{"id":3853156,"url":"https://alion.io/job/uber-software-engineer-ii-engineer-11","title":"Software Engineer II - Engineer","company":{"id":230,"name":"Uber","domain":"uber.com","url":"https://alion.io/company/uber","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Oracle","truth_index":{"grade":"A","score":88,"open_postings":154,"ghost_share":0.032,"stale_share":0.532,"repost_share":0.091,"time_to_fill_p50_days":12,"computed_at":"2026-11-06T05:58:05Z"}},"role":"Backend","role_family":"Backend","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":["Sunnyvale, United States","San Francisco, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":171000,"max":190000,"currency":"USD","period":"year","gross":null,"usd_annual":190000},"salary_estimate":null,"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":true,"technologies":[{"name":"ETL/ELT","optional":false},{"name":"Go","optional":false},{"name":"Java","optional":false},{"name":"JAX","optional":false},{"name":"Keras","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Ray","optional":false},{"name":"Spark","optional":false},{"name":"TensorFlow","optional":false},{"name":"Recommender Systems","optional":true}],"status":"live","first_seen_at":"2026-11-06T00:07:12Z","employer_posted_date":"2026-11-06","last_verified_at":"2026-11-06T14:51:29Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"About the Role\nThe UberEats Feed Ranking team is responsible for the ML ranking layer which personalizes the Eats Homefeed. This personalization layer is one of the main drivers of order growth across the Delivery organization. As a Machine Learning Engineer (MLE) on the Feed Ranking team, you will own and develop models across our entire ranking stack (Retrieval + Pre-Ranking, Second Pass Ranking, Post-Ranking). Our models are sophisticated (>5B parameters), our features are rich (near-realtime engagement history with 1-year lookback), and our serving requirements are stringent (>10M QPS with low latency and high availability). As an MLE, the reward for working within these requirements is significant - offline performance gains in our ranking models are strongly correlated with topline impact for the entire Eats business.\nWhat You’ll Do\nOwn and develop on all parts of the ML lifecycle - both offline (training, model architecture, etc.) and online (serving efficiency, GPU provisioning + utilization, etc.).\nBring SOTA ML techniques from DLRM and LLM literature (Scaling Laws, Kernel Fusion, Latent Attention, Agentic Auto-Research, RL-based post-ranking, etc.) into our modeling stack.\nCollaborate closely with backend engineers to ensure high-quality and reliability in end-to-end integration.\nCoordinate with Science and Product partners to maximize the business impact of ML improvements.\nBasic Qualifications\nBachelor’s degree or equivalent in Computer Science, Engineering, Mathematics or related field, with 3+ years of full-time experience working on production ML systems.\nStrong proficiency in at least one programming language such as Python, Go, or Java.\nStrong proficiency with common Deep-Learning frameworks (PyTorch, Tensorflow/Keras, Jax, etc.).\nExperience with distributed data processing and ETL frameworks (Spark, Ray Data, etc.).\nStrong technical communication and ability to manage multiple cross functional stakeholders.\nPreferred Qualifications\nMaster's or PhD in Computer Science, Engineering, Mathematics or related field, with publications in the ML/AI space (papers, conference presentations, company technical blogs, etc.).\nIndustry experience working on Deep Learning Recommendation Systems and/or LLM model development at scale in a production/user-facing environment. \nExperience optimizing Deep Learning model architectures + serving stack to improve GPU MFU across different hardware types.\nDesire to grow into technical leadership and become a force multiplier for junior engineers.\n For San Francisco, CA-based roles: The base salary range for this role is USD $171,000 per year - USD $190,000 per year.\nFor Sunnyvale, CA-based roles: The base salary range for this role is USD $171,000 per year - USD $190,000 per year.\nFor all US locations, you will be eligible to participate in Uber's bonus program, and may be offered an equity award & other types of comp. All full-time employees are eligible to participate in a 401(k) plan. 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