{"id":3064086,"url":"https://alion.io/job/normal-computing-research-engineer-algorithms","title":"Research Engineer, Algorithms","company":{"id":39034,"name":"Normal Computing","domain":"normalcomputing.com","url":"https://alion.io/company/normalcomputing","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":{"grade":"A","score":95,"open_postings":3,"ghost_share":0,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":67,"computed_at":"2026-10-23T05:54:15Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Mountain View, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":227000,"max_usd":500000,"period":"year","method":"role_country_seniority_unknown","sample_n":4194},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"KV Cache","optional":false},{"name":"LLM","optional":false},{"name":"Python","optional":false},{"name":"Quantization","optional":false},{"name":"Machine Learning","optional":true}],"status":"live","first_seen_at":"2026-10-22T06:36:54Z","employer_posted_date":"2026-10-22","last_verified_at":"2026-10-23T16:31:47Z","board_verified":true,"closed_at":null,"days_open":1,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":1},"description":"Normal Computing | Build with Us\nNormal is an applied AI company solving the hardest problems in AI and silicon. We build foundational hardware and software for the semiconductor industry, critical AI infrastructure, and the broader systems that power our world, in partnership with the world's most advanced institutions. We work as one team across New York City, Silicon Valley (Mountain View), London, Copenhagen, and Seoul.\nThe Role\nNormal's hardware runs the most memory-bound parts of large-model inference inside memory itself, rather than shuttling weights to a processor. The software that makes this programmable doesn't exist yet in any standard form. You will develop the computational methods that make LLM inference run efficiently on it. The core challenge is rethinking how operations like attention, memory access, and long-context decoding should behave on hardware that computes inside memory, rather than adapting standard GPU kernels to a new chip.\nYour job is to develop the algorithms that exploit this natively: understand which parts of LLM workloads are best suited to in-memory execution, design numerical methods that map onto the hardware's physical behavior, and validate them against our simulators and FPGA prototypes first, and against silicon as it arrives.\nThis is a co-design role. The hardware and the algorithms are developed in parallel, which means you will influence architectural decisions rather than implement against a fixed specification. The strongest candidates have a deep understanding of both large model inference and the mathematics of stochastic systems, and have built systems that run on real hardware.\nWhat You Will Own\nAlgorithm Development: Develop algorithms for LLM inference workloads running on our in-memory hardware. Success means algorithms that measurably improve LLM inference on our hardware, demonstrated in simulation and then on silicon.\n\nSoftware/Hardware Co-Design: Work directly with hardware and architecture teams to shape what the chip can and should compute natively.\n\nNumerical Methods: Design numerical methods that exploit thermal noise and analog dynamics rather than working around them.\n\nEvaluation & Benchmarks: Build evaluation frameworks and benchmarks that characterize algorithm behavior on real hardware or simulation.\n\nWorkload Translation: Translate insights about model workloads into constraints and opportunities for hardware design.\n\nRapid Prototyping: Prototype and iterate rapidly as hardware evolves from simulation to silicon.\n\nPerformance Optimization: Optimize performance at both the gate level and the algorithmic level.\n\nWhat Makes You a Great Fit\nDeep understanding of large model inference: attention mechanisms, KV cache, long-context decoding, memory bandwidth constraints\n\nExperience with inference optimization: quantization, sparsity, kernel fusion, or memory-efficient attention\n\nFamiliarity with stochastic systems, probabilistic methods, numerical analysis, or analog computation\n\nExperience implementing algorithms close to hardware, beyond high-level frameworks\n\nComfort reasoning from first principles about what a novel substrate can do efficiently\n\nTrack record of taking ideas from theory to working implementation on real hardware\n\nStrong programming skills in Python and at least one systems language\n\nCollaborative instinct and ability to work across hardware, architecture, and software teams\n\nBonus Points\nPhD in machine learning, applied mathematics, physics, electrical engineering, or a related field\n\nExposure to analog or mixed-signal systems, in-memory compute, or non-von-Neumann architectures\n\nExperience working on hardware that did not yet exist when you joined\n\nPublications or open-source work in efficient inference, stochastic algorithms, or novel computing\n\nEqual Employment Opportunity Statement\nNormal 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.\nAccessibility Accommodations\nNormal 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 .\nPrivacy Notice\nBy submitting your application, you agree that Normal Computing may collect, use, and store your personal information for employment-related purposes in accordance with our Applicant Privacy Notice.","description_format":"text","description_chars":4688,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"phd","optional":false},"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[{"name":"United States","iso":"US","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Processors, MCUs & AI Chips","EDA Software","AI Chips & Accelerators"],"lifecycle":[{"event":"open","at":"2026-10-22T07:19:08Z"}],"visa":[{"country":"US","licensed_sponsor":true,"evidence":"H-1B filings in 12 months: 1","filings_12m":1,"filings_prev_12m":3,"green_card_filings_12m":0,"median_offered_wage_usd":262500,"route":null,"cap_exempt":false,"checked_at":"2026-10-15T11:24:14+00:00","sources":["US Department of Labor: LCA disclosure data (H-1B, H-1B1, E-3)"],"filings_for_role_12m":0}],"liveness":{"score":90,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.903,"p_room":1,"age_days":0,"expected_fill_days":67,"reasons":["conf:3","velocity","win:early"],"computed_at":"2026-10-23T05:54:15Z"},"pay":null,"html_url":"https://alion.io/job/normal-computing-research-engineer-algorithms","json_url":"https://alion.io/job/normal-computing-research-engineer-algorithms.json","meta":{"generated_at":"2026-10-23T18:07:22Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler_verified","counted_by":"address","units_charged":1,"used_today":27606,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-24T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.02,"usd":0.002,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":39034},"rest":"https://alion.io/mcp/rest/get_company?id=39034"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fnormal-computing-research-engineer-algorithms"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":1,"usd":0.1},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Fnormal-computing-research-engineer-algorithms"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.02,"usd":0.002},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Fnormal-computing-research-engineer-algorithms"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/normal-computing-research-engineer-algorithms\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fnormal-computing-research-engineer-algorithms"}]}