H-1B1 Singapore Sponsorship Jobs in Washington
Washington's tech-heavy economy and Pacific Rim trade ties make it one of the stronger H-1B1 Singapore sponsorship markets in the U.S. Employers in Seattle, Bellevue, and Redmond regularly file LCAs for specialty occupation roles in software, cloud infrastructure, finance, and engineering.
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Get Access To All JobsJoin a team at the forefront of ML infrastructure and generative AI, where data and model workflows come together to enable the next generation of intelligent experiences on Apple products and services. We build robust systems that connect scalable data pipelines with advanced ML workflows, accelerating the development of real-world AI applications. Our work spans the full ML lifecycle, from experimentation to deployment, and you’ll play a key role in shaping how AI models are built, optimized, and scaled. We develop a platform for ML data and features that powers advanced GenAI applications. This includes embeddings (generation, evaluation, ANN search, multimodal support), AI Ops, efficient inference, and a modern feature platform designed to streamline experimentation and drive innovation. We’re looking for engineers and researchers passionate about generative models, data-centric ML, and intelligent systems across diverse real-world use cases. With the autonomy to experiment, the scale to make an impact, and the support to take ideas from prototype to production, you’ll work alongside a world-class team to build intelligent, flexible systems that make ML development faster, more reliable, and more creative.
Description
The Apple Cloud AI Platform team enables Apple's next generation of intelligent products by giving Apple's ML engineers and researchers the data systems and large-scale compute they need to build and ship models at Apple's bar for quality and privacy.
Responsibilities:
As a member of the Apple Cloud AI Platform team, your responsibilities will include:
- Design and build the platform behind Apple's largest model builds - ingestion, immutable versioning, lineage, and governance across structured, unstructured, and multimodal data at petabyte scale, so every model run is reproducible from a versioned dataset
- Develop and evolve Python SDKs and core data libraries that ML engineers depend on to access, transform, and load model-ready datasets across every stage of model development
- Build high-throughput data access and loading primitives that feed Apple's largest GPU fleets, keeping workloads compute-bound rather than I/O-bound
- Build and operate distributed data pipelines spanning Spark, Daft, and Rust-based systems for ingestion, transformation, and large-scale data preparation
- Optimize platform components for tight integration with leading ML frameworks - PyTorch, JAX, and TensorFlow - so dataset access is a first-class concern in the model development loop
- Partner with research and product teams to onboard new data sources, and enable rapid iteration on datasets powering GenAI workloads
- Ensure governance is a first-class platform capability: Legal Terms of Use enforcement, privacy controls, and end-to-end data lineage on every dataset version
- Drive efficiency, reliability, and automation across the data plane and control plane that power Apple's ML fleet
- Continuously evolve platform capabilities to support next-generation workloads, including foundation models, multimodal data, and retrieval-augmented systems
- Diagnose, fix, and automate away complex issues across the stack - from ingestion pipelines to dataset APIs to ML framework integrations - to maximize uptime and throughput
Minimum Qualifications
- Strong foundation in machine learning, with hands-on experience across the end-to-end ML workflow - including data preparation, pipeline development, experimentation, evaluation, and deployment
- Expertise in building and running large scale distributed systems
- Familiarity with modern generative techniques (e.g. transformers, diffusion, retrieval-augmented generation)
- Proven experience building and delivering data and machine learning infrastructure in real-world production environments
- Familiarity with fine-tuning workflows, model optimization, and preparing models for scalable inference
- Familiarity with generative AI and its applications in accelerating and enhancing machine learning workflows
- Experience configuring, deploying and troubleshooting large scale production environments
- Experience in designing, building, and maintaining scalable, highly available systems that prioritize ease of use
- Extensive programming experience in Java, Python or Go
- Strong collaboration and communication (verbal and written) skills
- Comfortable navigating ambiguity and evolving technical landscapes, especially in fast-moving areas
- B.S., M.S., or Ph.D. in Computer Science, Computer Engineering, or equivalent practical experience
Preferred Qualifications
Experience in any of the below is preferred:
- Proficiency with one or more modern ML frameworks (PyTorch, JAX, or TensorFlow), particularly the data loading and dataset access layer
- Columnar and lakehouse formats: Parquet, Iceberg, Delta, or Lance
- Distributed data loading frameworks for ML: Ray Data, NVIDIA DALI, WebDataset, or Mosaic StreamingDataset
- Performance engineering for I/O-bound workloads - Arrow, zero-copy, memory mapping, async I/O
- High-throughput object storage access patterns at GPU scale
- Data lineage and governance systems (DataHub, OpenLineage, Unity Catalog, or equivalent)
- Contributions to or operational experience with Spark, Daft, Polars, or DuckDB internals
- Containerization and orchestration technologies (Docker, Kubernetes)
Pay & Benefits
At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $175,000 and $308,500, and your base pay will depend on your skills, qualifications, experience, and location.
