{"id":2936484,"url":"https://alion.io/job/neurophos-seniorstaff-applied-scientist-numerical-optimization-quantization","title":"Senior/Staff Applied Scientist, Numerical Optimization & Quantization","company":{"id":678766,"name":"Neurophos","domain":"neurophos.com","url":"https://alion.io/company/neurophos","size_band":"201-500","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"staff","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Austin, United States","Sunnyvale, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":170000,"max":240000,"currency":"USD","period":"year","gross":null,"usd_annual":240000},"salary_estimate":null,"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":true,"technologies":[{"name":"Diffusion Models","optional":false},{"name":"JAX","optional":false},{"name":"Machine Learning","optional":false},{"name":"Post-training","optional":false},{"name":"PyTorch","optional":false},{"name":"Quantization","optional":false},{"name":"TensorFlow","optional":false},{"name":"Triton","optional":false},{"name":"LLM","optional":true}],"status":"live","first_seen_at":"2026-08-21T00:32:39Z","employer_posted_date":"2026-08-21","last_verified_at":"2026-11-01T10:20:44Z","board_verified":true,"closed_at":null,"days_open":72,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":72},"description":"About Neurophos\nThe demand for new data centers and AI compute is rapidly outpacing the planet's energy capacity. Digital solutions are hitting a power wall as we approach the physical limits of traditional silicon. Conquering this bottleneck means rethinking the fundamental architecture of inference compute. The industry's current path can't meet the need, so we're taking a different approach.\nInstead of traditional electronic circuits, we use silicon photonics and an active, programmable metasurface to perform matrix multiplications at the speed of light. Our optical cells are 10,000x smaller than traditional photonic components, enabling unprecedented density. By using photonics instead of electricity, our chips become more efficient as they scale. This architecture will deliver up to 100 times the energy efficiency of existing solutions while significantly improving performance for large-scale AI inference.\nWe’ve assembled a world-class team of industry veterans and recently raised a $110M Series A led by Gates Frontier. Participants include M12 (Microsoft’s Venture Fund), Carbon Direct Capital, Aramco Ventures, Bosch Ventures, Tectonic Ventures, Space Capital, and others.\nJoin us and shape the future of computing!\nLocation: Austin, TX or Sunnyvale, CA. Full-time onsite position.\nReports To: Mathematics and Quantization Lead\nFLSA Status: Exempt\nPosition Overview\nWe are seeking an experienced machine learning scientist to develop advanced post-training quantization methods for large language models (LLMs), diffusion models, and other ML applications for our revolutionary optical inference engines. This role is critical to demonstrating the full potential of our metamaterial-based optical processing units (OPUs) by adapting state-of-the-art AI models to leverage our ultra-high-throughput, low-precision compute architecture.\nThe ideal candidate will bridge the gap between cutting-edge ML research and novel hardware capabilities, ensuring customers can seamlessly deploy their AI workloads on Neurophos hardware.\nKey Responsibilities\nDevelop and execute hardware-aware post-training methods for full model quantization.\n\nInvestigate preconditioning and formulate quantization as non-convex, discrete, constrained, or second-order optimization and develop practical solutions.\n\nContribute to refining Neurophos's quantization strategy.\n\nDesign controlled numerical experiments to understand potential improvements and secondary effects due to analog processing hardware.\n\nBuild research-quality implementations and reproducible experiment harnesses for testing candidate methods.\n\nAdapt models from open-source repositories and customer private models.\n\nWork with models in various formats, including PyTorch, Triton, JAX, and emerging frameworks.\n\nDesign and execute re-quantization, retraining, and other model adaptation techniques to minimize accuracy loss during precision reduction.\n\nOptimize GEMM operations for high-throughput execution.\n\nCollaborate with hardware, software, and architecture teams to co-optimize model architectures for optical compute characteristics.\n\nPublish research papers on novel optimization techniques and methodologies, with appropriate IP protection.\n\nQualifications\nPhD, or equivalent research experience, in machine learning, applied mathematics, optimization, numerical analysis, computer science, or a closely related field.\n\n5+ years of experience in machine learning, with at least 3 years focused on model optimization and deployment.\n\nResearch or advanced engineering experience in neural network quantization, model compression, numerical optimization, or efficient inference.\n\nStrong knowledge of numerical linear algebra, including matrix factorizations, conditioning, covariance estimation, and iterative methods.