Distributed Systems Engineer Jobs in USA with Visa Sponsorship
Distributed systems engineers are among the most consistently sponsored roles in U.S. tech. H-1B visa approval rates for software infrastructure roles run high, and many employers sponsor from day one. This page shows open roles that include visa sponsorship. For detailed occupation requirements, see the O*NET profile.
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Get Access To All JobsINTRODUCTION
Involves a hybrid of multiple computational / data / Cyberinfrastructure (CI) dominated fields such as, but not limited to, bioinformatics, geological information services (GIS), data analytics, and computational chemistry. Applies computational, computer science, data science and cyber infrastructure (CI) research and development principles, with relevant domain science knowledge, to perform research and technology integration and development. Responsibilities include research, design, development, analysis, operation, and support of high performance computing (HPC) and data science research, software, tools and hardware resources. Develops data algorithms and performs computations, statistical analyses, interpretation and reporting of research. This specialty / function exists for those positions whose primary responsibility is to do research and use computational and data science technology as a tool to accomplish the research.
The Preclinical Design and Clinical Translation of Regimens for Tuberculosis (PReDiCTR-TB) Consortium is a 21st-century, model-informed drug development (MIDD) platform integrating computational science, translational pharmacology, and global clinical insight to accelerate the design and delivery of transformative TB regimens.
Positioned at the intersection of data, biology, engineering, econometrics, and decision science, PReDiCTR-TB functions as a strategic intelligence engine for regimen development, linking preclinical evidence, synthetic experiments, mechanistic models, and clinical data into a unified predictive framework. By embedding quantitative systems pharmacology (QSP), AI-driven analytics, and probabilistic decision modeling throughout development, the consortium enables real-time prioritization of regimens with the highest probability of clinical success.
Rather than advancing compounds in isolation, PReDiCTR-TB takes a regimen-first, translation-driven approach that optimizes combinations, dosing strategies, and treatment durations through iterative simulation-validation cycles grounded in human-relevant biology. This framework helps reduce reliance on costly empirical experimentation while increasing translational fidelity from bench to bedside.
The consortium delivers:
- Predictive insights that de-risk development earlier
- Optimized trial designs informed by mechanistic and statistical rigor
- Faster and more confident go/no-go decisions across the R&D lifecycle
PReDiCTR-TB is redefining how infectious disease regimens are designed, prioritized, and translated into clinical impact.
Custom Scope
Model-informed drug development (MIDD) and AI in Clinical Trials market is experiencing a massive shift, projected to skyrocket from $1.35 billion to $2.74 billion by 2030. Data and model-driven efficiencies in patient selection, drug repurposing, and real-time trial monitoring are fundamentally compressing drug development timelines.
At the UCSF Savic Lab, we aren't just reacting to this shift—we are driving it. We are moving past traditional, isolated data storage models to establish a modern, highly interconnected, and secure data & model infrastructure.
We are seeking a solution-minded, highly technical, and mission-driven Distributed Data Pipeline Architect. In this role, you will design, build, lead implementation, and operate a research-grade, scale-distributed data architecture. This framework will serve as the foundation for advanced analytics, multi-institution translational science, and 21st-century accelerated drug development decision support.
Department Overview
The Savic Lab is a global leader in model-informed drug development for infectious diseases and serves as a quantitative innovation hub for translational pharmacology, AI-enabled modeling, and next-generation regimen design. The laboratory conducts groundbreaking research across TB, HIV, malaria, pediatric infectious diseases, translational PK/PD, and systems pharmacology. Through its leadership role in PReDiCTR-TB, the lab collaborates with international partners to integrate computational science, mechanistic modeling, and clinical translation into actionable strategies that improve global health outcomes.
UC San Francisco seeks candidates whose experience, teaching, research, or community service has prepared them to contribute to our commitment to diversity and excellence. The University of California is an Equal Opportunity/Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion.
REQUIRED QUALIFICATIONS
- Experience: 5+ years of hands-on experience in data engineering, distributed systems, or enterprise platform architecture.
- Systems Design: A proven track record of architecting, deploying, and maintaining production-grade distributed data architectures.
- Data Processing: Direct experience handling large-scale data ingestion, multi-tenant databases, and event-driven data flows.
- Communication: Exceptional communication and presentation skills, with a demonstrated ability to explain complex technical concepts to non-technical executive stakeholders.
- Bachelor's degree in Computer / Computational / Data Science, or Domain Sciences with computer / computational / data specialization or equivalent experience.
PREFERRED QUALIFICATIONS
- Architectures: Data mesh environments, data fabric layers, and semantic web modeling.
- Streaming & Compute: Apache Kafka, Pulsar, AWS Kinesis, Apache Spark, Flink, or Apache Beam.