Apple employees also have the opportunity to become an Apple shareholder through participation in Apple's discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple's Employee Stock Purchase Plan. You'll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses - including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits
Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.
Join a team at the forefront of ML infrastructure and generative AI, where data and model workflows come together to enable the next generation of intelligent experiences on Apple products and services. We build robust systems that connect scalable data pipelines with advanced ML workflows, accelerating the development of real-world AI applications. Our work spans the full ML lifecycle, from experimentation to deployment, and you’ll play a key role in shaping how AI models are built, optimized, and scaled. We develop a platform for ML data and features that powers advanced GenAI applications. This includes embeddings (generation, evaluation, ANN search, multimodal support), AI Ops, efficient inference, and a modern feature platform designed to streamline experimentation and drive innovation. We’re looking for engineers and researchers passionate about generative models, data-centric ML, and intelligent systems across diverse real-world use cases. With the autonomy to experiment, the scale to make an impact, and the support to take ideas from prototype to production, you’ll work alongside a world-class team to build intelligent, flexible systems that make ML development faster, more reliable, and more creative.
Description
The Apple Cloud AI Platform team enables Apple's next generation of intelligent products by giving Apple's ML engineers and researchers the data systems and large-scale compute they need to build and ship models at Apple's bar for quality and privacy.
Responsibilities:
As a member of the Apple Cloud AI Platform team, your responsibilities will include:
- Design and build the platform behind Apple's largest model builds - ingestion, immutable versioning, lineage, and governance across structured, unstructured, and multimodal data at petabyte scale, so every model run is reproducible from a versioned dataset
- Develop and evolve Python SDKs and core data libraries that ML engineers depend on to access, transform, and load model-ready datasets across every stage of model development
- Build high-throughput data access and loading primitives that feed Apple's largest GPU fleets, keeping workloads compute-bound rather than I/O-bound
- Build and operate distributed data pipelines spanning Spark, Daft, and Rust-based systems for ingestion, transformation, and large-scale data preparation
- Optimize platform components for tight integration with leading ML frameworks - PyTorch, JAX, and TensorFlow - so dataset access is a first-class concern in the model development loop
- Partner with research and product teams to onboard new data sources, and enable rapid iteration on datasets powering GenAI workloads
- Ensure governance is a first-class platform capability: Legal Terms of Use enforcement, privacy controls, and end-to-end data lineage on every dataset version
- Drive efficiency, reliability, and automation across the data plane and control plane that power Apple's ML fleet
- Continuously evolve platform capabilities to support next-generation workloads, including foundation models, multimodal data, and retrieval-augmented systems
- Diagnose, fix, and automate away complex issues across the stack - from ingestion pipelines to dataset APIs to ML framework integrations - to maximize uptime and throughput
Minimum Qualifications
- Strong foundation in machine learning, with hands-on experience across the end-to-end ML workflow - including data preparation, pipeline development, experimentation, evaluation, and deployment
- Expertise in building and running large scale distributed systems
- Familiarity with modern generative techniques (e.g. transformers, diffusion, retrieval-augmented generation)
- Proven experience building and delivering data and machine learning infrastructure in real-world production environments
- Familiarity with fine-tuning workflows, model optimization, and preparing models for scalable inference
- Familiarity with generative AI and its applications in accelerating and enhancing machine learning workflows
- Experience configuring, deploying and troubleshooting large scale production environments
- Experience in designing, building, and maintaining scalable, highly available systems that prioritize ease of use
- Extensive programming experience in Java, Python or Go
- Strong collaboration and communication (verbal and written) skills
- Comfortable navigating ambiguity and evolving technical landscapes, especially in fast-moving areas
- B.S., M.S., or Ph.D. in Computer Science, Computer Engineering, or equivalent practical experience
Preferred Qualifications
Experience in any of the below is preferred:
- Proficiency with one or more modern ML frameworks (PyTorch, JAX, or TensorFlow), particularly the data loading and dataset access layer
- Columnar and lakehouse formats: Parquet, Iceberg, Delta, or Lance
- Distributed data loading frameworks for ML: Ray Data, NVIDIA DALI, WebDataset, or Mosaic StreamingDataset
- Performance engineering for I/O-bound workloads - Arrow, zero-copy, memory mapping, async I/O
- High-throughput object storage access patterns at GPU scale
- Data lineage and governance systems (DataHub, OpenLineage, Unity Catalog, or equivalent)
- Contributions to or operational experience with Spark, Daft, Polars, or DuckDB internals
- Containerization and orchestration technologies (Docker, Kubernetes)
Pay & Benefits
At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $175,000 and $308,500, and your base pay will depend on your skills, qualifications, experience, and location.
