Senior Product Manager - Software
About the job
- At least 7 years of product management experience for developer-facing software platforms such as SDKs, compilers, runtimes, APIs, machine-learning tooling, or embedded developer ecosystems.
- Experience shipping tools and platforms used directly by external developers, including operating through the customer-zero phase.
- Demonstrated ownership of developer adoption, onboarding success, workflow completion, or related developer productivity metrics.
- Strong understanding of modern machine-learning inference and deployment pipelines such as PyTorch, ONNX, ExecuTorch, LiteRT, TensorRT, or equivalent.
- Experience translating customer workflows and technical constraints into actionable product requirements that engineering teams can implement without ambiguity.
- Familiarity with software performance tradeoffs involving memory, latency, throughput, power, and hardware utilization.
- Ability to work effectively with compiler, runtime, systems software, and silicon architecture teams and influence across them without authority.
- Ability to establish credibility with technical developers, compiler engineers, and machine-learning practitioners through clear thinking, technical depth, and customer empathy.
- Define the product roadmap for the compiler, SDK, runtime, model conversion and enablement tooling, and developer platform; set priorities based on customer requirements, competitive positioning, and strategic platform needs.
- Lead framework and model enablement strategy across PyTorch, ONNX, ExecuTorch, LiteRT, and future runtimes, including compatibility, validation coverage, performance expectations, and support commitments.
- Drive reduction in time-to-first-working-application (TTFWA); define the metric, establish a baseline, track it, and own roadmap items that improve it.
- Establish SDK governance, including surface area, versioning, API stability commitments, and deprecation policy; define release quality criteria, validation expectations, and backward compatibility standards.
- Drive developer confidence through reproducible benchmarking, deterministic tooling behavior, clear diagnostics, and strong documentation.
- Participate in customer engagements to understand workflow friction, deployment blockers, and framework requirements; represent software platform strategy in technical customer discussions and ecosystem partnerships.
- Gather and prioritize developer feedback across evaluation, onboarding, optimization, and deployment, and maintain structured mechanisms to feed it into roadmap decisions.
- Partner with Applications Engineering to define software platform capabilities for production-quality reference applications and customer evaluation flows, including AI inference, vision, audio, and sensor-processing pipelines.
- Define requirements for profiling, debugging, and performance-analysis workflows that help developers understand and optimize application behavior on Efficient hardware.
- Balance long-term platform investments against near-term customer enablement and revenue-driving requirements, and decide when to ship a polished narrow capability versus a rough broad one.
- Influence silicon decisions based on software needs and constraints; partner with the Hardware PM and architecture team when compiler or runtime requirements have silicon implications.
- Establish and own developer success and workflow metrics and diagnostics, including build success rates, TTFWA, workflow completion rate, and friction points.
- Define and own the SDK release cadence, packaging, distribution, and support lifecycle.
- Experience with compiler toolchains, intermediate representations (IRs), and deployment flows such as LLVM, MLIR, TOSA, XLA, or equivalent.
- Experience with embedded AI deployment pipelines and edge inference workflows.
- Experience at a semiconductor or AI hardware company building developer tools for a novel architecture.
- Familiarity with embedded software ecosystems such as FreeRTOS, CMSIS-NN, and microcontroller SDK patterns, as well as Linux-based embedded development environments and cross-compilation workflows.
- Experience with performance profiling tools, compiler traces, and benchmark-driven optimization workflows.
- Experience with benchmarking methodologies such as MLPerf, including designing, running, and publishing credible results.
- Experience operating in early-stage environments with incomplete requirements and rapidly evolving architectures.
- Experience in startup environments with hybrid product-management and developer-relations responsibilities.
About the company
Efficient Computer designs energy-efficient programmable processors for IoT edge devices and provides a toolchain that integrates with existing embedded languages and AI/ML frameworks. The processors support local edge computation to cut data transmission, extend battery life, and enable on-device processing and data compression with a developer-friendly toolchain. The company differentiates itself by prioritizing battery longevity and offering an end-to-end ecosystem that works with familiar programming environments rather than requiring new workflows. Its goal is to enable widespread, cost-effective edge deployments by dramatically reducing energy use and extending device lifetimes in IoT networks.
Company Size
51-200
Company Stage
Series B
Total Funding
$173M
Headquarters
Pittsburgh, Pennsylvania
Founded
2022
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Simplify's Take
What believers are saying
- September 29, 2026 Series B added $97 million at a $650 million valuation.
- TQ Ventures said customers are already building Electron E1 into physical AI products.
- Efficient says the new capital funds expanded shipments through 2027 and datacenter scaling.