- Storage & Databases: Distributed object storage (S3-compatible API environments) and NoSQL systems (Cassandra, DynamoDB, MongoDB).
- Clinical Data Standards: Familiarity with NIH-preferred schemas and ontologies (e.g., BIDS for imaging, OMOP common data models, LOINC, SNOMED CT, or FHIR transfer protocols).
- Governance & Security: Data cataloging, automated data lineage tools, and strict data access control frameworks (HIPAA / NIST compliance).
- Master's degree in Computer / Computational / Data Science, or Domain Sciences with computer / computational / data specialization preferred.
INTRODUCTION
Involves a hybrid of multiple computational / data / Cyberinfrastructure (CI) dominated fields such as, but not limited to, bioinformatics, geological information services (GIS), data analytics, and computational chemistry. Applies computational, computer science, data science and cyber infrastructure (CI) research and development principles, with relevant domain science knowledge, to perform research and technology integration and development. Responsibilities include research, design, development, analysis, operation, and support of high performance computing (HPC) and data science research, software, tools and hardware resources. Develops data algorithms and performs computations, statistical analyses, interpretation and reporting of research. This specialty / function exists for those positions whose primary responsibility is to do research and use computational and data science technology as a tool to accomplish the research.
The Preclinical Design and Clinical Translation of Regimens for Tuberculosis (PReDiCTR-TB) Consortium is a 21st-century, model-informed drug development (MIDD) platform integrating computational science, translational pharmacology, and global clinical insight to accelerate the design and delivery of transformative TB regimens.
Positioned at the intersection of data, biology, engineering, econometrics, and decision science, PReDiCTR-TB functions as a strategic intelligence engine for regimen development, linking preclinical evidence, synthetic experiments, mechanistic models, and clinical data into a unified predictive framework. By embedding quantitative systems pharmacology (QSP), AI-driven analytics, and probabilistic decision modeling throughout development, the consortium enables real-time prioritization of regimens with the highest probability of clinical success.
Rather than advancing compounds in isolation, PReDiCTR-TB takes a regimen-first, translation-driven approach that optimizes combinations, dosing strategies, and treatment durations through iterative simulation-validation cycles grounded in human-relevant biology. This framework helps reduce reliance on costly empirical experimentation while increasing translational fidelity from bench to bedside.
The consortium delivers:
- Predictive insights that de-risk development earlier
- Optimized trial designs informed by mechanistic and statistical rigor
- Faster and more confident go/no-go decisions across the R&D lifecycle
PReDiCTR-TB is redefining how infectious disease regimens are designed, prioritized, and translated into clinical impact.
Custom Scope
Model-informed drug development (MIDD) and AI in Clinical Trials market is experiencing a massive shift, projected to skyrocket from $1.35 billion to $2.74 billion by 2030. Data and model-driven efficiencies in patient selection, drug repurposing, and real-time trial monitoring are fundamentally compressing drug development timelines.
At the UCSF Savic Lab, we aren't just reacting to this shift—we are driving it. We are moving past traditional, isolated data storage models to establish a modern, highly interconnected, and secure data & model infrastructure.
We are seeking a solution-minded, highly technical, and mission-driven Distributed Data Pipeline Architect. In this role, you will design, build, lead implementation, and operate a research-grade, scale-distributed data architecture. This framework will serve as the foundation for advanced analytics, multi-institution translational science, and 21st-century accelerated drug development decision support.
Department Overview
The Savic Lab is a global leader in model-informed drug development for infectious diseases and serves as a quantitative innovation hub for translational pharmacology, AI-enabled modeling, and next-generation regimen design. The laboratory conducts groundbreaking research across TB, HIV, malaria, pediatric infectious diseases, translational PK/PD, and systems pharmacology. Through its leadership role in PReDiCTR-TB, the lab collaborates with international partners to integrate computational science, mechanistic modeling, and clinical translation into actionable strategies that improve global health outcomes.
UC San Francisco seeks candidates whose experience, teaching, research, or community service has prepared them to contribute to our commitment to diversity and excellence. The University of California is an Equal Opportunity/Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion.
REQUIRED QUALIFICATIONS
- Experience: 5+ years of hands-on experience in data engineering, distributed systems, or enterprise platform architecture.
- Systems Design: A proven track record of architecting, deploying, and maintaining production-grade distributed data architectures.
- Data Processing: Direct experience handling large-scale data ingestion, multi-tenant databases, and event-driven data flows.
- Communication: Exceptional communication and presentation skills, with a demonstrated ability to explain complex technical concepts to non-technical executive stakeholders.
- Bachelor's degree in Computer / Computational / Data Science, or Domain Sciences with computer / computational / data specialization or equivalent experience.
PREFERRED QUALIFICATIONS
- Architectures: Data mesh environments, data fabric layers, and semantic web modeling.
