Role Description We're hiring Engineers dedicated to building AI-powered tools that change how work gets done inside the company. You'll embed with teams across operations, customer success, and finance, find the work that's slow and manual, and ship AI agents and automations that actually get adopted. This is a build-fast, talk-to-humans, own-the-problem role - not a research role and not a pure-infrastructure role. You'll define what "internal AI" looks like here from scratch. Who this is for - You're energized by ambiguity and ownership. - You'd rather ship a rough tool this week, watch someone use it, and improve it than spend a month on a perfect spec. - You like sitting with a non-technical teammate, understanding their actual workflow, and turning it into something that saves them hours. - You're comfortable being the only person who owns a problem end to end. Who you will work with - Partner closely with the CEO and founders on identifying the highest-leverage operational problems to solve. - Work alongside functional leaders across the business to understand their priorities, surface opportunities, and align on what to build. - Collaborate with Operations, Customer Success, Finance, People/Talent Acquisition, Engineering, IT, and Data teams. What you'll actually build - An agent that drafts customer success replies from our knowledge base and ticket history. - A Slack-based workflow that routes and pre-fills finance approvals. - An internal eval harness so we can trust the LLM features we ship. - A tool that lets the ops team query [internal system] in plain language. Qualifications - 1–3 years building software — backend services, internal tools, automations, or API-driven apps. Internships and co-ops count. - Strong in TypeScript and/or Python. - Experience building something with LLMs — an agent, a RAG pipeline, a copilot, a prompt-driven tool — through work, a side project, or coursework. - Comfortable wiring systems together: APIs, webhooks, SaaS integrations, async workflows. - Ability to talk to non-technical stakeholders, figure out what they really need, and drive adoption. - Ability to move fast under ambiguity and iterate from real feedback. Bonus, not required - Built copilots, agents, or workflow automations before. - Used an orchestration framework (LangChain, Temporal, or similar). - Worked with vector databases or retrieval systems. - Integrated with Salesforce, Slack, Zendesk, Notion, or Jira. - Startup / high-growth experience. What success looks like - First few weeks: shipped your first internal tool and it's in active daily use by a business team. - Within 2 months: 2–3 tools live, and stakeholders are coming to you with ideas. - By month 3: you've set the team's baseline practices for evals, prompt management, and safe LLM deployment. Compensation Compensation for this role is based on market data and takes into account a variety of factors, including location, skills, qualifications, and prior relevant experience. Salary is just one part of the total rewards package at Qventus. Salary Range: $120,000 - $150,000 USD Benefits - Open Paid Time Off. - Paid parental leave. - Professional development. - Wellness and technology stipends. - A generous employee referral bonus. - Employee stock option awards. Equal Opportunity Employment Qventus values diversity in its workforce and proudly upholds the principles of Equal Opportunity Employment. We welcome all qualified applicants and ensure fair consideration for employment without discrimination based on any legally protected characteristics.

Role Description As a Member of Technical Staff, you will be a foundational member of our small, multi-disciplinary R&D team. We are looking for 'first principles' thinkers who are excited to tackle the hardest, most ambiguous technical challenges at the intersection of AI, physics, and computer architecture. You will be responsible for driving invention, prototyping, and validation of the core components of our novel computing platform. Your work will be fluid and could span from theoretical modeling and simulation to algorithm development, hardware/software co-design, or experimental validation in collaboration with other team members. We're hiring exceptional problem-solvers who can navigate deep uncertainty and help chart our technical roadmap. Qualifications - Exceptional technical ability in a quantitative field (e.g., Physics, Computer Science, Electrical Engineering, Applied Math, or a related discipline). An MS/PhD or equivalent research/project experience is strongly preferred. - A "0-to-1" mindset. You have a demonstrated history of tackling complex, ambiguous R&D problems, often from a blank slate. - Deep curiosity. You are comfortable diving into new domains, whether it's semiconductor physics, machine learning theory, or systems-level design. - A creative and unconventional approach to problem-solving. Requirements - Core competences in the analysis of nonlinear dynamical systems (ODEs, PDEs, SDEs), ideally with experience analyzing the stability, noise robustness, and capacity of such systems. - The ability to leverage analytic insights to build practical tools – metrics, algorithmic optimizations, and automated analyses – that can be used to study dynamical systems. - Strong command of Python and expertise with using numeric computing and visualization libraries, such as numpy, scipy, and matplotlib. - Familiarity with dynamics-based ML model architectures, such as diffusion models and energy-based models, and general experience with ML model training flows. - Experience with using high-level ML model frameworks, such as PyTorch and JAX. Bonus Points (Nice to Have) - Compute model technical staff may focus primarily on the above skillsets, or may be cross-disciplinary with hardware or AI/ML algorithms expertise. - Excellent ability to translate complex technical concepts for diverse teams. - You will act as a translator, discussing algorithmic/model trade-offs with ML/AI teams and eliciting hardware constraints and features from hardware engineering teams. Benefits - A comprehensive package including best-in-class health benefits. - 401k matching. - Truly unlimited PTO. - Complimentary meals in our Palo Alto office.