What critics are saying
- No revenue disclosed on September 29, 2026; volume shipments still need customer pull-through.
- Intel, Nvidia, and entrenched MCU vendors can outspend Efficient on software ecosystems.
- If datacenter-class Fabric misses commercialization by 2027, Efficient stays a niche hardware startup.
What makes Efficient Computer unique
- Electron E1 shipped in volume by September 2026, not just prototypes.
- Efficient Labs, launched June 30, 2026, reduces embedded-development friction for Electron E1.
- Fabric architecture targets general-purpose programmability with accelerator-like energy savings, unlike fixed-function chips.
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Benefits
401(k) Company Match
401(k) Retirement Plan
Health Insurance
Paid Parental Leave
Flexible Work Hours
Growth & Insights and Company News
6 month growth
10%1 year growth
-3%2 year growth
10%Efficient Computer raises $97M at $650M valuation. -based Efficient Computer, Funding, raised $97M Series A+ from TQ Ventures. The round values the company at $650M. -based Efficient Computer, Funding, raised $97M Series A+ from TQ Ventures. Traded Editorial Efficient Computer raises $97M in a series a+ from TQ Ventures at a $650M valuation.
Efficient Computer, a chip startup spun out of Carnegie Mellon University, has raised $100 million in additional funding, bringing its valuation to $650 million. The Pittsburgh-based company is developing chips with data flow architectures that promise to be 10 to 100 times more energy-efficient than those used by Intel or Nvidia. The startup has raised $176 million to date. Whilst Efficient plans to eventually make large chips for data centres, its first products target drones and small robots. TQ Ventures led the funding round, which included participation from Eclipse, Union Square Ventures, Giant Ventures, Triatomic Capital, and others. Chief executive Brandon Lucia says the company has developed both hardware and software tools to make data flow chips suitable as general purpose processors.
Chip startup Efficient Computer raises $97 million at $650 million valuation. 29 Sep 2026 11:03PM (Updated: 29 Sep 2026 11:28PM) Add CNA as a trusted source to help Google better understand and surface our content in search results. (Corrects headline and paragraphs 1 and 9 to reflect that Efficient Computer raised $97 million, not $100 million) By Stephen Nellis SAN FRANCISCO, Sept 29: Efficient Computer, a chip startup spun out of Carnegie Mellon University, on Tuesday said it has raised $97 million in additional funding, bringing its valuation to $650 million. Efficient is developing chips with what is known as data flow architectures that can be both faster and 10 to 100 times more energy-efficient than the architectures used by Intel Corp or Nvidia Corp. Data-flow chips have bounced around academic literature for decades without commercial traction, largely because they are difficult for software developers to program for the wide variety of tasks that can be handled by chips from Intel or Nvidia. But booming demand for computing has spurred a new wave of interest in data-flow chips. Pittsburgh, Pennsylvania-based Efficient says it started from scratch in academic labs to develop both the hardware and software tools to make the data-flow approach suitable as a general purpose processor. While Efficient plans to eventually make large chips that can be used in data centers, the first chips that is shipping are aimed at drones and small robots. While those chips are tiny, cramming AI features into those battery-powered devices means the chips need to handle a greater variety of tasks than in the past. "We sort of thread the needle where we're easy to program, fast, and efficient," CEO Brandon Lucia said in an interview. "When you build an AI system, the system ends up doing a lot more than two little nano-optimized AI algorithms." Efficient did not disclose its revenues, but Andrew Marks, partner at TQ Ventures, which led the funding round, said the new cash will go toward increasing shipments through next year. "Plenty of chip startups have great architecture. Very few get silicon into customers' hands," Marks said. With the new capital, Efficient has raised $173 million to date. In addition to TQ Ventures, the funding round included participation from Eclipse, Union Square Ventures, Giant Ventures, Triatomic Capital, TO Capital, TF Capital, Mana Ventures, Toyota Ventures, Overmatch and Borderless.
PITTSBURGH, Sept. 29, 2026. Efficient Computer, the company building the world's most energy-efficient processors, today announced it entered into agreements for more than $97 million in Series B financing at a $650 million valuation, bringing its total funding raised to $173 million. The new round is led by TQ Ventures, with participation from Eclipse, Union Square Ventures, Giant Ventures, Triatomic Capital, TO Capital, TF Capital, Mana Ventures, Toyota Ventures, Overmatch, and Borderless. Efficient will use the capital to ship the Electron E1 processor in volume to lead customers, and to scale the architecture to datacenter-class performance at more than 10x improvement in energy consumption than systems built today.
The Securities and Exchange Commission has not necessarily reviewed the information in this filing and has not determined if it is accurate and complete.The reader should not assume that the information is accurate and complete.