--- title: 'Member of Technical Staff, Kernels at Magic' canonical: 'https://feeny.ai/job/member-of-technical-staff-kernels-magic-san-francisco-j9gvfjz3n9ch' type: 'job' last_seen: '2026-09-09' --- # Member of Technical Staff, Kernels at Magic - **Company:** Magic - **Location:** San Francisco, CA - **Compensation:** $225k–$550k - **Employment:** full-time - **Work type:** onsite - **Posted:** 2024-01-24 - **Last confirmed live:** 2026-09-09 - **Apply:** https://jobs.ashbyhq.com/magic.dev/42010571-7943-43d5-b182-c77d8342b088 ## Job description Magic’s mission is to build safe AGI that accelerates humanity’s progress on the world’s most important problems. We believe the most promising path to safe AGI lies in automating research and code generation to improve models and solve alignment more reliably than humans can alone. Our approach combines frontier-scale pre-training, domain-specific RL, ultra-long context, and inference-time compute to achieve this goal. ## ABOUT THE ROLE As a Kernel Engineer, you will design, implement, and maintain high-performance kernels to optimize throughput and latency during training and inference. Magic's long-context windows create distinct kernel optimization challenges around memory utilization, data movement, and sustained throughput. ## WHAT YOU'LL WORK ON - Design and implement kernels that support high-performance long-context behavior - Ownership of kernel design, implementation, deployment, and production reliability - Focus on robustness, extensive testing, and functional correctness, while pushing on performance - Evaluate porting Magic’s compute kernels to alternative hardware options - Co-design kernels with understanding and interaction with training, inference, and RL teams - For a sample of our work, see Magic-Attention, presented at GTC 2026 https://www.nvidia.com/gtc/session-catalog/sessions/gtc26-s82294/ ## WHAT WE’RE LOOKING FOR - Low-level programming experience targeting AI accelerators such as NVIDIA Blackwell or Google TPUs - Develop and optimize GPU kernels in frameworks such as NCCL https://developer.nvidia.com/nccl, MSCCLPP https://github.com/microsoft/mscclpp, CUTLASS https://github.com/NVIDIA/cutlass, CuTeDSL https://docs.nvidia.com/cutlass/latest/media/docs/pythonDSL/cute_dsl.html, Triton https://github.com/triton-lang/triton, Quack https://github.com/Dao-AILab/quack, Flash-Attention https://github.com/Dao-AILab/flash-attention, and similar frameworks - Experience in other kernel authoring frameworks such as Pallas https://docs.jax.dev/en/latest/pallas/index.html/Mosaic (GPU https://docs.jax.dev/en/latest/pallas/gpu/index.html or TPU https://docs.jax.dev/en/latest/pallas/gpu/index.html), or Mojo https://www.modular.com/blog/matrix-multiplication-on-nvidias-blackwell-part-1-introduction also maps well to the work on Magic's kernel team - Strong depth over shallow breadth: for kernel engineering, we prefer candidates with deep expertise in computer architecture, low-level machine optimizations, and code generation, with breadth across ML - Agility, ownership mindset, and grit ## OUR CULTURE - Integrity. Words and actions should be aligned - Hands-on. At Magic, everyone is building - Teamwork. We move as one team, not N individuals - Focus. Safely deploy AGI. Everything else is noise - Quality. Magic should feel like magic Magic strives to be the place where high-potential individuals can do their best work. We value quick learning and grit just as much as skill and experience. ## COMPENSATION, BENEFITS AND PERKS (US) - Annual salary ranges between $275K - $550K based on experience - Equity is a significant part of total compensation, in addition to salary - 401(k) plan with 6% salary matching - Generous health, dental and vision insurance for you and your dependents - Unlimited paid time off - Visa sponsorship and relocation stipend to bring you to SF, if possible - A small, fast-paced, highly focused team ## About Magic ## Company Overview - **One-liner**: Magic is building frontier-scale code models to automate software engineering and research, with the goal of achieving safe AGI. - **Entity Type**: Private (Series B) - **Headquarters**: San