{"id":1289703,"url":"https://alion.io/job/millennium-management-deep-learning-quantitative-researcher","title":"Deep Learning Quantitative Researcher","company":{"id":704283,"name":"Millennium Management","domain":"mlp.com","url":"https://alion.io/company/millennium-management","size_band":"11-50","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Eightfold","truth_index":{"grade":"A","score":91,"open_postings":74,"ghost_share":0,"stale_share":0.176,"repost_share":0,"time_to_fill_p50_days":110,"computed_at":"2026-09-30T05:45:00Z"}},"role":"Data Science","role_family":"Data Science","seniority":"middle","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Hong Kong"],"countries":["HK"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":55000,"max_usd":147000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":550},"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"C++","optional":false},{"name":"CUDA","optional":false},{"name":"CUDA Toolkit","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false}],"status":"live","first_seen_at":"2026-07-21T00:00:00Z","employer_posted_date":"2026-07-24","last_verified_at":"2026-09-30T18:39:17Z","board_verified":true,"closed_at":null,"days_open":72,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":72},"description":"Deep Learning Quantitative ResearcherPlease submit resumes to  and reference REQ-30088.\nPreferred Candidate Profile\nTop-tier academic background from a globally top-20 university (e.g., MIT, Harvard, Princeton, Stanford, Caltech)\nPhD-level training in Computer Science, Engineering, Physics, Mathematics, or Statistics preferred\nGold medal in a national or international olympiad (IMO, CMO, IOI, NOI, IPhO, CPhO) strongly preferred\nPractical, hands-on experience with large-scale, end-to-end deep learning at a top-tier quantitative trading firm or a leading AI/technology company preferred Key Responsibilities\nDesign and build the firm’s core deep learning pipelines for applied quantitative alpha research- from data preparation and distributed training through evaluation and production deployment.\nDrive a significant part of the research agenda using applied deep learning techniques, owning the full empirical loop: problem formulation, model design, training, validation, and performance attribution.\nUphold rigorous research discipline in a low signal-to-noise domain - strict out-of-sample hygiene, leakage prevention, and honest benchmarking against simpler baselines.\nAct as the firm’s central point of deep learning expertise: advise on architecture selection and training diagnostics, review model designs, and set standards for how models are evaluated and promoted.\nFacilitate the seamless flow of model fitting and model computation across teams and systems through standardized training and inference interfaces and reusable components.\nQualifications & Experience\n3-5 years of professional experience applying deep learning to large-scale problems, ideally in quantitative finance. A strong PhD research record plus hands-on experience training large models at a leading AI/technology company will be considered in lieu of direct quant experience.\nProven end-to-end ownership of the deep learning model lifecycle on at least one significant production system or published research line.\nDeep expertise in Python and a modern DL framework.\nHands-on experience with large-scale model training: distributed/multi-GPU training, mixed precision, and throughput profiling and optimization.\nStrong foundations in statistics, optimization, and machine learning theory.\nHard Skills & Technical Knowledge:\nCommand of modern deep learning architectures, and the judgment to know when a simpler model should win.\nPractical technique for low signal-to-noise learning: regularization, ensembling, and validation protocols that survive out-of-sample.\nExperience with large-scale datasets - efficient columnar formats, streaming data loaders, and point-in-time-correct dataset construction.\nFluency with experiment-management tooling: experiment tracking, hyperparameter optimization, and reproducible research environments.\nWorking knowledge of C++ or CUDA-level optimization a plus; familiarity with LLM tooling as a research accelerant a plus.\nSoft Skills:\nResearch Taste & Rigor: Designs clean experiments and kills ideas quickly when the evidence says so.\nProactive Collaboration: Builds strong partnerships across research and engineering.\nHigh Integrity: Upholds rigorous ethical standards in handling sensitive data and models.\nGrowth Mindset: Stays current with a fast-moving field and adopts what works.\nSuperb Communication: Explains model behavior and uncertainty to technical and nontechnical audiences.","description_format":"text","description_chars":3441,"description_truncated":false,"requirements":{"experience_years_min":3,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"phd","optional":false},"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Hedge Funds & Alternative Investments"],"lifecycle":[{"event":"open","at":"2026-09-26T07:37:06Z"}],"liveness":{"score":43,"band":"fade","label":"Fading","p_open":1,"p_active":0.591,"p_room":0.72,"age_days":71,"expected_fill_days":110,"reasons":["conf:6","velocity","win:mid","crowd:brand"],"computed_at":"2026-09-30T05:45:00Z"},"pay":null,"html_url":"https://alion.io/job/millennium-management-deep-learning-quantitative-researcher","json_url":"https://alion.io/job/millennium-management-deep-learning-quantitative-researcher.json","meta":{"generated_at":"2026-10-01T00:02:59Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","usage":{"tier":"crawler","counted_by":"address","units_charged":1,"used_today":51,"day_limit":5000,"remaining_today":4949,"minute_limit":60,"resets_at":"2026-10-02T00:00:00Z"}}}