Apple employees also have the opportunity to become an Apple shareholder through participation in Apple's discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple's Employee Stock Purchase Plan. You'll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses - including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits
Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.
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Tips for Finding H-1B1 Singapore Sponsorship Jobs in Washington
Verify your degree meets specialty occupation standards
Singapore universities are well-represented in USCIS specialty occupation assessments, but your degree field must directly align with your target role. Cross-check your occupation's educational requirements using O*NET before applying to Washington employers.
Target employers with active LCA filing history
Use Migrate Mate to filter Washington employers by H-1B1 Singapore LCA filings, so you're approaching companies that have already cleared DOL prevailing-wage requirements for your occupation code, not just companies that theoretically could sponsor.
Focus your search on the Eastside tech corridor
Bellevue and Redmond consistently produce H-1B1 Singapore LCA filings in software engineering and cloud services. Employers headquartered on the Eastside often have established Singapore-to-U.S. transfer pipelines, which shortens the employer education phase of your job search.
Confirm your role clears the prevailing wage floor
The H-1B1 Singapore cap is rarely exhausted, but DOL prevailing wage compliance is still mandatory. Use the OFLC Wage Search to look up Level I through Level IV wages for your SOC code in the Seattle-Bellevue-Everett metro before your offer negotiation.
Ask HR whether they've filed H-1B1 Singapore before
Many Washington employers have H-1B visa and TN visa experience but have never processed an H-1B1 Singapore. Asking directly during the offer stage reveals whether their legal team needs to get up to speed, which affects your start date planning.
Get your DS-160 and LCA timing straight before accepting
The H-1B1 Singapore is consular-processed, so your employer's LCA certification must be complete before you can schedule your visa interview. Clarify the employer's filing timeline with USCIS and the U.S. Embassy before you give notice at your current job.
See all 2,543+ H-1B1 Singapore jobs in Washington
Sign up for free to unlock all listings, filter by visa type, and get alerts for new H-1B1 Singapore jobs in Washington.
Get Access To All JobsH-1B1 Singapore Jobs in Washington: Frequently Asked Questions
Which industries in Washington sponsor the most H-1B1 Singapore visas?
Software development, cloud computing, and enterprise technology account for the bulk of H-1B1 Singapore LCA filings in Washington, driven by the concentration of large tech employers in Seattle, Bellevue, and Redmond. Financial services, engineering, and life sciences in the Puget Sound region also generate consistent sponsorship activity for specialty occupation roles.
Which cities in Washington have the most H-1B1 Singapore sponsorship jobs?
Seattle and Bellevue generate the highest volume of H-1B1 Singapore sponsorship activity in Washington. Redmond, Kirkland, and Bothell are also active, particularly in software and biotech. Smaller markets like Spokane and Tacoma have sponsoring employers but at meaningfully lower density than the greater Seattle metro area.
What types of roles typically qualify for H-1B1 Singapore sponsorship in Washington?
Roles that qualify must meet the specialty occupation standard: a minimum of a bachelor's degree in a directly related field is required. In Washington, qualifying roles are concentrated in software engineering, data science, systems architecture, financial analysis, and electrical engineering. Generalist or management roles without a degree-specific requirement typically do not qualify.
How do I find H-1B1 Singapore sponsorship jobs in Washington?
Migrate Mate lets you search Washington employers filtered by H-1B1 Singapore LCA filing history, so you can identify companies that have already completed DOL prevailing-wage certification for your occupation. This removes the guesswork of cold-applying to employers who have never sponsored the visa and focuses your effort on verified sponsoring companies in your target industry.
What is the prevailing wage for H-1B1 Singapore jobs in Washington?
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.