Sales Executive, TorchBridge

Job Description

As Sales Executive for TorchBridge, you will sell to some of the most technically demanding buyers in enterprise technology: AI engineering teams, MLOps leads, and infrastructure and platform leaders at organizations spending serious money on GPU compute and actively looking for a way to reduce their hardware dependency on a single vendor.Your buyers have lived with NVIDIA lock-in. They know what it costs, they know what the alternatives look like, and they have probably already evaluated at least one competitor. They are not going to be moved by a pitch deck. They are going to be moved by a technically credible conversation about what TorchBridge actually does, why the competition's single-backend or inference-only approach falls short for their workloads, and what a real proof-of-concept benchmark looks like on their hardware.Your job is to earn that conversation, run it with authority, and convert it into a deal. Then do it again.

ABOUT TORCHBRIDGE
TorchBridge is a hardware abstraction layer (HAL) for PyTorch. It lets AI engineering teams write training and inference code once and run it unchanged on NVIDIA, AMD, AWS Trainium, Google TPU, and CPU, without modifying a single line or maintaining separate deployment configurations per hardware target.The business problem it solves is expensive and growing: AI teams locked into NVIDIA face GPU scarcity and pricing power with no practical exit. Hardware changes require months of rewriting. Each vendor's SDK, compiler, memory manager, and optimization pipeline are incompatible with the others. Operational overhead compounds as teams maintain separate Dockerfiles, CI pipelines, and monitoring for each hardware target.TorchBridge eliminates all of that. It is the only solution on the market providing a complete HAL across NVIDIA, AMD, TPU, and Trainium with full training and inference support, validated on six hardware platforms, and shipping production-ready CLI, Docker, CI/CD, monitoring, and model serving infrastructure out of the box. The market it operates in is $182B today and growing to an estimated $466B by 2030, with $8B+ in VC flowing to competitors who each cover at most one or two backends. None of them cover all four with training support. TorchBridge does.

Job Requirement

  • Prospect, qualify, and own the full sales cycle for TorchBridge across AI engineering organizations, MLOps teams, AI infrastructure and platform groups, and the FinOps and CTO-level leaders who approve infrastructure spend at organizations running significant PyTorch workloads on GPU clusters
  • Build relationships across the technical and business buying committee: ML engineers and MLOps practitioners who evaluate the tooling, infrastructure architects who assess integration and deployment, and VPs of Engineering, CTOs, and FinOps leads who approve the spend
  • Lead technically credible discovery conversations that surface the specific costs of hardware lock-in for each prospect: rewrite costs when hardware changes, operational overhead of maintaining parallel deployment configurations, GPU cost exposure from single-vendor dependency, and efficiency losses from tooling fragmentation
  • Work with Solution Engineers to execute TorchBridge proof-of-concept engagements: cross-backend benchmark runs on the customer's actual hardware, CI/CD validation demonstrations, and MTTR-reduction scenarios that quantify the business value of hardware portability in concrete terms
  • Build and present commercial proposals that tie TorchBridge's value to measurable outcomes: GPU cost reduction through hardware diversification, engineering time saved through unified tooling, and risk reduction through multi-backend portability
  • Engage confidently with the competitive landscape: you know exactly why Modular covers inference only, why vLLM has no auto-backend detection, why Lightning's HAL is shallow with no kernel-level dispatch, why DeepSpeed is NVIDIA-centric with weak AMD support, and why none of them cover Trainium or TPU alongside NVIDIA and AMD with training support
  • Maintain accurate, up-to-date pipeline data in the CRM and meet or exceed quarterly revenue targets
  • Represent CloudlyIO at AI infrastructure, MLOps, GPU computing, and open-source developer events and communities where TorchBridge's target buyers gather
  • Relay buyer feedback, competitive intelligence, and market observations to the TorchBridge product and engineering teams
YOU MAY BE A GOOD FIT IF YOU HAVE

  • 2 to 3 years of B2B sales experience in developer tools, AI/ML infrastructure, cloud computing, or enterprise platform software
  • Genuine familiarity with AI infrastructure: you understand GPU compute, PyTorch workloads, Kubernetes deployment, and why hardware vendor lock-in is a real and growing cost for organizations building production AI systems
  • Proven track record of meeting or exceeding sales targets in a technical enterprise sales environment, with specific examples you can speak to
  • Ability to hold a technically credible conversation with a senior ML engineer or infrastructure architect without needing the Solution Engineer to translate everything
  • Experience connecting deeply technical infrastructure value propositions to the financial and strategic business case that VP and CTO-level buyers need to justify investment
  • Comfort navigating multi-stakeholder technical sales cycles that involve both engineering evaluators and executive decision-makers on different timelines
  • Proficiency with CRM platforms such as HubSpot or Salesforce and a disciplined approach to pipeline management

PREFERRED QUALIFICATIONS
  • Existing relationships within AI engineering, MLOps, GPU infrastructure, or cloud platform communities
  • Experience selling developer tools, open-source enterprise products, or AI/ML infrastructure platforms
  • Familiarity with competing tools including Modular/MAX, vLLM, HuggingFace Optimum, Lightning AI, DeepSpeed, or Fireworks, and their specific limitations
  • Knowledge of hardware vendor dynamics: NVIDIA pricing and availability, AMD MI300X/MI350X competitive positioning, AWS Trainium pricing, and Google TPU economics
  • Experience with FinOps practices and how organizations quantify and justify GPU infrastructure spend decisions
  • Familiarity with cloud partner ecosystems such as AWS, Google Cloud, or NVIDIA and co-selling motion experience
  • Bachelor's degree in Computer Science, Engineering, Business, or a related field

COMPENSATION & BENEFITS
  • Salary: Competitive base, negotiable based on experience
  • Performance-based commission structure: your earnings scale directly with your results
  • Two annual festive bonuses, each equivalent to half a month's salary
  • Two-day weekends, 10 days casual leave, 10 days sick leave, and 14 public holidays per CloudlyIO's global holiday calendar for Bangladesh
  • Fully subsidized lunch and evening snacks, plus tea and coffee throughout the day
  • Direct collaboration with US clients and teams, with real exposure to global enterprise AI deals from day one