Finance H-1B1 Chile Sponsorship Jobs in Washington
Washington's finance sector spans Seattle's major bank headquarters, asset management firms, and fintech companies with active international hiring. Chilean nationals on the H-1B1 visa can work in financial analysis, risk management, compliance, and treasury roles at employers including JPMorgan Chase, Wells Fargo, and Washington Federal. No lottery applies to the H-1B1, making Washington a practical target for finance professionals from Chile.
See All Finance JobsOverview
Showing 5 of 7+ Finance H-1B1 Chile Sponsorship Jobs in Washington
Have you applied for this role?
Have you applied for this role?
Have you applied for this role?
Have you applied for this role?
Have you applied for this role?
See all Finance H-1B1 Chile Sponsorship Jobs in Washington
Sign up for free to unlock all listings, filter by visa type, and get alerts for new Finance H-1B1 Chile Sponsorship Jobs in Washington.
Get Access To All JobsJob Description
WHO WE ARE
Goldman Sachs is a leading global investment banking, securities and investment management firm that provides a wide range of services worldwide to a substantial and diversified client base that includes corporations, financial institutions, governments and high net-worth individuals. Founded in 1869, it is one of the oldest and largest investment banking firms. The firm is headquartered in New York and maintains offices in London, Bangalore, Frankfurt, Tokyo, Hong Kong and other major financial centers around the world. We are committed to growing our distinctive Culture and holding to our core values which always place our client's interests first. These values are reflected in our Business Principles, which emphasize integrity, commitment to excellence, innovation and teamwork.
Business Unit Overview
Enterprise Technology Operations (ETO) is a Business Unit within Core Engineering focused on running scalable production management services with a mandate of operational excellence and operational risk reduction achieved through large scale automation, best-in-class engineering, and application of data science and machine learning. The Production Runtime Experience (PRX) team in ETO applies software engineering and machine learning to production management services, processes, and activities to streamline monitoring, alerting, automation, and workflows.
TEAM OVERVIEW
The Machine Learning and Artificial Intelligence team in PRX applies advanced ML and GenAI to reduce the risk and cost of operating the firm’s large-scale compute infrastructure and extensive application estate. Building on strengths in statistical modelling, anomaly detection, predictive modelling, and time-series forecasting, we leverage foundational LLM Models to orchestrate multi-agent systems for automated production management services. By unifying classical ML with agentic AI, we deliver reliable, explainable, and cost-efficient operations at scale.
ROLE AND RESPONSIBILITIES
In this role, you will be responsible for launching and implementing GenAI agentic solutions aimed at reducing the risk and cost of managing large-scale production environments with varying complexities. You will address various production runtime challenges by developing agentic AI solutions that can diagnose, reason, and take actions in production environments to improve productivity and address issues related to production support.
What You’ll Do
- Build agentic AI systems: Design and implement tool-calling agents that combine retrieval, structured reasoning, and secure action execution (function calling, change orchestration, policy enforcement) following MCP protocol. Engineer robust guardrails for safety, compliance, and least-privilege access.
- Productionize LLMs: Build evaluation framework for open-source and foundational LLMs; implement retrieval pipelines, prompt synthesis, response validation, and self-correction loops tailored to production operations.
- Integrate with runtime ecosystems: Connect agents to observability, incident management, and deployment systems to enable automated diagnostics, runbook execution, remediation, and post-incident summarization with full traceability.
- Collaborate directly with users: Partner with production engineers, and application teams to translate production pain points into agentic AI roadmaps; define objective functions linked to reliability, risk reduction, and cost; and deliver auditable, business-aligned outcomes.
- Safety, reliability, and governance: Build validator models, adversarial prompts, and policy checks into the stack; enforce deterministic fallbacks, circuit breakers, and rollback strategies; instrument continuous evaluations for usefulness, correctness, and risk.
- Scale and performance: Optimize cost and latency via prompt engineering, context management, caching, model routing, and distillation; leverage batching, streaming, and parallel tool-calls to meet stringent SLOs under real-world load.
- Build a RAG pipeline: Curate domain-knowledge; build data-quality validation framework; establish feedback loops and milestone framework maintain knowledge freshness.
- Raise the bar: Drive design reviews, experiment rigor, and high-quality engineering practices; mentor peers on agent architectures, evaluation methodologies, and safe deployment patterns.
Qualifications
A Bachelor’s degree (Masters/ PhD preferred) in a computational field (Computer Science, Applied Mathematics, Engineering, or in a related quantitative discipline), with 7+ years of experience as an applied data scientist / machine learning engineer.
Essential Skills
- 7+ years of software development in one or more languages (Python, C/C++, Go, Java); strong hands-on experience building and maintaining large-scale Python applications preferred.
- 3+ years designing, architecting, testing, and launching production ML systems, including model deployment/serving, evaluation and monitoring, data processing pipelines, and model fine-tuning workflows.
- Practical experience with Large Language Models (LLMs): API integration, prompt engineering, finetuning/adaptation, and building applications using RAG and tool-using agents (vector retrieval, function calling, secure tool execution).
- Understanding of different LLMs, both commercial and open source, and their capabilities (e.g., OpenAI, Gemini, Llama, Qwen, Claude).
- Solid grasp of applied statistics, core ML concepts, algorithms, and data structures to deliver efficient and reliable solutions.
- Strong analytical problem-solving, ownership, and urgency; ability to communicate complex ideas simply and collaborate effectively across global teams with a focus on measurable business impact.
