{"id":2096073,"url":"https://alion.io/job/scandit-senior-machine-learning-engineer-biometrics-face-recognition","title":"Senior Machine Learning Engineer (Biometrics & Face Recognition)","company":{"id":48217,"name":"Scandit","domain":"scandit.com","url":"https://alion.io/company/scandit","size_band":"201-500","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":{"grade":"A","score":92,"open_postings":3,"ghost_share":0,"stale_share":0.333,"repost_share":0,"time_to_fill_p50_days":46,"computed_at":"2026-10-15T05:46:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":null,"work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Tampere, Finland","Zurich, Switzerland"],"countries":["FI","CH"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":105000,"max_usd":270000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":2102},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":true,"technologies":[{"name":"C++","optional":false},{"name":"Edge AI","optional":false},{"name":"Machine Learning","optional":false},{"name":"ONNX","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"PyTorch C++","optional":false},{"name":"Core ML","optional":true},{"name":"LiteRT","optional":true},{"name":"ONNX Runtime","optional":true},{"name":"TensorFlow","optional":true},{"name":"TFLite","optional":true}],"status":"live","first_seen_at":"2026-10-08T15:11:01Z","employer_posted_date":"2026-10-08","last_verified_at":"2026-10-15T05:44:53Z","board_verified":true,"closed_at":null,"days_open":6,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":6},"description":"Scandit gives people superpowers. Whether enabling delivery drivers to make quicker deliveries, matching a patient with their medication, or allowing retailers to make store operations more efficient, our technology automates workflows. It provides actionable insights to help businesses in a variety of industries. Join us as we continue to expand, grow, innovate, and help take Scandit to the next level.\nBanks, fintechs and regulated businesses need more than document scanning. They need to know the person holding the ID is real and is its rightful owner. As a Senior Machine Learning Engineer in our ID Capture team, you will build the machine learning behind that, centred on face recognition and face matching. You will also support our liveness and presentation attack detection work.\nYou will have real ownership of the architecture, data and benchmarks, and your models will run fully on device, on the phones our users already have, with no cloud round-trip.\nIf these challenges sound interesting to you, we'd love to hear from you!\nAbout the role\nYou will drive our face-matching work for high-assurance identity verification. You will own models end to end and work closely with our C++ engineers to ship them on device, at scale, on millions of phones and scanners. \nWhat you will do\nDesign, train and evaluate models for face detection, face recognition and 1:1 face matching against ID document portraits\nSupport the team's work on liveness and presentation attack detection (PAD) against spoofs, replays and deepfakes\nDefine quality metrics, benchmarks and test sets that meet high-assurance and regulatory expectations\nOwn the full loop: data collection and labelling strategy, prototyping, training, evaluation, debugging and model integration\nOptimise models for on-device inference with our C++ engineers (quantisation, pruning, mobile runtimes)\nTrack research in biometrics and face anti-spoofing, and turn it into product direction\nMentor engineers on ML practice\nOur tech stack\nPython, PyTorch\nONNX\nC++ on-device SDK\niOS / Android\nWho you are\nWe are looking for an experienced ML engineer who has shipped production models to real users and has worked hands-on with biometrics. You are comfortable owning ambiguous problems, moving fast and then iterating. You also work well with product, C++ and QA engineers.\n5+ years as an ML engineer, with production models shipped to real users\nHands-on experience with biometrics: face recognition, face verification, liveness or PAD\nFluent in Python and at least one deep learning framework (PyTorch preferred)\nStrong grasp of evaluation for security-sensitive systems: FAR/FRR, bias and fairness across demographics, adversarial robustness\nClear communicator who works well across disciplines\nEligible to work in the hiring location\nNice to have:\nKnowledge of standards such as ISO/IEC 30107 (PAD), NIST FRTE/FATE or iBeta testing\nExperience deploying models on mobile or edge (TFLite, ONNX Runtime, Core ML)\nWorking knowledge of C++\nWhat we offer\nHere are just some of the reasons why people choose to build their careers at Scandit:\nA highly skilled team and a fun environment where you can put your enthusiasm for cutting-edge technologies to use\nHackathons\nFlexible, office, hybrid or home working\nPeople-first culture\nGlobal team outings, Micro Adventures\nFestive/end of year all company celebrations\nYour birthday off\nAn attractive individual equity plan in a high growth company\nWe are certified as a \"Great Place to Work\" in 7 countries!\nExcellent office infrastructure, optimized for hybrid working in Zurich, Warsaw, Tampere, and London\nExcellent support for remote work across Switzerland, Finland, Poland, UK, Italy and Germany\nSpecific benefits related to the location you are joining\nAt Scandit we strive to create an inclusive environment that empowers our employees. We believe that our products and services benefit from our diverse backgrounds and experiences and are proud to be a safe space for all.\nAll qualified applicants will receive consideration for employment without regard to race, color, nationality, religion, sexual orientation, gender, gender identity, age, physical [dis]ability or length of time spent unemployed.\nImagine the What. 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