{"id":2163214,"url":"https://alion.io/job/cognizant-machine-learning-infrastructure-engineer-2","title":"Machine Learning Infrastructure Engineer","company":{"id":85,"name":"Cognizant","domain":"cognizant.com","url":"https://alion.io/company/cognizant","size_band":"5000+","is_staffing_agency":false,"employer_type":"services","is_intermediary":false,"listed_via":null,"ats_vendor":"Cognizant Careers","truth_index":{"grade":"B","score":76,"open_postings":86,"ghost_share":0,"stale_share":0.977,"repost_share":0,"time_to_fill_p50_days":24,"computed_at":"2026-10-12T06:55:09Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"staff","employment_type":"full_time","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["San Francisco, United States"],"countries":["US"],"hiring_countries":["US"],"hiring_countries_total":1,"salary":null,"salary_estimate":{"min_usd":133000,"max_usd":251000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":147},"experience_years_min":10,"visa_sponsorship":true,"relocation_package":false,"has_equity":true,"technologies":[{"name":"Feature Store","optional":false},{"name":"GCP","optional":false},{"name":"Machine Learning","optional":false},{"name":"Platform Engineering","optional":false},{"name":"TensorFlow","optional":false},{"name":"TorchServe","optional":false},{"name":"Triton","optional":false}],"status":"live","first_seen_at":"2026-09-15T14:53:36Z","employer_posted_date":"2026-10-09","last_verified_at":"2026-10-12T22:07:06Z","board_verified":true,"closed_at":null,"days_open":27,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":27},"description":"Staff Machine Learning Infrastructure Engineer\n*** No Visa transfer/ sponsorship/ c2c available for this role**\nJob Type: Full-time\nDepartment: ML Platform Engineering\n10+ years of software engineering experience, with 5+ years focusing on ML infrastructure and GCP -- must have to be considered.\nAbout the Role: As a Staff Machine Learning Infrastructure Engineer, you will architect and lead the technical vision for our ML platform initiatives, focusing on building scalable infrastructure that powers our ML capabilities. You will design and drive the evolution of our ML platforms, data systems, and serving infrastructure that enable teams to efficiently develop, deploy, and operate ML models at scale.\nKey Responsibilities:\nArchitect end-to-end ML infrastructure spanning data processing, feature management, and model serving\nDesign and lead implementation of next-generation ML platforms that support diverse ML workloads\nDrive technical excellence in ML infrastructure through standardization and automation\nBuild scalable data processing systems and feature platforms that handle massive-scale ML workloads\nDesign robust ML serving architectures supporting both real-time and batch inference\nEstablish best practices for ML observability, monitoring, and operational excellence\nLead cross-functional technical initiatives and mentor platform engineers\nDrive infrastructure decisions that impact the entire ML lifecycle\nTechnical Leadership:\nDefine technical strategy and roadmap for ML infrastructure\nDrive architectural decisions for complex ML systems\nLead design reviews and provide technical mentorship\nCollaborate with data science teams to understand and address infrastructure needs\nEstablish standards for reliability, scalability, and performance\nBuild frameworks and platforms that accelerate ML development\nRequired Qualifications:\n 10+ years of software engineering experience, with 5+ years focusing on ML infrastructure\nDeep expertise in distributed systems and data processing at scale\nStrong background in ML platform development and MLOps practices\nExperience building production ML infrastructure supporting critical business applications\nProven track record of leading complex technical initiatives\nExpert-level knowledge in:Large-scale data processing systems (Spark, Beam)\nFeature store architectures and implementations\nML serving platforms and inference optimization (TorchServe, Tensorflow Serving and Triton)\nContainer orchestration and cloud platforms\nData pipeline design and optimization\nML system monitoring and observability\n\nTechnical Expertise:\nData Infrastructure:Feature stores and feature computation systems\nData quality and validation frameworks\nDataset versioning and lineage tracking\nEfficient data storage and access patterns\n\nServing Infrastructure:Model deployment and serving platforms\nInference optimization and scaling\nLoad balancing and traffic management\nModel versioning and lifecycle management\n\nPlatform Development:MLOps tooling and automation\nExperimentation platforms\nMonitoring and observability systems\nResource management and optimization\n\nPreferred Qualifications:\nExperience with GPU infrastructure and optimization\nBackground in high-performance computing\nContributions to open-source ML infrastructure projects\nExperience with ML-specific security and compliance requirements\nMaster's degree in Computer Science or related field\nImpact:\nShape the technical direction of ML infrastructure across the organization\nDrive innovation in ML platforms and tools\nMentor and grow the technical capabilities of the team\nEstablish architectural patterns and best practices\nEnable rapid ML development and deployment at scale\nSalary and Other Compensation:\nThe annual salary for this position is between $140-155Kdepending on experience and other qualifications of the successful candidate.\nThis position is also eligible for Cognizant’s discretionary annual incentive program, based on performance and subject to the terms of Cognizant’s applicable plans.\nBenefits: Cognizant offers the following benefits for this position, subject to applicable eligibility requirements:\n· Medical/Dental/Vision/Life Insurance\n· Paid holidays plus Paid Time Off\n· 401(k) plan and contributions\n· Long-term/Short-term Disability\n· Paid Parental Leave\n· Employee Stock Purchase Plan\nDisclaimer: The salary, other compensation, and benefits information is accurate as of the date of this posting. 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