Drive Capital
Location New York City; London Employment Type Full time Location Type On-site Department Hardware Compensation $234,001 - $298,252 • Offers Equity We are committed to competitive and equitable compensation based on role, skills, and experience. Salary ranges are guidelines, with final compensation varying by role, experience, and location and reviewed regularly for fairness. Normal Computing Incredible Opportunities The Normal Team builds foundational software and hardware that help move technology forward, supporting the semiconductor industry, critical AI infrastructure, and the broader systems that power our world. We work as one team across New York, San Francisco, Copenhagen, Seoul, and London. Your Role in Our Mission Look at the AI accelerator roadmaps coming out of every major silicon company right now and you will notice something strange: they are all building the same chip. Bigger systolic arrays. More HBM. More of the same architecture, scaled harder. The industry has placed a collective bet that the way to win the next decade of AI inference is to refine the GPU paradigm until it cannot be refined any further. We know that bet is wrong. Normal is building ASICs purpose-built for image and video diffusion inference, grounded in the physics of computation rather than the assumptions everyone else has inherited. The compute substrate has to be invented, not specified, and we are looking for the person who wants to help invent it. You will work directly alongside our lead architect and research engineers, contributing across the full architecture stack: compute core microarchitecture, memory subsystem, interconnect, and the FPGA prototyping that proves the decisions before silicon. The team is small. The scope is wide. The architecture is being shaped now, not refined, and your contributions will be visible in the chip when it tapes out. If the appeal of working on a chip that has to be invented is greater to you than iterating on one that already exists, keep reading. Responsibilities Help define the architecture and microarchitecture of novel AI accelerator compute blocks. PE array design, datapath organization, and support for efficiency techniques such as sparsity exploitation and reduced-precision computation. The compute tile is the surface where Normal's research advantages have to show up in silicon, and you are one of the people responsible for making sure they do. Translate workload analysis and research findings into hardware specifications. Identify where architectural innovation creates the most leverage, define the structures that realize it, and produce microarchitecture documents unambiguous enough for RTL engineers to implement against. You work closely with them through implementation, not over the wall from it. Reason across the full stack and defend PPA tradeoffs at every level. Move between algorithm-level workload behavior, memory hierarchy, on-chip interconnect, and physical design constraints. Make the call when the data is incomplete, and articulate why under scrutiny from the lead architect and the research team. Partner with the compiler lead on ISA co-design. The compute tile must be compilable and programmable, not just simulatable. The programming model and the microarchitecture are defined together, and you are accountable for both sides meeting in the middle. Own the FPGA prototyping work. Scope what the FPGA implementation actually proves, drive the implementation through to bring-up, and use it to de-risk architecture decisions before tapeout. You decide which questions are worth answering in FPGA versus cycle accurate simulation. Stay current with the AI accelerator research landscape and be able to articulate clearly where Normal's approach differs from existing solutions and why that matters. This is a research adjacent seat and you are expected to read, not just consume. What We're Looking For A degree in Electrical Engineering, Computer Engineering, Computer Science, or equivalent work experience. PhD welcome but not required; the bar is the work, not the credential. Substantial experience in architecture or microarchitecture of high-performance digital systems. AI accelerators, compute engines, or similarly complex logic. You have shaped the structures inside a chip, not just consumed them from the outside. Fluency moving between algorithm-level analysis and hardware specification. You can read a profile of a workload and translate it into datapath widths, pipeline stages, and area/power estimates without losing the thread on either side. Experience with simulation driven architecture. You have used cycle accurate or analytical models to make and defend design decisions before RTL exists, and you know which questions each tool can answer and which it cannot. Familiarity with quantization and reduced precision approaches for inference and their implementation implications. You understand the cost of a bit at the hardware level, not just the model level. Experience writing microarchitecture specifications and working closely with RTL engineers through implementation. Your specs are read, not just filed. Proficiency in Python or C++ for