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Computer Operator in New York

17 active opportunities · Updated October 2026

Explore current computer operator jobs in New York. Filter by work mode, employment type, experience, department, date posted and distance.

L
📍 New York, NY, United States· Full-time
✓ High-confidence listing
Quick readStrong listing-quality and freshness signals

At Lyft, our purpose is to serve and connect. We aim to achieve this by cultivating a work environment where all team members belong and have the opportunity to thrive. Lyft Business is changing the way companies, brands and organizations leverage the Lyft platform to redefine the way they operate and move people around. We are solving big problems, and meeting the needs of our clients and the people they care about in a whole new way. We realize that the world has changed so much recently, and that the needs of our customers and how they envision transportation is changing too. Our work is fun, challenging and rewarding and we are looking for customer and growth-focused team members passionate about solving problems and delighting our customers. As an Account Manager, you’ll help our clients understand the power of Lyft Business. You will build and manage a named account list of large organizations in the US, ensuring that they leverage Lyft’s products for any transportation objective they have. We’re building the next great transportation platform, and we need world-class talent and candidates that aren’t afraid to try new things and think outside the box to build new business. Responsibilities: Own the customer relationship with enterprise grade partners as their primary representative to Lyft for all their business related transportation needs Partner with Fortune 500 companies, to design, sell and implement custom transportation solutions for their respective passengers that include employees (essential and non), recruits, VIPs, guests, subscribers, etc Identify and generate additional revenue streams within existing accounts, including up-sells and cross-sells; selling across commuter benefits, events, employee perks, concierge, and corporate travel use cases to exceed revenue targets Generate and maintain an active pipeline with meticulous attention to opportunity staging, close dates, revenue forecasts, and deal documentation Constantly ‘se

AIGoMarketingHR
M
📍 New York, new york, United States· Full-time
✓ Quality checkedCompany trend -63%

About Us: AI needs a new infrastructure layer. We're building it at Modal. Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now. Our customers include category-defining companies like Lovable , Ramp , Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale. We recently raised a $355M Series C at a $4.65B valuation, led by General Catalyst and Redpoint Ventures. We've crossed $300M+ ARR and grown fivefold since September. Our team includes creators of popular open-source projects (e.g., Seaborn , Luig i ), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience. The Role: We are looking for strong engineers with experience in making ML systems performant at scale. If you are interested in contributing to open-source projects and Modal’s container runtime to push language and diffusion models towards higher throughput and lower latency, we’d love to hear from you! Requirements: 5+ years of experience writing high-quality, high-performance code. Experience working with torch, high-level ML frameworks, and inference engines (vLLM or TensorRT). Familiarity with Nvidia GPU architecture and CUDA. Experience with ML performance engineering (tell us a story about boosting GPU performance — debugging SM occupancy issues, rewriting an algorithm to be compute-bound, eliminating host overhead, etc). Nice-to-have: familiarity with low-level operating system foundations (Linux kernel, file systems, containers, etc).

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