Francisco, California, United States - **Founded**: 2022 - **Founders**: Eric Steinberger, Sebastian De Ro ## Core Business - **Primary industry/industries**: Artificial Intelligence, Software Development - **Target customers**: B2B, Enterprise (via code generation models and APIs) - **Mission or purpose statement**: To safely deploy AGI by automating AI research and code generation, aligning models more reliably than humans can alone. ## Products & Services - **LTM (Long Term Memory) Models**: A family of frontier-scale LLMs with ultra-long context windows. LTM-1 had a 5,000,000 token context window, and LTM-2-Mini has a 100,000,000 token context window. These models are designed to understand and generate code across entire codebases. - **AI Software Engineer**: An internal and product-facing system aimed at autonomously performing software engineering tasks, from code generation to complex research. ## Market Standing - **Valuation/Market Cap**: Not publicly disclosed. - **Key Metric**: Total Funding of **$515 million** (across Seed, Series A, and Series B rounds). - **Notable Investors/Partners**: Nat Friedman, Daniel Gross, CapitalG (Alphabet’s independent growth fund), Elad Gil, Sequoia Capital, Jane Street, Eric Schmidt. - **Growth Signals**: - Rapid headcount growth: 79 employees in 2025 (up ~100% YoY from ~55 in 2024). - Partnership with Google Cloud (announced in conjunction with 100M token context windows and new funding). - Operates in 12 countries (including Austria, Switzerland). ## Competitive Advantages - **Ultra-Long Context Windows**: Their proprietary LTM architecture (100M token context) is a significant moat, enabling the model to process entire code repositories in a single pass. - **Infrastructure Scale**: Possesses "thousands of GB200s" (NVIDIA GPUs) for frontier-scale pre-training, a massive capital advantage. - **Small, High-Caliber Team**: A deliberate strategy of maintaining a small team of engineers and researchers to focus on a "short list of fundamental research problems." - **Direct Path to AGI Thesis**: Uniquely focused on using code generation as both the product and the core research path to AGI, differentiating from broader AI labs. ## Strategic Focus - **Automate AI Research**: Using code models to autonomously design, develop, and evaluate new alignment techniques. - **Safety & Alignment**: Actively publishing an "AGI Readiness Policy" and engaging in safety research, viewing alignment as a core technical problem to be solved by AI. - **Post-Training & Applied Team**: Recently announced an Applied Team focused on post-training to explore practical applications of their unreleased LTM2 models. - **Security Standards**: Advocating for and implementing cyber, physical, and information security standards comparable to the defense and nuclear industry. ## Why Work Here - **Mission-Driven**: A small team with a shared belief in the positive potential of responsibly deployed AGI. The work is directly tied to solving fundamental research problems. - **Culture & Values**: Emphasizes integrity, hands-on work ("everyone is building"), teamwork, focus, and quality. - **Compensation & Benefits**: - Competitive salary with significant equity compensation. - Unlimited paid time off. - 401K with 6% salary matching. - Health, dental, and vision insurance for employees and dependents. - Access to mental health, financial wellbeing, and telehealth programs. - Office catering (chef-prepared lunch and dinner). - **Work Environment**: In-person team meetings and office work are emphasized. The hiring process includes a take-home assessment and an in-person team meeting. - **Hiring Process**: Designed to be fair and focused on potential, not just resume credentials. Includes an application review, interview with a senior team member, take-home assessment (~5-8 hours), in-person team meeting, and reference check. ## Sources 1. [magic.dev](https://magic.dev/) 2. [magic.dev/careers](https://magic.dev/careers) 3. [magic.dev/safety](https://magic.dev/safety) 4. [linkedin.com/company/magicailabs](https://www.linkedin.com/company/magicailabs) 5. 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