Top Tech Jobs & Startup Jobs

Designs, builds, and deploys production-grade autonomous agents, multi-agent systems, and LLM-powered enterprise applications. Responsibilities include agent orchestration, tool and enterprise-system integration, prompt and memory optimization, workflow reliability, monitoring, evaluation, and AWS cloud deployment. The role also establishes reusable engineering standards and applies MLOps/AIOps practices while collaborating with engineering, data, cloud, and business teams in regulated financial-services environments.

Conduct frontier AI research across synthetic and agentic data generation, reinforcement learning, post-training, model understanding, benchmarking, and evaluation. Design experiments, datasets, prototypes, tooling, and evaluation frameworks; train and assess models; analyze results; ensure rigor and reproducibility; and translate findings into scalable AI products. Collaborate across research, engineering, product, and operations teams, communicate technical insights, contribute to publications or open source, and mentor engineers and researchers.

Conduct research on frontier AI systems, including synthetic and agentic data generation, reinforcement learning, post-training, model understanding, benchmarking, and evaluation. Design rigorous experiments, datasets, prototypes, tooling, and research workflows; train and assess models; analyze results; and translate findings into scalable AI products. Collaborate across research, engineering, product, and operations teams, communicate technical conclusions, contribute to publications or open-source work, and mentor engineers and researchers.

Leads end-to-end delivery of large-scale AI and software-engineering data programs, managing scope, timelines, quality, throughput, contributors, and costs. Designs workflows for coding datasets, agentic trajectories, reinforcement-learning environments, benchmarks, and evaluations. Builds quality-control systems, analyzes operational metrics, manages distributed technical contributors, communicates with AI lab customers, translates research goals into task specifications, automates reporting with code, and develops reusable operational playbooks.

Leads end-to-end delivery of large-scale AI and software-engineering data programs, including coding datasets, agentic trajectories, reinforcement-learning environments, benchmarks, and evaluations. Owns scope, quality, throughput, contributor performance, timelines, and cost; improves workflows, quality controls, capacity planning, and reporting. Manages distributed contributor teams, communicates with AI lab customers, translates research objectives into task specifications, analyzes operational metrics, and develops reusable delivery playbooks.

Leads large-scale AI and software-engineering data programs, owning delivery scope, timelines, quality, throughput, contributor performance, and cost. Designs workflows, quality-control systems, metrics, and capacity plans for coding datasets, agentic trajectories, benchmarks, and evaluations. Manages distributed contributor and reviewer networks, partners with talent teams, communicates with AI-lab customers, analyzes operational data, and converts successful processes into reusable playbooks and evaluation assets.

Lead the assessment and transformation of complex datacenter and cloud environments. Design target architectures spanning compute, storage, networking, virtualization, backup, disaster recovery, automation, and Azure services. Evaluate vendors and infrastructure models, assess resilience and lifecycle requirements, define migration roadmaps, and provide cost, licensing, sizing, and operational recommendations. Present architecture strategies and decision materials to senior stakeholders across global regions.

Opportunity for software developers, founders, engineering teams, and code owners to submit existing repositories for licensing evaluation. Eligible code may include abandoned startups, side projects, internal tools, SaaS products, and discontinued applications. Applicants must own or have legal rights to license the code, provide repository access, and exclude unauthorized third-party material. Compensation is based on agreed licensing terms, with reference rates for non-exclusive licenses.

Opportunity for software developers, founders, engineering teams, and code owners to submit existing repositories for licensing assessment. Eligible codebases may include abandoned startups, personal projects, internal tools, SaaS products, and discontinued applications. Applicants must own or legally control licensing rights, provide repository access, and exclude unauthorized third-party material. Compensation is based on agreed licensing terms, with reference rates for source-code and Git-history tokens.

Opportunity for software developers, founders, engineering teams, and code owners to submit existing repositories for licensing assessment. Eligible codebases include abandoned startups, side projects, internal tools, SaaS products, and discontinued applications. Applicants must own or have legal rights to license the code, provide repository access, and exclude unauthorized third-party material. Compensation is based on agreed licensing terms, with reference rates for non-exclusive licenses.