Technical Account Manager

Lambda•San Francisco, CA

8d•$231,000 - $342,000•Hybrid

About The Position

The Technical Account Manager owns the technical health of the post-sales relationship for Lambda’s public cloud accounts, spanning AI-native startups, Enterprises, and Fortune 500 companies. Where the Customer Success Manager owns the commercial health of an account, you own its technical health: the customer’s workloads run well, the architecture is right, the SLA story is defensible, and technical risk is found and retired before it threatens revenue. Solutions Engineering carries the account through pre-sales and hypercare; at handoff, you take ownership of the technical relationship for the life of the contract. This is a hands-on role, not a coordination role. You will understand what customers are actually building (training runs, fine-tuning pipelines, inference services) deeply enough to lead joint POC sessions, design and defend architectures, validate SLA events at the root-cause level, and build the tooling that makes account health measurable. You will be the customer’s most credible technical advocate inside Lambda and Lambda’s most trusted technical voice inside the account.

Requirements

5+ years in technical account management, solutions engineering or architecture, ML engineering, technical program or product management, or infrastructure engineering with significant customer-facing scope, in cloud, HPC, or AI infrastructure.
Hands-on fluency with GPU infrastructure: able to provision, benchmark, and debug across compute, networking (InfiniBand, Ethernet), storage, and schedulers (Slurm, Kubernetes), and to read results critically.
Working command of AI/ML workloads (training, fine-tuning, inference) sufficient to map a customer’s stack, identify constraints, and lead technical conversations with their ML and infrastructure engineers.
Track record leading structured technical engagements: POCs with defined success criteria, architecture designs, benchmark programs, or high-severity escalations.
A builder’s toolkit: scripting, SQL, and dashboarding, with a history of turning operational data into tools other people depend on.
Executive-grade communication of deeply technical content, in writing and in the room.
Comfort with ambiguity and a track record of building methodology where none exists.

Nice To Haves

Experience at an AI cloud, neocloud, or hyperscaler serving large-scale GPU or HPC customers.
Applied LLM experience (fine-tuning, RAG systems, or inference serving) that mirrors the workloads Lambda customers run.
Depth in the NVIDIA ecosystem: NIM and NeMo, Base Command / BCM, the CUDA stack, DGX-class systems.
Familiarity with SLA structures, service credits, enterprise contract mechanics, and retention metrics (NRR/GRR).
Product management or TPM background with experience converting customer evidence into roadmap decisions.

Responsibilities

Own the technical health of your accounts. Take the technical handoff from Solutions Engineering at the end of hypercare and own the account’s technical outcomes through steady state, expansion, and renewal. Know the state of every cluster and workload you are accountable for, and keep your commercial counterparts ahead of technical risk.
Understand customer AI use cases end to end. Map what it means for each customer to train, fine-tune, and serve models on Lambda: frameworks, schedulers, parallelism strategy, data paths, and performance baselines. Build the customer user journey and convert it into value-add opportunities across the platform, documentation, and escalation routing.
Lead joint customer POC sessions. Define success criteria with the customer before a node is provisioned: acceptance thresholds, benchmarks, timelines. Own the execution plan, coordinate capacity and provisioning, run or oversee the tests, and drive the POC to a clear verdict: win the workload, close the gap through product, or qualify out.
Lead customer architecture designs. Produce and defend reference architectures spanning compute, networking, storage, connectivity, and scheduler integration (Slurm, Kubernetes). Make support boundaries explicit: what is managed and what is not. Own the design as it evolves after handoff, pulling in engineering domain experts with specific, well-framed questions.
Own SLA and reliability engineering. Build and own the canonical methodology for uptime, downtime, and credit calculation. Validate breach events at the technical level, down to the specific Ethernet or InfiniBand failure, and arm CSMs and leadership with defensible numbers. Partner with product to standardize SLA language and structure across 1CC, on-demand, and reserved offerings.
Build the tooling that makes accounts measurable. Own the technical data surfaces for customer health end to end: dashboards, telemetry and uptime history views, health scoring, and churn early-warning signals. Scope, build, and drive adoption. Replace “escalate to engineering to answer a basic question” with self-serve data for the whole GTM org.
Direct technical escalations and incidents. Serve as the technical lead during high-severity events on your accounts: drive root cause, hold the quality bar on RCAs, coordinate engineering, support, and vendors (NVIDIA, storage, networking), and give account teams a technically accurate narrative. Run proactive maintenance and known-issue communication so customers hear about problems from Lambda first.
Drive the technical voice of the customer. Run a structured feature-request pipeline into product with committed triage timelines. Audit the platform hands-on by provisioning as a customer and testing known friction points. Lead product-led POCs (for example, NVIDIA NIM) that open new value for customers.
Know the market technology landscape. Track GPU roadmaps, competing clouds and neoclouds, and the evolving training and inference stacks. Brief customers on what is coming and internal teams on where Lambda stands, and let that context shape architecture and expansion recommendations.