{"id":125522,"url":"https://alion.io/job/adobe-senior-machine-learning-engineer-applied-science-data-frameworks","title":"Senior Machine Learning Engineer, Applied Science Data Frameworks","company":{"id":26,"name":"Adobe","domain":"adobe.com","url":"https://alion.io/company/adobe","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"B","score":80,"open_postings":51,"ghost_share":0,"stale_share":0.784,"repost_share":0,"time_to_fill_p50_days":47,"computed_at":"2026-10-17T05:46:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["San Jose, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":178000,"max_usd":339000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":1213},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":true,"technologies":[{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Dask","optional":false},{"name":"Databricks","optional":false},{"name":"Docker","optional":false},{"name":"DuckDB","optional":false},{"name":"LanceDB","optional":false},{"name":"Machine Learning","optional":false},{"name":"Multimodal AI","optional":false},{"name":"OpenSearch","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Ray","optional":false},{"name":"Semantic Search","optional":false},{"name":"Semantic Search","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"TensorFlow","optional":false}],"status":"live","first_seen_at":"2026-08-26T00:00:00Z","employer_posted_date":"2026-09-18","last_verified_at":"2026-10-17T09:10:50Z","board_verified":true,"closed_at":null,"days_open":52,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":52},"description":"About the Role\nWe are looking for a Senior Machine Learning Engineer to join our Applied Science Data Frameworks team responsible for building the foundational infrastructure that powers large-scale multimodal AI training and inference. This role is ideal for someone with strong distributed systems and data engineering fundamentals who is eager to work in an ML-adjacent environment, contributing to training data loaders, distributed inference frameworks, feature enrichment pipelines, and dataset management systems that enable ML teams to train foundation models at petabyte scale. You'll work on high-impact projects involving distributed data loading for PyTorch training workloads, batch inference pipelines for feature enrichment, semantic search infrastructure for dataset discovery, and production-grade ML data pipelines that support generative AI model development. Your systems will process billions of images,\nvideos, and multimodal content across large-scale GPU clusters.\nIf you're excited about building distributed data frameworks, optimizing data pipelines at scale, and growing your expertise in ML infrastructure, we'd love to hear from you.\nWhat You'll Do\nContribute to building and maintaining distributed training data loaders that handle multi-source data ingestion, temporal sampling, and real-time transformations for large-scale model training workflows.\nHelp implement and maintain feature enrichment pipelines and dataset registry systems that support multimodal model training across images, video, documents, and text.\nBuild and maintain batch inference pipelines for large-scale feature extraction,\nprocessing assets through distributed GPU clusters with queue management and fault tolerance.\nDevelop data processing systems using frameworks like Apache Ray, Spark, DuckDB, or similar distributed computing tools for SQL-based data ingestion and Apache Arrow-based storage formats.\nSupport semantic search capabilities and vector database infrastructure (e.g.,\nOpenSearch, LanceDB) for dataset discovery and embedding-based retrieval.\nContribute to CI/CD infrastructure for ML systems including self-hosted runner\nmanagement, Docker image builds, automated testing pipelines, and deployment automation.\nCollaborate with ML research teams to translate training requirements into reliable, scalable data loading and preprocessing infrastructure.\nWrite reusable framework components, SDKs, and documentation to help accelerate platform adoption across modeling teams.\nOptimize data pipeline performance across dimensions like startup latency, throughput, memory footprint, and GPU utilization.\nContribute to observability and reliability standards for production data systems\nsupporting 24/7 training workloads.\nWhat You Need to Succeed\n5-6 years of professional experience building and operating distributed systems or data infrastructure in production environments.\nSolid understanding of distributed computing concepts and experience with framework like Apache Spark, Ray, Dask, or equivalent.\nFamiliarity with cloud platforms (AWS or Azure) and data platforms such as Databricks or Spark.\nProficiency in Python and strong software engineering fundamentals - system design, data structures, algorithms.\nFamiliarity with ML frameworks such as PyTorch or TensorFlow; hands-on ML experience is a plus but not required.\nBasic familiarity with MLOps practices including CI/CD pipelines, containerization (Docker), and deployment automation.\nFamiliarity with batch inference architectures and large-scale data processing patterns is a plus.\nBachelor's degree in Computer Science, Engineering, or a related field; MS is a plus.\nStrong communication skills and ability to collaborate across engineering and research teams.\nAbout Adobe\nAdobe empowers everyone to create through innovative platforms and tools that unleash creativity, productivity and personalized customer experiences. Adobe’s industry-leading offerings including Adobe Acrobat Studio, Adobe Express, Adobe Firefly, Creative Cloud, Adobe Experience Platform, Adobe Experience Manager, and GenStudio enable people and businesses to turn ideas into impact, powered by AI and driven by human ingenuity.\nOur 30,000+ employees worldwide are creating the future and raising the bar as we drive the next decade of growth. We’re on a mission to hire the very best and believe in creating a company culture where all employees are empowered to make an impact. At Adobe, we believe that great ideas can come from anywhere in the organization. The next big idea could be yours.\n\nLet’s Adobe together\nAt Adobe, we believe in creating a company culture where all employees are empowered to make an impact. Learn more about Adobe life, including our values and culture, focus on people, purpose and community, Adobe for All, comprehensive benefits programs, the stories we tell, the customers we serve, and how you can help us advance our mission of empowering everyone to create.\nAdobe is proud to be an Equal Employment Opportunity employer. We do not discriminate based on gender, race or color, ethnicity or national origin, age, disability, religion, sexual orientation, gender identity or expression, veteran status, or any other protected characteristic. Learn more.\nAdobe aims to make our Careers website and recruiting process accessible to any and all users. If you have a disability or special need that requires accommodation to navigate our website or complete the application process, email .\nAI Use Guidelines for Interviews:\nOur interviews are designed to reflect your own skills and thinking. The use of AI or recording tools during live interviews is not permitted unless explicitly invited by the interviewer or approved in advance as part of a reasonable accommodation. If these tools are used inappropriately or in a way that misrepresents your work, your application may not move forward in the process.\nAt Adobe, we empower employees to innovate with AI - and we look for candidates eager to do the same. As part of the hiring experience, we provide clear guidance on where AI is encouraged during the process and where it’s restricted during live interviews. See how we think about AI in the hiring experience.\nExpected Pay Range:\nOur compensation reflects the cost of labor across several U.S. geographic markets, and we pay differently based on those defined markets. The U.S. pay range for this position is $151,800 - $265,350 annually. Pay within this range varies by work location and may also depend on job-related knowledge, skills, and experience. Your recruiter can share more about the specific salary range for the job location during the hiring process.\n\nIn California, the pay range for this position is $183,300 - $265,350\nAt Adobe, for sales roles starting salaries are expressed as total target compensation (TTC = base + commission), and short-term incentives are in the form of sales commission plans. Non-sales roles starting salaries are expressed as base salary and short-term incentives are in the form of the Annual Incentive Plan (AIP).\nIn addition, certain roles may be eligible for long-term incentives in the form of a new hire equity award.\nState-Specific Notices:\nCalifornia:\nFair Chance Ordinances\nAdobe will consider qualified applicants with arrest or conviction records for employment in accordance with state and local laws and “fair chance” ordinances.\nColorado:\nApplication Window Notice\nIf this role is open to hiring in Colorado (as listed on the job posting), the application window will remain open until at least the date and time stated above in Pacific Time, in compliance with Colorado pay transparency regulations. If this role does not have Colorado listed as a hiring location, no specific application window applies, and the posting may close at any time based on hiring needs.\nMassachusetts:\nMassachusetts Legal Notice\nIt is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. 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