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SambaNova builds the RDU, a dataflow accelerator for AI inference, and delivers it as rack-scale systems for on-premises deployment, as managed capacity in partner data centers (SambaManaged), and as an API (SambaCloud). Our customers are clouds, governments, and enterprises building token factories: data center capacity dedicated to running agentic AI workloads. The RDU's advantage is in decode, and it is increasingly deployed alongside GPUs that handle prefill.

SambaNova earns revenue from that advantage only if three things happen: the right models run on the platform at the right time, the surrounding ecosystem of serving engines, networking, storage, and orchestration treats RDU as a first-class target, and the company concentrates its engineering on the bets with the highest payoff. Core Product is the team responsible for those three things. It decides what SambaNova builds and why, grounds those decisions in benchmarks, competitive intelligence, and unit economics, and partners with engineering to see them through to release.

Responsibilities

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. This individual will be responsible for:

Market intelligence to product inputs

• 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.

Competitor specifications and roadmaps

• 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.

AI tooling for intelligence

• 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.

Segment and demand theses

• 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.

Intelligence cadence

• 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. Another team member manages the vendor relationships and access. This role sets the questions and turns the outputs into product inputs.

How We Measure This Role

We measure this role by the product, pricing, and investment decisions its analysis changes, not by the volume of reports or summaries produced. Automated tooling handles monitoring and first drafts. The person in this role is accountable for the conclusions SambaNova draws from them.

Basic Qualifications

• 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.

Additional Required Qualifications

• 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 Qualifications

• 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.