performance modeling and analysis, and familiarity with SystemVerilog or equivalent RTL. Comfort operating in an environment where the architecture is actively being discovered alongside the work. You do not need the answer to be already known to make progress on it. Mindset and Impact This role is not for everyone. You will spend years on a chip that does not exist yet, defending decisions that cannot be fully validated until silicon comes back, on a bet most of the industry has not made. The people who thrive here, run toward the hard stuff. They are steady in ambiguity, comfortable when the data is incomplete, and they take the hardest problem on the board first because that is where the answer is hiding. They are not waiting for the architecture to be handed to them. They want to be the ones who define it. If you have spent your career suspecting you are one of those people, this is where you find out. The chip you help build will be the silicon behind a generation of image and video AI - the work people watch, the work people create with, the work that shapes what the next decade of visual computing looks like. Equal Employment Opportunity Statement Normal Computing is an Equal Opportunity Employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status, or any other legally protected status. Accessibility Accommodations Normal Computing is committed to providing reasonable accommodations to individuals with disabilities. If you need assistance or an accommodation due to a disability, please let us know at . Privacy Notice By submitting your application, you agree that Normal Computing may collect, use, and store your personal information for employment related purposes in accordance with our Privacy Policy. Compensation Range: $234,001 - $298,252
Location New York City; London Employment Type Full time Location Type On-site Department Hardware Compensation $234,001 - $298,252 • Offers Equity We are committed to competitive and equitable compensation based on role, skills, and experience. Salary ranges are guidelines, with final compensation varying by role, experience, and location and reviewed regularly for fairness. Normal Computing Incredible Opportunities The Normal Team builds foundational software and hardware that help move technology forward, supporting the semiconductor industry, critical AI infrastructure, and the broader systems that power our world. We work as one team across New York, San Francisco, Copenhagen, Seoul, and London. Your Role in Our Mission Look at the AI accelerator roadmaps coming out of every major silicon company right now and you will notice something strange: they are all building the same chip. Bigger systolic arrays. More HBM. More of the same architecture, scaled harder. The industry has placed a collective bet that the way to win the next decade of AI inference is to refine the GPU paradigm until it cannot be refined any further. We know that bet is wrong. Normal is building ASICs purpose-built for image and video diffusion inference, grounded in the physics of computation rather than the assumptions everyone else has inherited. The compute substrate has to be invented, not specified, and we are looking for the person who wants to help invent it. You will work directly alongside our lead architect and research engineers, contributing across the full architecture stack: compute core microarchitecture, memory subsystem, interconnect, and the FPGA prototyping that proves the decisions before silicon. The team is small. The scope is wide. The architecture is being shaped now, not refined, and your contributions will be visible in the chip when it tapes out. If the appeal of working on a chip that has to be invented is greater to you than iterating on one that already exists, keep reading. Responsibilities Help define the architecture and microarchitecture of novel AI accelerator compute blocks. PE array design, datapath organization, and support for efficiency techniques such as sparsity exploitation and reduced-precision computation. The compute tile is the surface where Normal's research advantages have to show up in silicon, and you are one of the people responsible for making sure they do. Translate workload analysis and research findings into hardware specifications. Identify where architectural innovation creates the most leverage, define the structures that realize it, and produce microarchitecture documents unambiguous enough for RTL engineers to implement against. You work closely with them through implementation, not over the wall from it. Reason across the full stack and defend PPA tradeoffs at every level. Move between algorithm-level workload behavior, memory hierarchy, on-chip interconnect, and physical design constraints. Make the call when the data is incomplete, and articulate why under scrutiny from the lead architect and the research team. Partner with the compiler lead on ISA co-design. The compute tile must be compilable and programmable, not just simulatable. The programming model and the microarchitecture are defined together, and you are accountable for both sides meeting in the middle. Own the FPGA prototyping work. Scope what the FPGA