Top Tech Jobs & Startup Jobs

Build and own AI-powered software for real estate title and escrow operations. Responsibilities include creating property and transaction data systems, developing multi-agent workflows, integrating and evaluating AI systems, applying language models to property law, and automating closings, mortgages, documents, and payments. The role involves full-stack product ownership, technical decision-making, deployment, and close collaboration with attorneys, closing teams, operators, and the CTO.

Internal, unpublished systems-integration fixture for validating that Workable applications preserve candidate identity, resume, LinkedIn profile, and application metadata through Raydar Applicant Hub and Paraform. It is not an employment opportunity and provides no compensation or benefits. Production roles must be cloned, renamed, given an actual description, and configured with mandatory candidate name, email, resume, and public LinkedIn URL fields.

Own the full sales cycle for acquiring datasets from startups winding down. Responsibilities include founder outreach, outbound prospecting, pipeline development, deal negotiation, data pricing and legal terms, closing transactions, and improving the sales playbook. The role targets at least 10 datasets acquired monthly with a 40% or higher close rate and requires onsite work five days per week in Brooklyn.

Build qualified sales pipeline through outbound prospecting and inbound lead qualification. Research target accounts, personalize outreach, qualify leads, educate technical and business buyers on AI inference and model-serving products, meet pipeline targets, and collaborate with Sales and Marketing. The role requires full-time onsite work in New York City or the San Francisco Bay Area.

Internal, unpublished integration fixture used to verify that Workable applications transfer candidate identity, LinkedIn profile URLs, metadata, and resumes into Raydar Applicant Hub and Paraform. It is not an employment opportunity and offers no compensation or benefits. Production roles must be cloned, renamed, populated with an actual description, and configured with mandatory candidate name, email, resume, and public LinkedIn profile fields.

Build full-stack and backend software for supply chain, pricing, quoting, inventory, and manufacturing operations. Design data models, develop production simulations, and integrate equipment telemetry, robotics, and factory automation. Collaborate with hardware and automation engineers, shape platform architecture and product direction, and own solutions from problem definition through deployment. The role requires onsite work in Los Angeles and eligibility for ITAR-controlled projects.

Deploys and supports production software in secure government environments, leading requirements, pilots, rollouts, backend development, and user engagement. Acts as the primary technical contact for government customers, translating operational needs into software and managing projects through go-live. Requires experience delivering systems in defense or government IT environments, strong backend and full-stack engineering skills, U.S. citizenship, security clearance eligibility, and 20–30% travel.

Design and deliver safety-critical tooling and production equipment for hazardous materials, including holding, forming, lifting, transfer, temperature regulation, and material-processing systems. Own projects from concept through shop-floor implementation using simulation, tolerance analysis, and design-for-manufacturability practices. Collaborate with manufacturing and design teams, ensure aerospace and defense standards compliance, and coach junior engineers and technicians.

Design and ship production software systems built on large language models, focusing on AI agent quality, evaluation, reliability, and autonomous behavior. Build end-to-end evaluation frameworks, prototype multi-agent and feedback-learning approaches, develop agent coordination services, and translate current AI research into practical experiments. Own major product areas and influence technical direction in an early-stage B2B SaaS environment.

Develop and optimize a production machine learning serving platform for low latency, high throughput, reliability, and cost efficiency. Responsibilities include supporting open-source models, improving batching, caching, decoding, and quantization, writing GPU kernels, operating mixed-hardware compute clusters, and managing production inference. The role also includes customer communication and autonomous work direction. Strong computer systems knowledge is required, with GPU programming and large-scale infrastructure experience preferred.