Staff Machine Learning Infrastructure Engineer
*** No Visa transfer/ sponsorship/ c2c available for this role**
Job Type: Full-time
Department: ML Platform Engineering
10+ years of software engineering experience, with 5+ years focusing on ML infrastructure-- must have to be considered.
About 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.
Key Responsibilities:
- Architect end-to-end ML infrastructure spanning data processing, feature management, and model serving
- Design and lead implementation of next-generation ML platforms that support diverse ML workloads
- Drive technical excellence in ML infrastructure through standardization and automation
- Build scalable data processing systems and feature platforms that handle massive-scale ML workloads
- Design robust ML serving architectures supporting both real-time and batch inference
- Establish best practices for ML observability, monitoring, and operational excellence
- Lead cross-functional technical initiatives and mentor platform engineers
- Drive infrastructure decisions that impact the entire ML lifecycle
Technical Leadership:
- Define technical strategy and roadmap for ML infrastructure
- Drive architectural decisions for complex ML systems
- Lead design reviews and provide technical mentorship
- Collaborate with data science teams to understand and address infrastructure needs
- Establish standards for reliability, scalability, and performance
- Build frameworks and platforms that accelerate ML development
Required Qualifications:
- 10+ years of software engineering experience, with 5+ years focusing on ML infrastructure
- Deep expertise in distributed systems and data processing at scale
- Strong background in ML platform development and MLOps practices
- Experience building production ML infrastructure supporting critical business applications
- Proven track record of leading complex technical initiatives
- Expert-level knowledge in:
- Large-scale data processing systems (Spark, Beam)
- Feature store architectures and implementations
- ML serving platforms and inference optimization (TorchServe, Tensorflow Serving and Triton)
- Container orchestration and cloud platforms
- Data pipeline design and optimization
- ML system monitoring and observability
Technical Expertise:
- Data Infrastructure:
- Feature stores and feature computation systems
- Data quality and validation frameworks
- Dataset versioning and lineage tracking
- Efficient data storage and access patterns
- Serving Infrastructure:
- Model deployment and serving platforms
- Inference optimization and scaling
- Load balancing and traffic management
- Model versioning and lifecycle management
- Platform Development:
- MLOps tooling and automation
- Experimentation platforms
- Monitoring and observability systems
- Resource management and optimization
Preferred Qualifications:
- Experience with GPU infrastructure and optimization
- Background in high-performance computing
- Contributions to open-source ML infrastructure projects
- Experience with ML-specific security and compliance requirements
- Master's degree in Computer Science or related field
Impact:
- Shape the technical direction of ML infrastructure across the organization
- Drive innovation in ML platforms and tools
- Mentor and grow the technical capabilities of the team
- Establish architectural patterns and best practices
- Enable rapid ML development and deployment at scale
Exempt Roles (up to AD Level)
Note: Compensation range should extend from the minimum to P75, consistent with the job family and level.
For all remote positions and those based in pay transparency locations, include this text in the External Description:
Salary and Other Compensation:
The annual salary for this position is between $140-155Kdepending on experience and other qualifications of the successful candidate.
This position is also eligible for Cognizant’s discretionary annual incentive program, based on performance and subject to the terms of Cognizant’s applicable plans.
Benefits: Cognizant offers the following benefits for this position, subject to applicable eligibility requirements:
· Medical/Dental/Vision/Life Insurance
· Paid holidays plus Paid Time Off
· 401(k) plan and contributions
· Long-term/Short-term Disability
· Paid Parental Leave
· Employee Stock Purchase Plan
Disclaimer: The salary, other compensation, and benefits information is accurate as of the date of this posting. Cognizant reserves the right to modify this information at any time, subject to applicable la
About Cognizant:
Cognizant (Nasdaq: CTSH) is an AI Builder and technology services provider, bridging the gap between AI investment and enterprise value by building full-stack AI solutions for our clients. Our deep industry, process and engineering expertise enables us to build an organization’s unique context into technology systems that amplify human potential, drive tangible outcomes and keep global enterprises ahead in a fast-changing world. See how at cognizant.ai or @cognizant.
Additional employment information
Compensation information is accurate as of the date of this posting. Cognizant reserves the right to modify this information at any time, subject to applicable law.
Applicants may be required to attend interviews in person or by video conference. In addition, candidates may be required to present their current state or government issued ID during each interview.
Cognizant is an equal opportunity employer. Your application and candidacy will not be considered based on race, color, sex, religion, creed, sexual orientation, gender identity, national origin, disability, genetic information, pregnancy, veteran status or any other characteristic protected by federal, state or local laws.