Role Description As a Member of Technical Staff, Language & Reasoning Models, you will drive the development of foundational language and reasoning models that fundamentally leverage the dynamics of our novel silicon. Your goal is to map the behaviors of modern language models directly onto the physics of our hardware. You will sit at the intersection of NLP/reasoning research and hardware codesign, proving that high-fidelity, large-scale language understanding and generation can be achieved natively on an unconventional computing substrate. What You'll Do - Model Development: Design, train, and scale next-generation language and reasoning architectures (such as transformers, state space models, diffusion/flow models, and deep equilibrium models) specifically tailored for unconventional compute. - Physics-Informed Architecture: Rethink standard sequence modeling to exploit the continuous-time dynamics of silicon, moving away from layers of inefficient digital abstraction. - Evaluation & Scaling: Establish the training recipes, loss functions, and evaluation metrics needed to reach the frontier of language comprehension, logical reasoning, and generation speed while maintaining the massive energy efficiency of our platform. - Extreme Codesign: Collaborate with hardware designers and theorists, and system builders to co-design the model architecture alongside the underlying physical compute primitives. Qualifications - Education: An MS/PhD or equivalent research/project experience in a quantitative field such as AI/Machine Learning, Computer Science, Physics, Electrical Engineering, or Applied Math. - Experience: Deep, hands-on expertise in the theory, architecture, and training of modern foundation models (transformers, SSMs, text diffusion/flow, etc.). - Systems Fluency: Hands-on, battle-tested experience dealing with model scaling. You have successfully designed and executed full-scale, distributed training runs for large language or reasoning models, managing the complexities of massive compute clusters. - Software Development: You are fluent in modern deep learning frameworks (PyTorch or JAX) and have a proven track record of writing clean, scalable training code for large language models. Requirements - Unconventional Experience: As a bonus, you may have experience working with hardware-in-the-loop training, mixed-signal hardware, quantization, or physics-informed neural networks. Benefits - A comprehensive package including best-in-class health benefits. - 401k matching. - Truly unlimited PTO. - Complimentary meals in our Palo Alto office.

Role Description - Define, architect, and implement comprehensive verification plans and environments for IP blocks and full chip from specification to tape-out. - Help build verification infrastructure from ground zero to flexible and nimble environments, able to move quickly and scale for growth. - Take full ownership of the verification lifecycle, including the development and maintenance of scalable testbenches, sophisticated test cases, and robust verification environments. - Deploy industry-standard methodologies (UVM/SystemVerilog) to conduct constrained-random stimulus generation, coverage-driven verification, and assertions. - Collaborate closely with digital and analog design teams, as well as software and systems teams, to define verification requirements, debug complex hardware/software interactions, and ensure design integrity. - Perform exhaustive analysis of functional and code coverage. Debug failures at the RTL and gate level to ensure 100% verification closure and high-quality silicon. Qualifications - Bachelor’s degree in Electrical Engineering, Computer Engineering, Computer Science, or a related field, or equivalent practical experience. - Minimum of 8 years of hands-on experience with advanced verification methodologies and languages, specifically UVM and SystemVerilog. - Proven track record of developing and maintaining complex verification testbenches and automated test environments. - Proficiency in scripting languages (e.g., Python, Shell, or Bash) for automation of verification flows and result analysis. - Strong familiarity with general-purpose operating systems such as Linux, and industry standard tools from companies such as Cadence and Synopsys. Requirements - Master's degree or Ph.D. in Electrical Engineering or Computer Science, with a specific emphasis on computer architecture (preferred). - Expert-level knowledge of UVM architecture and high-level behavioral modeling (preferred). - Experience with modern AI-driven code generation tools (preferred). Benefits - A comprehensive package including best-in-class health benefits. - 401k matching. - Truly unlimited PTO. - Complimentary meals in our Palo Alto office.