Top Tech Jobs & Startup Jobs in NYC, NY

Manage a team to train state-of-the-art AI models, work with clients on solutions, and improve products with data-driven insights.

As a Software Engineer for the ARC team, you'll design and implement secure backend systems, manage model inference layers, improve engineering standards, and collaborate with teams to meet government agency needs.

As a Software Engineer in Frontier AI Infrastructure, you'll design secure systems for the public sector, improve engineering standards, and collaborate with cross-functional teams to meet government requirements. You'll also participate in customer engagements and contribute to the product strategy while ensuring compliance with federal regulations.

The role involves developing data solutions, collaborating with customers, and applying rigorous data science to affect business outcomes. Responsibilities include designing and deploying data solutions, conducting experiments, and influencing product strategy.

Manage the engineering team, drive technical delivery, oversee product lifecycle, build features, and mentor engineers. Collaborate with cross-functional teams and ensure high-quality product releases in a fast-paced environment.

Lead the development of evaluation methodologies for large language models, collaborating with teams and publishing research findings. Enforce best practices in AI development.

The Technical Lead Manager will build and optimize Scale's ML training and inference platform, collaborating with ML teams to advance model and data curation technologies.

The Product Marketing Manager will develop messaging for enterprise offerings in Healthcare, Insurance, and Financial Services, create marketing assets, and drive marketing programs to enhance engagement and awareness.

The Senior Infrastructure Software Engineer will architect and scale multi-cloud infrastructure, focusing on compliance, observability, and developer efficiency for enterprise AI deployments.

The role involves designing and maintaining ML infrastructure systems for compute allocation and scheduling, improving reliability of workloads, and collaborating with ML engineers to enhance system efficiency.