Top Tech Jobs & Startup Jobs in NYC, NY

Design and develop shared platform systems, foundational data platforms, cloud infrastructure, orchestration, and software delivery tooling. Partner with stakeholders and cross-functional teams to define and deliver features, improve engineering practices, and communicate technical concepts. The role requires expertise in backend systems, distributed systems, public cloud, containerization, databases, orchestration, and CI/CD, with opportunities to support AI-focused products.

Own the technical foundation for building, packaging, executing, verifying, and scaling reinforcement learning environments. Design sandboxed execution, rollout orchestration, trajectory capture, verifier frameworks, environment versioning, and authoring tools. Build high-throughput infrastructure and reliable reward signals, instrument real applications, develop task suites, and defend against reward hacking. Provide staff-level technical leadership across engineering and research teams while shipping complex production systems.

Product Manager for Gen AI Platform to drive strategy, execute product roadmaps, and manage cross-functional teams while ensuring quality training data systems for AI models.

Research and develop novel post-training methods such as SFT, RLHF, reward modeling, and preference optimization for text and multimodal LLMs. Analyze model behavior, improve alignment and generalization, mitigate bias, enhance robustness, and publish findings at leading AI conferences. Collaborate with engineers, researchers, and foundation model labs to establish data-driven AI development practices and provide technical and strategic guidance.

Conduct research on evaluating frontier large language models and AI agents. Analyze model behavior, diagnose capability, reasoning, robustness, and alignment failures, and develop benchmarks for text and multimodal systems. Apply post-training techniques such as supervised fine-tuning, RLHF, and reward modeling to connect failures with training interventions. Collaborate with AI labs, define evaluation best practices, translate findings into technical strategy, and publish research at major conferences.

Build and own technical enablement for customer-facing engineers, including role-based onboarding, ramp criteria, knowledge-base governance, ongoing technical development, and readiness programs. Partner with engineering, enablement, product, sales, and delivery stakeholders; manage concurrent programs, track milestones, maintain documentation health, identify readiness gaps, and report program outcomes to leadership.

Lead cross-functional initiatives to drive multimillion-dollar revenue: partner with product and engineering, own data-labeling operations for strategic projects, provide executive updates, and solve high-impact operational and product problems.

Lead corporate and product communications for Scale’s enterprise AI business: craft narratives for product launches, customer and partner announcements, and executive visibility; develop media strategies and press materials; coordinate joint messaging with partners; build reporter relationships; collaborate across legal, policy, marketing, and social; prepare executives for external engagements; run multi-channel campaigns and monitor coverage to refine communications impact.

Own Scale’s end-to-end commercial acquisition of proprietary training data for AI environments. Identify high-value data domains and organizations, develop partnership and pricing structures with Legal, conduct outreach, negotiate and close transactions, and translate dataset requirements between commercial and Research teams. Build the acquisition function from the ground up, converting initial hands-on learnings into a repeatable pipeline.

The Research Scientist will design methods and experiments for AI control and monitoring, focusing on safety and robustness in advanced AI systems. Key tasks include developing monitoring techniques, researching layered control mechanisms, and collaborating with policymakers and industry experts.