implementation actually proves, drive the implementation through to bring-up, and use it to de-risk architecture decisions before tapeout. You decide which questions are worth answering in FPGA versus cycle accurate simulation. Stay current with the AI accelerator research landscape and be able to articulate clearly where Normal's approach differs from existing solutions and why that matters. This is a research adjacent seat and you are expected to read, not just consume. What We're Looking For A degree in Electrical Engineering, Computer Engineering, Computer Science, or equivalent work experience. PhD welcome but not required; the bar is the work, not the credential. Substantial experience in architecture or microarchitecture of high-performance digital systems. AI accelerators, compute engines, or similarly complex logic. You have shaped the structures inside a chip, not just consumed them from the outside. Fluency moving between algorithm-level analysis and hardware specification. You can read a profile of a workload and translate it into datapath widths, pipeline stages, and area/power estimates without losing the thread on either side. Experience with simulation driven architecture. You have used cycle accurate or analytical models to make and defend design decisions before RTL exists, and you know which questions each tool can answer and which it cannot. Familiarity with quantization and reduced precision approaches for inference and their implementation implications. You understand the cost of a bit at the hardware level, not just the model level. Experience writing microarchitecture specifications and working closely with RTL engineers through implementation. Your specs are read, not just filed. Proficiency in Python or C++ for performance modeling and analysis, and familiarity with SystemVerilog or equivalent RTL. Comfort operating in an environment where the architecture is actively being discovered alongside the work. You do not need the answer to be already known to make progress on it. Mindset and Impact This role is not for everyone. You will spend years on a chip that does not exist yet, defending decisions that cannot be fully validated until silicon comes back, on a bet most of the industry has not made. The people who thrive here, run toward the hard stuff. They are steady in ambiguity, comfortable when the data is incomplete, and they take the hardest problem on the board first because that is where the answer is hiding. They are not waiting for the architecture to be handed to them. They want to be the ones who define it. If you have spent your career suspecting you are one of those people, this is where you find out. The chip you help build will be the silicon behind a generation of image and video AI - the work people watch, the work people create with, the work that shapes what the next decade of visual computing looks like. Equal Employment Opportunity Statement Normal Computing is an Equal Opportunity Employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status, or any other legally protected status. Accessibility Accommodations Normal Computing is committed to providing reasonable accommodations to individuals with disabilities. If you need assistance or an accommodation due to a disability, please let us know at . Privacy Notice By submitting your application, you agree that Normal Computing may collect, use, and store your personal information for employment related purposes in accordance with our Privacy Policy. Compensation Range: $234,001 - $298,252
Drive Capital
About the Role As the Senior Manager of Global Payroll Systems & Equity Operations at HTS, you will serve as a player-coach reporting to the Head of People Operations & Workforce Technology to modernize and scale our workforce compensation infrastructure. Sitting on the leadership team, your mandate is to transition the function away from manual, multi-vendor handoffs into an automated, highly scalable operating model that maximizes systemic efficiency without proportional headcount growth. This high-visibility role uniquely blends global cross functional system design with hands on execution to directly mitigate operational and financial risk. What would your day to day look like Own the end to end global payroll operating model across employees, contractors, EORs, PEOs, and local providers. Manage and develop a team of 2-3 direct reports while maintaining execution discipline and high operational standards. Lead the operational administration of employee equity in Shareworks, covering grant processing, data updates, and multi department reconciliation. Identify and implement automation workflows to minimize manual data transfers, recurring exceptions, and data friction. Partner with HR Technology, IT, and Finance to drive payroll platform consolidations and maximize system reliability. Strengthen compliance protocols, payroll controls, and documentation to protect the business from financial exposure and ensure audit readiness. Establish clear processing calendars, accountability metrics, and data scorecards to measure function accuracy and capacity gains. Step into complex tax, equity, and market