{"id":105982,"url":"https://alion.io/job/bumble-senior-machine-learning-engineer","title":"Senior Machine Learning Engineer","company":{"id":13694,"name":"Bumble","domain":"bumble.com","url":"https://alion.io/company/bumble","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Lever","truth_index":{"grade":"B","score":79,"open_postings":12,"ghost_share":0,"stale_share":0.833,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-10-17T05:46:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Austin, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":220000,"max":250000,"currency":"USD","period":"year","gross":null,"usd_annual":250000},"salary_estimate":null,"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"A/B Testing","optional":false},{"name":"Agile","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Spark","optional":false},{"name":"TensorFlow","optional":false}],"status":"live","first_seen_at":"2026-04-09T22:00:48Z","employer_posted_date":"2026-04-09","last_verified_at":"2026-10-18T03:12:29Z","board_verified":true,"closed_at":null,"days_open":191,"trust":{"level":"stale","repost_count":0,"flags":["stale"],"days_open":191},"description":"Introduction to the role & team \n At Bumble, we’re building a world where all relationships are healthy and equitable, and machine learning is central to how we make that real for millions of people every day. As part of our Machine Learning team, you’ll help shape intelligent systems that power meaningful connections, safer interactions, and more personalised experiences across our platform.\nAs a Senior Machine Learning Engineer, you’ll own impactful problems end-to-end-from data exploration through to production deployment, while collaborating closely with Product, Engineering, and Data partners. You’ll bring curiosity into how we experiment, iterate, and improve, and you’ll role model our values of Curiosity and Excellence by continuously raising the bar in how we build and apply AI.\nAI is deeply embedded in how we evolve at Bumble. In this role, you’ll independently apply modern machine learning and emerging AI techniques, contributing to scalable systems while ensuring thoughtful, responsible use of AI in everything we ship.\nWhat you’ll do\nBuild and deploy machine learning models that improve recommendations, ranking, and personalization, driving measurable impact on user experience and engagement\n\nOwn problems end-to-end, from data exploration and feature engineering through to model training, evaluation, and production deployment\n\nDevelop and maintain scalable ML pipelines using tools such as Spark and Airflow to support reliable, high-quality model delivery\n\nApply modern ML frameworks (e.g. PyTorch or TensorFlow) to design, train, and optimise models in production environments\n\nContribute to experimentation frameworks, including A/B testing and offline evaluation, to iterate on model performance with an agile mindset\n\nCollaborate cross-functionally with Product and Engineering, working with purpose to translate product questions into ML solutions\n\nTake ownership of delivering high-quality solutions and see work through from insight to impact, balancing speed and rigor\n\nApply responsible AI practices, ensuring fairness, transparency, and safety are considered in model development and deployment\n\nAbout You\nTypically requires 5-8 years of experience, though we welcome candidates with alternative backgrounds that demonstrate equivalent skills.\n\nStrong experience building and deploying machine learning models in production environments\n\nProficiency in Python and experience with at least one major ML framework (e.g. PyTorch, TensorFlow)\n\nExperience working with data pipelines and distributed systems (e.g. Spark, Airflow) to support ML workflows\n\nFamiliarity with experimentation methodologies such as A/B testing and model evaluation techniques\n\nAbility to collaborate effectively across functions, demonstrating strong ownership and a collaborative 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