- Streaming & Compute: Apache Kafka, Pulsar, AWS Kinesis, Apache Spark, Flink, or Apache Beam.
- Storage & Databases: Distributed object storage (S3-compatible API environments) and NoSQL systems (Cassandra, DynamoDB, MongoDB).
- Clinical Data Standards: Familiarity with NIH-preferred schemas and ontologies (e.g., BIDS for imaging, OMOP common data models, LOINC, SNOMED CT, or FHIR transfer protocols).
- Governance & Security: Data cataloging, automated data lineage tools, and strict data access control frameworks (HIPAA / NIST compliance).
- Master's degree in Computer / Computational / Data Science, or Domain Sciences with computer / computational / data specialization preferred.
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Get Access To All JobsTips for Finding Visa Sponsorship as a Distributed Systems Engineer
Target infrastructure-heavy engineering teams
Companies running large-scale distributed systems, think cloud platforms, fintech, and streaming services, sponsor the most consistently. These teams have ongoing headcount needs and established immigration pipelines, making sponsorship a standard part of hiring.
Lead with systems design depth in your resume
USCIS approves H-1B petitions for distributed systems roles based on specialty occupation criteria. Resumes that demonstrate theoretical depth, such as consensus algorithms, fault tolerance, or distributed transactions, strengthen the case that a bachelor's degree in CS is required.
Clarify your work authorization timeline early
If you're on OPT or STEM OPT, be upfront with recruiters about your end date. Employers who sponsor regularly already know the H-1B cap cycle. Being clear early filters out employers who won't engage and saves weeks of back-and-forth.
Factor cap-exempt employers into your search
Universities, nonprofit research labs, and certain government-affiliated organizations are exempt from the H-1B lottery. Distributed systems roles exist at these institutions, and cap-exempt sponsorship means you can start immediately without waiting for the annual lottery cycle.
Understand that O-1A is a real alternative
If you've published research, spoken at systems conferences like OSDI or SOSP, or led widely adopted open-source infrastructure projects, you may qualify for an O-1A. This visa bypasses the lottery entirely and is increasingly viable for senior engineers.
Check LCA filings to verify sponsorship history
Employers file Labor Condition Applications publicly for every H-1B hire. Searching the Department of Labor's OFLC disclosure database by company name shows whether a prospective employer has actually sponsored engineers recently, not just claimed they do.
Frequently Asked Questions
Is distributed systems engineer a specialty occupation for H-1B purposes?
Yes. Distributed systems engineering consistently qualifies as a specialty occupation under H-1B visa rules because the role normally requires at least a bachelor's degree in computer science, computer engineering, or a closely related field. USCIS has approved thousands of H-1B petitions for this job title. Roles focused on consensus protocols, distributed storage, or large-scale reliability are particularly well-supported by the specialty occupation standard.
Do I need a computer science degree specifically, or will a related field work?
A degree in computer science, electrical engineering, mathematics, or software engineering all support an H-1B petition for this role. The key is that your degree field connects logically to distributed systems work. If your degree is in an unrelated field, three years of directly relevant professional experience can substitute for each year of missing education under USCIS equivalency rules.
Which types of employers sponsor distributed systems engineers most reliably?
Cloud infrastructure companies, large-scale fintech firms, streaming platforms, and database companies sponsor the most distributed systems engineers. These organizations run complex internal systems that require specialized expertise and have legal teams experienced with H-1B petitions. You can browse verified sponsoring employers on Migrate Mate, where all listed roles include visa sponsorship.
How does the H-1B lottery affect my chances if I have a master's degree in CS?
A U.S. master's degree in a qualifying STEM field enters you into the advanced degree exemption pool, which is drawn first. In recent fiscal years, the master's cap selection rate has been meaningfully higher than the regular cap rate. If you earned your master's outside the U.S., you compete in the general pool only, though the practical difference in selection odds has narrowed as overall registrations have grown.
Can I switch employers mid-H-1B if I find a better distributed systems role?
Yes. Under H-1B portability rules, you can start working for a new employer as soon as they file an H-1B transfer petition, without waiting for approval, provided your current H-1B was properly approved and you've maintained valid status. Your new employer files a fresh Form I-129 with an updated Labor Condition Application reflecting the new role and location.
What is the prevailing wage requirement for sponsored Distributed Systems Engineer jobs?
U.S. employers sponsoring a visa must pay at least the prevailing wage, which is what workers in the same role, area, and experience level typically earn. The Department of Labor sets this rate to make sure companies aren't hiring foreign workers simply because they'd accept lower pay than a U.S. worker. It varies by job title, location, and experience. You can look up current prevailing wage rates for any occupation and location using the OFLC Wage Search page.