specific payroll challenges to drive swift cross functional resolution. An ideal candidate has Global Payroll Leadership: Proven experience managing multi country payroll operations, specifically navigating EOR, PEO, and local provider frameworks. Equity Administration Expertise: Direct familiarity handling equity platforms like Shareworks, including managing grant lifecycles and complex equity tax coordination. Systems & Automation Focus: A strong track record of designing HRIS integrations, optimizing workflows, and intentionally removing manual touchpoints from financial operations. Controls & Compliance Rigor: Deep knowledge of payroll controls, financial audit readiness, and tax implications within a high growth technology or fintech corporate structure. Player Coach Capability: The demonstrated capability to manage and upskill a remote operational team while simultaneously acting as a hands on technical expert. Commercial & Analytical Mindset: The ability to convert complex operational patterns into clear, data driven decisions that improve company wide scaling efficiency. Benefits Well funded and proven startup with large ambitions, competitive salary, upsides of pre IPO equity packages. Hopper covers 100% of the premiums for the employee for a group insurance plan through Vitality Health. Automatic contributions when you start with Hopper through Smart Pension. Please ask us about our very generous parental leave, much above industry standards. Access to co working space on demand through FlexDesk and a work from home stipend. Carrot Cash travel stipend. Unlimited PTO. Entrepreneurial culture where pushing limits and taking risks is everyday business. Open communication with management and company leadership. Small, dynamic teams = massive impact.
About the Role As the Senior Manager of Global Payroll Systems & Equity Operations at HTS, you will serve as a player-coach reporting to the Head of People Operations & Workforce Technology to modernize and scale our workforce compensation infrastructure. Sitting on the leadership team, your mandate is to transition the function away from manual, multi-vendor handoffs into an automated, highly scalable operating model that maximizes systemic efficiency without proportional headcount growth. This high-visibility role uniquely blends global cross functional system design with hands on execution to directly mitigate operational and financial risk. What would your day to day look like Own the end to end global payroll operating model across employees, contractors, EORs, PEOs, and local providers. Manage and develop a team of 2-3 direct reports while maintaining execution discipline and high operational standards. Lead the operational administration of employee equity in Shareworks, covering grant processing, data updates, and multi department reconciliation. Identify and implement automation workflows to minimize manual data transfers, recurring exceptions, and data friction. Partner with HR Technology, IT, and Finance to drive payroll platform consolidations and maximize system reliability. Strengthen compliance protocols, payroll controls, and documentation to protect the business from financial exposure and ensure audit readiness. Establish clear processing calendars, accountability metrics, and data scorecards to measure function accuracy and capacity gains. Step into complex tax, equity, and market specific payroll challenges to drive swift cross functional resolution. An ideal candidate has Global Payroll Leadership: Proven experience managing multi country payroll operations, specifically navigating EOR, PEO, and local provider frameworks. Equity Administration Expertise: Direct familiarity handling equity platforms like Shareworks, including managing grant lifecycles and complex equity tax coordination. Systems & Automation Focus: A strong track record of designing HRIS integrations, optimizing workflows, and intentionally removing manual touchpoints from financial operations. Controls & Compliance Rigor: Deep knowledge of payroll controls, financial audit readiness, and tax implications within a high growth technology or fintech corporate structure. Player Coach Capability: The demonstrated capability to manage and upskill a remote operational team while simultaneously acting as a hands on technical expert. Commercial & Analytical Mindset: The ability to convert complex operational patterns into clear, data driven decisions that improve company wide scaling efficiency. Benefits Well funded and proven startup with large ambitions, competitive salary, upsides of pre IPO equity packages. Hopper covers 100% of the premiums for the employee for a group insurance plan through Vitality Health. Automatic contributions when you start with Hopper through Smart Pension. Please ask us about our very generous parental leave, much above industry standards. Access to co working space on demand through FlexDesk and a work from home stipend. Carrot Cash travel stipend. Unlimited PTO. Entrepreneurial culture where pushing limits and taking risks is everyday business. Open communication with management and company leadership. Small, dynamic teams = massive impact.