\n\nExperience with one or more of non-convex optimization, discrete optimization, manifold optimization, second-order methods, or constrained optimization.\n\nStrong proficiency in PyTorch and familiarity with other ML frameworks, including JAX, Triton, and TensorFlow.\n\nHands-on experience with transformer architectures, LLMs, and diffusion models.\n\nExperience designing controlled numerical experiments and distinguishing algorithmic improvements from calibration or benchmark artifacts.\n\nStrong written communication and research collaboration skills.\n\nPreferred Skills\nExperience with low-precision inference optimization (INT8, FP8, or lower).\n\nBackground in analog or optical computing architectures.\n\nKnowledge of in-memory computing paradigms and matrix-vector multiplication acceleration.\n\nKnowledge of randomized numerical linear algebra, sketching, or structured transforms.\n\nPublications in quantization, optimization, numerical linear algebra, model compression, or efficient ML.\n\nExperience with vector quantization, lattice methods, learned codebooks, or rate-distortion ideas.\n\nExperience with large-scale batch inference optimization.\n\nFamiliarity with prefill versus decode optimization strategies in LLM inference.\n\nExperience conducting experiments on models large enough to expose scaling and generalization problems.\n\nWhat We Offer\nThis is an opportunity to play a pivotal role in an innovative startup redefining the future of AI hardware. Work on game-changing technology at the intersection of photonics and AI as part of a collaborative, brilliant team. You’ll contribute to a platform that redefines computational performance and accelerates the future of artificial intelligence. Come help us bring this transformative technology to the world.\nBenefits\nJoin a team that invests in your future and your well-being. At Neurophos, we offer:\n100% coverage of base health plan premiums for you and your dependents, plus HSA contributions.\n\nUnlimited PTO. No rigid vacation banks, just a focus on delivery.\n\n401(k) matching and stock option opportunities to ensure our success is your success.\n\nFull suite of voluntary benefits, including Dental, Vision, Life, Hospital, Critical Illness, and Accident insurance.\n\nPersonalized Benefits. Choose the plans that fit your life and take the cash back for those that don’t.","description_format":"text","description_chars":6132,"description_truncated":false,"requirements":{"experience_years_min":5,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"phd","optional":false},"security_clearance":false,"languages":[]},"benefits":["Unlimited PTO"],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Processors, MCUs & AI Chips","AI Chips & Accelerators"],"lifecycle":[{"event":"open","at":"2026-10-20T16:12:15Z"}],"visa":[{"country":"US","licensed_sponsor":true,"evidence":"H-1B filings in 12 months: 1","filings_12m":1,"filings_prev_12m":0,"green_card_filings_12m":0,"median_offered_wage_usd":312500,"route":null,"cap_exempt":false,"checked_at":"2026-10-29T16:27:21+00:00","sources":["US Department of Labor: LCA disclosure data (H-1B, H-1B1, E-3)"],"filings_for_role_12m":1}],"liveness":{"score":28,"band":"fade","label":"Fading","p_open":1,"p_active":0.765,"p_room":0.36,"age_days":72,"expected_fill_days":43,"reasons":["conf:3","win:tail","crowd:"],"computed_at":"2026-11-01T06:10:25Z"},"pay":{"stated_usd_annual":240000,"is_top_pay":false},"html_url":"https://alion.io/job/neurophos-seniorstaff-applied-scientist-numerical-optimization-quantization","json_url":"https://alion.io/job/neurophos-seniorstaff-applied-scientist-numerical-optimization-quantization.json","meta":{"generated_at":"2026-11-01T12:22:11Z","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":19043,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-11-02T00:00:00Z"}},"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":678766},"rest":"https://alion.io/mcp/rest/get_company?id=678766"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fneurophos-seniorstaff-applied-scientist-numerical-optimization-quantization"},{"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%2Fneurophos-seniorstaff-applied-scientist-numerical-optimization-quantization"},{"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%2Fneurophos-seniorstaff-applied-scientist-numerical-optimization-quantization"},{"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/neurophos-seniorstaff-applied-scientist-numerical-optimization-quantization\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fneurophos-seniorstaff-applied-scientist-numerical-optimization-quantization"}],"agent":{"mcp":"https://alion.io/mcp/jobs","mcp_open":"https://alion.io/mcp/open","next":[{"tool":"get_job","arguments":{"id":2936484}},{"tool":"search_jobs","arguments":{"company":"neurophos.com","detail":"compact"}},{"tool":"match_people","arguments":{"job_id":2936484},"note":"people open to offers who fit this vacancy"}],"note":"The same data through MCP tools: https://alion.io/mcp/open needs no sign-in (compact searches, postings, people); https://alion.io/mcp with an Alion account has everything. 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