Senior Solution Engineer

About the role

You will own the technical articulation of Lambda's value proposition for the world's most ambitious AI teams. You will architect and sell GPU cloud infrastructure that turns abstract compute demand into reliable, high-performance production environments. Working at the intersection of sales and engineering, you will translate complex customer requirements into executable blueprints that de-risk deployment and accelerate adoption. You will act as the trusted technical advisor who guides enterprises through every layer of the AI stack from networking to orchestration. You will partner closely with revenue leaders to ensure each engagement moves the strategic needle for both the customer and Lambda. You will champion data-driven decision-making by rigorously validating performance claims through hands-on testing and real-world benchmarks.

Key facts

What you'll do

• Drive technical sales and executive influence by engaging with C-suite and architecture leaders to frame large-scale GPU cloud opportunities.
• Partner with Account Executives to lead complex deals with large enterprises and digital native businesses and build trusted relationships with technical leaders (CTOs, Heads of AI/ML, Platform Leads).
• Evaluate customer architectural needs, uncover potential bottlenecks, and design end-to-end GPU cloud solutions that align with workload requirements and business outcomes.
• Author comprehensive proposals and architecture diagrams and collaborate with teams on Bill of Materials (BOMs), and rack elevations for multi-node GPU clusters.
• Lead hands-on proof-of-concept (PoC) activities and benchmarking for customers to validate hypotheses and reduce procurement risk.
• Design, execute, and deliver technical PoCs and custom prototypes to demonstrate Lambda's performance, reliability, and value across diverse deployment scenarios.
• Run benchmark evaluations across training and inference workloads to show tangible performance and cost advantages over competitors using real-world metrics.
• Architect and optimize AI and ML workloads by guiding enterprise engineering teams on structuring their AI lifecycle from data ingestion and distributed training (SLURM, Kubernetes) to inference optimization (vLLM, TensorRT-LLM) and observability.
• Provide architectural guidance on high-performance networking including InfiniBand, RoCE, distributed storage, and cluster topologies to ensure maximum GPU utilization and resilience.
• Champion customer feedback and product advocacy by serving as the technical voice of the customer internally and highlighting field insights and product gaps.
• Create field enablement assets such as technical whitepapers, architectural blueprints, and lead technical workshops for prospective clients and partners.
• Represent Lambda as a subject matter expert at industry conferences, webinars, and technical community events to elevate brand credibility.
• Reinforce Lambda's culture by contributing positively across the organization and maintaining a high level of agility and responsiveness.
• Hyper-focus on customer satisfaction by anticipating needs and delivering outcomes that reinforce trust and long-term partnerships.
• Drive continuous improvement in sales tools, technical collateral, and playbooks based on feedback from customers and internal stakeholders.

Requirements

• Have a proven track record deploying, benchmarking, and optimizing workloads on NVIDIA GPU architectures (e.g., HGX platforms, NVLink) using deep learning frameworks (PyTorch, NeMo) and inference engines (vLLM, TensorRT-LLM).
• Have 8+ years of experience designing, deploying, and scaling enterprise cloud infrastructure across multiple environments and use cases.
• Have 4+ years in a Solution Architect, Solution Engineer, or technical customer-facing capacity supporting complex cloud environments with demanding performance and reliability expectations.
• Have 3+ years of hands-on experience architecting and deploying cloud-based AI and ML workloads including data pipelines, training loops, and inference services.
• Have strong experience with modern infrastructure orchestration tools such as Kubernetes, Docker, SLURM, Terraform, and Ansible to automate and manage scalable pipelines.
• Have deep knowledge of cloud networking concepts including high-speed interconnects such as InfiniBand and RoCE, distributed file systems like NFS, NVMe-oF, Weka, and VAST, as well as security and cost optimization strategies.
• Have experience coding in Python, Go, C/C++, or CUDA to debug performance issues and validate technical integrations.
• Have experience partnering with Account Executives to close complex cloud deals, present technical architectures to C-level stakeholders (CTOs, VP of Eng), and drive customer alignment through structured discovery and solutioning.
• Have demonstrated impact at an organizational or multi-departmental level by leading cross-functional initiatives and mentoring junior SEs or architects.
• Thrive in dynamic settings by embracing radical ownership of initiatives and outcomes while maintaining clarity under pressure and shifting priorities.

Nice to have

• Direct experience with end-to-end LLM fine-tuning, algorithm selection, pipeline design, or distributed training setups (3D parallelism, Megatron-LM) that align with advanced model training strategies.
• Prior experience with product launches, leading GTM initiatives, or publishing technical whitepapers and benchmarks that establish thought leadership.
• Experience integrating RESTful APIs, gRPC, and service-oriented cloud architectures to enable scalable and resilient solutions.

Practical notes

• This role requires presence in our San Francisco, San Jose, or Bellevue office location 4 days per week; Lambda's designated work from home day is currently Tuesday.
• This is a full-time position with standard employment terms and expectations.
• Compensation details are not included in this listing.
• No specific visa or travel deadlines are published in this source document.

Salary range information

The annual salary range for this position has been set based on market data and other factors. However, a salary higher or lower than this range may be appropriate for a candidate depending on relevant skills and experience.