML teams. Own scoped projects involving implementation, testing, maintenance, and clean API design for enterprise and government customers.

Build and deploy production-grade AI applications across front-end, back-end, and data workflows. Translate customer problems into product requirements, integrate AI models, optimize application performance and reliability, evaluate AI outputs, and iterate using customer feedback. The role supports impactful projects for government, healthcare, and industrial customers, with ownership across the full product lifecycle.

Build and deploy production-grade AI systems for regulated institutions. Own ambiguous ML problems end-to-end, including data definition, model training, evaluation, deployment, and monitoring. Work with LLMs, vision models, reinforcement learning, agentic systems, and rule engines to process complex documents and support institutional decisions. Collaborate directly with customers, engineer for accuracy, latency, cost, and reliability, and develop rigorous evaluations and failure-mode taxonomies.

Build shared machine learning capabilities for AI-native platform products, including document extraction, foundation models, model routing, evaluation, agentic systems, and continuous improvement. Translate urgent product-team needs into reusable platform abstractions, own capabilities through production, optimize accuracy, latency, cost, and reliability, and develop feedback and retraining loops that improve deployed systems across regulated institutional workflows.

Own the financial services product line from discovery through launch and iteration. Set the roadmap, determine which deployment capabilities become core product features, conduct user discovery, define quality standards, and establish accuracy, human-review, citation, and safe-failure models for regulated workflows. Prototype in design or code, collaborate with engineers and designers, understand the market, and deliver reusable AI capabilities across financial institutions.

Conduct AI diligence for private equity and corporate sponsors, evaluating target companies’ AI strategies, data infrastructure, and technical maturity. Translate technical findings into investment and value-creation recommendations, manage client relationships and engagement workstreams, prepare presentations and deliverables, and collaborate with engineering and product leaders to define feasible AI solutions. The role requires strong analytical, communication, commercial, and AI/ML literacy skills in a fast-paced startup environment.

Own full-cycle technical recruiting for AI/ML, AI Product, and AI Platform engineering teams. Partner with leadership to define hiring needs, design sourcing strategies, manage candidate experience, build market maps and pipelines, negotiate complex offers, and represent Brain Co. at events to attract top technical talent.

As an AI Product Engineer, you will develop and deploy AI-driven software solutions, collaborate with customers, and optimize applications for real-world impacts.