About the job
SambaNova is hiring a Principal Product Manager, Product Strategy and Competitive Intelligence to turn external market intelligence into SambaNova product decisions. The role converts third-party research, competitor disclosures, and benchmark data into roadmap, pricing, and positioning calls, and it builds the AI tooling that keeps that analysis current without a large team behind it. The person in this role should be able to build a TCO model from primary inputs, read both architecture specifications and financial statements, and be hands-on enough with AI tooling to automate their own research pipeline.
Responsibilities
Own the pipeline from third-party research (SemiAnalysis TCO, Tokenomics, Accelerator, and Datacenter models; competitor disclosures; benchmark data) to decision-ready analysis.
Maintain the house model of where RDU inference wins on cost per million tokens, at which latency tiers, for which workloads, and against which competitor configurations.
Convert TCO, tokenomics, and data center data into roadmap language: feature priorities, pricing moves, and segment bets.
Open each product review with a summary of market changes since the last quarter and their implications for SambaNova.
Track chip-level specifications for NVIDIA, AMD, Google TPU, AWS Trainium, Cerebras, Groq, and emerging accelerators: compute, memory hierarchy (SRAM, HBM, DDR), interconnect, power, process node, and packaging.
Track rack- and cluster-level design: scale-up and scale-out topology, networking (NVLink-class fabrics, Ethernet and InfiniBand, optics), storage, cooling, and rack power density.
Track roadmaps: announced parts, credible leaks, and supply signals such as HBM allocation, CoWoS capacity, and foundry node ramps.
When a spec or roadmap change moves the TCO or latency frontier, quantify the effect on SambaNova's positioning within a week.
Maintain a versioned spec comparison matrix with a changelog.
Build agentic pipelines that monitor research releases, competitor announcements, InferenceMAX and ClusterMAX updates, conference disclosures (Hot Chips, GTC, OCP), and regulatory and supply-chain filings.
Extract specs and claims into the comparison matrix automatically, with human review on anything that feeds a decision.
Automate the first draft of the weekly brief from monitored sources.
Treat the tooling as a product and iterate on precision, coverage, and detection latency.
Use data center capacity and power data to pressure-test the SambaManaged pipeline: which operators have stranded power and cooling suited to air-cooled RDU racks, where, and on what timeline.
Track workload mix shifts (agentic, chat, coding) and what they imply for decode-optimized positioning.
Keep a short list of falsifiable house theses and score them quarterly against outcomes.
Write a weekly two-page brief covering what changed, what it means, and which decision is needed from whom.
Produce a monthly deep dive tied to one live roadmap question.
Run a quarterly thesis review with the product and executive teams.
Set the analytical agenda for SambaNova's external research relationships, including the questions to raise on analyst calls and the custom data pulls to request.
Qualifications
Minimum
8+ years in semiconductor or AI infrastructure analysis, competitive intelligence, equity research, corporate or product strategy at a chip, cloud, or AI infrastructure company.
Has built quantitative market or economic models (accelerator forecasts, TCO, capacity and demand) from primary inputs.
Bachelor's degree in engineering, computer science, economics, or another quantitative field, or equivalent experience.
Fluent in accelerator and system architecture (compute, memory hierarchy, interconnect, power) and how each drives inference performance and cost.
Track record of translating competitor spec and roadmap changes into product, pricing, or investment decisions.
Hands-on with Python and LLM APIs, and able to build or direct agentic research tooling (extraction pipelines, monitoring agents) without waiting on an engineering team.
Concise, quantified, executive-ready writing with stated uncertainty.
Preferred
Direct experience with SemiAnalysis models (TCO, Tokenomics, Accelerator, ClusterMAX, InferenceMAX) or comparable institutional research.
Depth in inference economics: cost per million tokens, latency and throughput tradeoffs, prefill and decode disaggregation, and workload mix.
Rack- and cluster-level fluency: networking, optics, cooling, power density, and scale-up and scale-out topologies.
Has done capacity planning or GPU economics at a cloud, neocloud, or hyperscaler.
Has written an investment thesis that redirected a roadmap or a capital allocation, or built a decision framework that others kept running.
Willing to challenge a senior stakeholder's figures when the evidence supports it.