{"id":827764,"url":"https://alion.io/job/radixark-member-of-technical-staff-inference-multimodal-diffusion","title":"Member of Technical Staff — Inference-Multimodal & Diffusion","company":{"id":686458,"name":"RadixArk","domain":"radixark.com","url":"https://alion.io/company/radixark","size_band":null,"is_staffing_agency":false,"is_intermediary":false,"ats_vendor":"Greenhouse","truth_index":{"grade":"C","score":55,"open_postings":20,"ghost_share":0.75,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-09-24T05:45:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"staff","employment_type":null,"work_mode":"hybrid","remote_scope":null,"hiring_geo_confidence":"structured","locations":["Palo Alto, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":200000,"max":400000,"currency":"USD","period":"year","gross":null,"usd_annual":400000},"salary_estimate":null,"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Diffusion Models","optional":false},{"name":"GitHub","optional":false},{"name":"JAX","optional":false},{"name":"LLM","optional":false},{"name":"Multimodal AI","optional":false},{"name":"PyTorch","optional":false},{"name":"SGLang","optional":false},{"name":"TPU","optional":false}],"status":"live","first_seen_at":"2026-02-17T10:33:07Z","employer_posted_date":"2026-08-12","last_verified_at":"2026-09-24T04:23:38Z","board_verified":true,"closed_at":null,"days_open":218,"trust":{"level":"ghost","repost_count":0,"flags":["stale","company_stale"],"days_open":218},"description":"About the Role\nRadixArk is seeking a Member of Technical Staff - Inference-Multimodal & Diffusion to advance the frontier of generative modeling.\nYou will work on cutting-edge diffusion and flow-based models for image, video, and multimodal generation, pushing model quality, efficiency, and scalability. This role combines deep research thinking with strong engineering execution - from designing novel algorithms to training and deploying models at scale.\nYour work will directly shape next-generation generative AI systems used by researchers, developers, and real-world applications.\nThis is a high-impact role for engineers and researchers who want to push the limits of generative models in both theory and practice.\nRequirements\n5+ years of experience in ML research or applied ML engineering\n\nStrong expertise in diffusion models or generative models (DDPM, DDIM, latent diffusion, flow matching, etc.)\n\nDeep understanding of deep learning fundamentals and optimization\n\nProven experience training large-scale models on GPUs/TPUs\n\nStrong proficiency in PyTorch or JAX\n\nExperience implementing research ideas into working systems\n\nStrong mathematical foundation in probability, statistics, and optimization\n\nAbility to move from research prototypes to production-quality models\n\nStrong Plus\nPublications in top-tier conferences (NeurIPS, ICML, ICLR, CVPR, etc.)\n\nExperience with large-scale distributed training\n\nExperience in multimodal generation (text-to-image, video, audio)\n\nFamiliarity with transformer architectures and hybrid models\n\nExperience improving sampling speed and generation efficiency\n\nContributions to open-source generative model projects\n\nExperience scaling models to billions of parameters\n\nResponsibilities\nDesign and develop next-generation diffusion and generative models\n\nImprove model quality, controllability, and sample efficiency\n\nResearch and implement novel training and sampling methods\n\nOptimize models for large-scale distributed training\n\nCollaborate with systems teams to scale training and inference\n\nTranslate research ideas into practical production systems\n\nEvaluate models using rigorous metrics and benchmarks\n\nContribute to long-term research and product direction in generative AI\n\nAbout RadixArk\nRadixArk is an infrastructure-first company built by engineers who've shipped production AI systems, created SGLang (30K+ GitHub stars, the fastest open LLM serving engine), and developed Miles (our large-scale RL framework). Founded by AI infrastructure veterans from xAI and NVIDIA, we're on a mission to democratize frontier-level AI infrastructure by building world-class open systems for inference and training. Our team has optimized kernels serving billions of tokens daily, designed distributed training systems coordinating 10,000+ GPUs, and contributed to infrastructure that powers leading AI companies and research labs.\nCompensation\nDepending on background, skills, and experience, the expected annual salary range for this position is $200,000 - $400,000 USD + equity.\nEqual Opportunity\nRadixArk is an Equal Opportunity Employer and is proud to offer equal employment opportunity to everyone regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, and more.","description_format":"text","description_chars":3333,"description_truncated":false,"requirements":{"experience_years_min":5,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":["Equity"],"hiring_locations":[{"name":"United States","iso":"US","kind":"country"},{"name":"Canada","iso":"CA","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["AI Infrastructure"],"lifecycle":[{"event":"open","at":"2026-09-12T15:26:28Z"}],"liveness":{"score":8,"band":"cold","label":"Long shot","p_open":1,"p_active":0.27,"p_room":0.28,"age_days":218,"expected_fill_days":31,"reasons":["conf:1","stale_co","ghost","win:tail","crowd:"],"computed_at":"2026-09-24T05:45:00Z"},"pay":{"stated_usd_annual":400000,"is_top_pay":true},"html_url":"https://alion.io/job/radixark-member-of-technical-staff-inference-multimodal-diffusion","json_url":"https://alion.io/job/radixark-member-of-technical-staff-inference-multimodal-diffusion.json","meta":{"generated_at":"2026-09-24T08:07:20Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers"}}