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.

Role Description At Lumen, we are building the digital backbone for the AI economy. As we accelerate our AI ambitions, we are looking for an experienced enterprise leader who can turn fragmented efforts into a disciplined system for prioritizing, building, governing, and scaling AI. In this role, you will define how AI work gets done across Lumen by establishing the operating model, governance, standards, and execution discipline that move initiatives from idea to pilot to production and scale. This is not a centralized AI delivery role. It is a high-impact leadership opportunity for a strategist and operator who can help teams across the company build AI consistently, responsibly, and with measurable business value. Main Responsibilities - Define and evolve the enterprise AI operating model - Define how AI opportunities are identified, prioritized, governed, built, scaled, and measured across the enterprise - Establish clear roles, decision rights, lifecycle stages, and accountability across functions - Build and run the AI portfolio system - Create a transparent enterprise view of AI initiatives, including value, ownership, stage, risks, and dependencies - Lead portfolio review mechanisms that support prioritization, investment decisions, and trade-offs - Drive prioritization and decision discipline - Partner with AI, engineering, finance, HR, and business leaders to focus resources on the highest-value work - Apply rigorous decision frameworks across feasibility, readiness, reuse, risk, and sequencing - Establish governance-by-design - Establish risk-based governance that enables speed within clear guardrails - Embed responsible AI practices into the delivery lifecycle rather than relying on post-hoc review - Define AI standards and quality bar - Set enterprise standards for LLMs, agents, evaluation, monitoring, reliability, and human oversight in partnership with engineering - Define a shared enterprise quality bar for AI performance, safety, and business impact - Enable reuse and enterprise scale - Drive a build-once, reuse-many approach through shared patterns, agents, workflows, APIs, and reference architectures - Reduce duplication and strengthen enterprise-wide AI capability - Design hybrid human-agent operating models (with HR) - Partner with HR to define how teams evolve from people-only models to people-plus-agent operating models - Clarify ownership, accountability, and decision-making in hybrid work environments - Provide internal and external AI thought leadership - Shape executive decision frameworks and the enterprise AI narrative - Translate AI strategy into clear, credible messaging for internal and external audiences Qualifications - 10+ years of experience in AI, digital, or technology leadership - 5+ years leading complex, cross-functional AI initiatives across business, engineering, and data organizations - Deep expertise in modern AI, including LLMs, agents, evaluation, orchestration, monitoring, and production trade-offs - Proven ability to design scalable execution systems, including operating models, governance frameworks, technical standards, portfolio management, value tracking, and executive decision forums - Strong business and technical judgment across AI, engineering, product, and data - Ability to balance speed, quality, risk, reuse, and enterprise impact - Create clarity and momentum in ambiguous situations - Influence senior leaders and stakeholders without direct authority - Excellent communication skills, with the ability to translate complex AI concepts into clear, executive-ready narratives - Credibility as an internal and external AI thought leader Compensation This information reflects the anticipated base salary range for this position based on current national data. Minimums and maximums may vary based on location. Individual pay is based on skills, experience and other relevant factors. - $174,876 - $233,168 in these states: AL, AR, AZ, FL, GA, IA, ID, IN, KS, KY, LA, ME, MO, MS, MT, ND, NE, NM, OH, OK, PA, SC, SD, TN, UT, VT, WI - $183,621 - $244,827 in these states: CO, HI, MI, MN, NC, NH, NV, OR, RI - $192,364 - $256,486 in these states: AK, CA, CT, DC, DE, IL, MA, MD, NJ, NY, TX, VA, WA Benefits - Comprehensive package featuring a broad range of Health, Life, Voluntary Lifestyle benefits and other perks that enhance your physical, mental, emotional, and financial wellbeing.