Preferred
- Proficiency building and operating on cloud infrastructure (ideally AWS), including containerized services (ECS/EKS), serverless (Lambda), data services (S3, DynamoDB, Redshift), orchestration (Step Functions), model serving (SageMaker), and infra-as-code (Terraform/CloudFormation).
Salary Range
The expected base salary for this Seattle, Washington United States-based position is $150,000-$250,000. In addition, you may be eligible for a discretionary bonus if you are an active employee as of fiscal year-end.
YOUR CAREER
Goldman Sachs is a meritocracy where you will be given all the tools to advance your career. At Goldman Sachs, you will have access to excellent training programs designed to improve multiple facets of your skill portfolio. Our in-house training program, “Goldman Sachs University” offers a comprehensive series of courses that you will have access to as your career progresses. Goldman Sachs University has an impressive catalogue of courses which span technical, business and leadership skills.
Job Description
WHO WE ARE
Goldman Sachs is a leading global investment banking, securities and investment management firm that provides a wide range of services worldwide to a substantial and diversified client base that includes corporations, financial institutions, governments and high net-worth individuals. Founded in 1869, it is one of the oldest and largest investment banking firms. The firm is headquartered in New York and maintains offices in London, Bangalore, Frankfurt, Tokyo, Hong Kong and other major financial centers around the world. We are committed to growing our distinctive Culture and holding to our core values which always place our client's interests first. These values are reflected in our Business Principles, which emphasize integrity, commitment to excellence, innovation and teamwork.
Business Unit Overview
Enterprise Technology Operations (ETO) is a Business Unit within Core Engineering focused on running scalable production management services with a mandate of operational excellence and operational risk reduction achieved through large scale automation, best-in-class engineering, and application of data science and machine learning. The Production Runtime Experience (PRX) team in ETO applies software engineering and machine learning to production management services, processes, and activities to streamline monitoring, alerting, automation, and workflows.
TEAM OVERVIEW
The Machine Learning and Artificial Intelligence team in PRX applies advanced ML and GenAI to reduce the risk and cost of operating the firm’s large-scale compute infrastructure and extensive application estate. Building on strengths in statistical modelling, anomaly detection, predictive modelling, and time-series forecasting, we leverage foundational LLM Models to orchestrate multi-agent systems for automated production management services. By unifying classical ML with agentic AI, we deliver reliable, explainable, and cost-efficient operations at scale.
ROLE AND RESPONSIBILITIES
In this role, you will be responsible for launching and implementing GenAI agentic solutions aimed at reducing the risk and cost of managing large-scale production environments with varying complexities. You will address various production runtime challenges by developing agentic AI solutions that can diagnose, reason, and take actions in production environments to improve productivity and address issues related to production support.
What You’ll Do
- Build agentic AI systems: Design and implement tool-calling agents that combine retrieval, structured reasoning, and secure action execution (function calling, change orchestration, policy enforcement) following MCP protocol. Engineer robust guardrails for safety, compliance, and least-privilege access.
- Productionize LLMs: Build evaluation framework for open-source and foundational LLMs; implement retrieval pipelines, prompt synthesis, response validation, and self-correction loops tailored to production operations.
- Integrate with runtime ecosystems: Connect agents to observability, incident management, and deployment systems to enable automated diagnostics, runbook execution, remediation, and post-incident summarization with full traceability.
- Collaborate directly with users: Partner with production engineers, and application teams to translate production pain points into agentic AI roadmaps; define objective functions linked to reliability, risk reduction, and cost; and deliver auditable, business-aligned outcomes.
- Safety, reliability, and governance: Build validator models, adversarial prompts, and policy checks into the stack; enforce deterministic fallbacks, circuit breakers, and rollback strategies; instrument continuous evaluations for usefulness, correctness, and risk.
- Scale and performance: Optimize cost and latency via prompt engineering, context management, caching, model routing, and distillation; leverage batching, streaming, and parallel tool-calls to meet stringent SLOs under real-world load.
- Build a RAG pipeline: Curate domain-knowledge; build data-quality validation framework; establish feedback loops and milestone framework maintain knowledge freshness.
- Raise the bar: Drive design reviews, experiment rigor, and high-quality engineering practices; mentor peers on agent architectures, evaluation methodologies, and safe deployment patterns.
Qualifications
A Bachelor’s degree (Masters/ PhD preferred) in a computational field (Computer Science, Applied Mathematics, Engineering, or in a related quantitative discipline), with 7+ years of experience as an applied data scientist / machine learning engineer.
Essential Skills
- 7+ years of software development in one or more languages (Python, C/C++, Go, Java); strong hands-on experience building and maintaining large-scale Python applications preferred.
- 3+ years designing, architecting, testing, and launching production ML systems, including model deployment/serving, evaluation and monitoring, data processing pipelines, and model fine-tuning workflows.
- Practical experience with Large Language Models (LLMs): API integration, prompt engineering, finetuning/adaptation, and building applications using RAG and tool-using agents (vector retrieval, function calling, secure tool execution).
- Understanding of different LLMs, both commercial and open source, and their capabilities (e.g., OpenAI, Gemini, Llama, Qwen, Claude).
- Solid grasp of applied statistics, core ML concepts, algorithms, and data structures to deliver efficient and reliable solutions.
- Strong analytical problem-solving, ownership, and urgency; ability to communicate complex ideas simply and collaborate effectively across global teams with a focus on measurable business impact.
Preferred
- Proficiency building and operating on cloud infrastructure (ideally AWS), including containerized services (ECS/EKS), serverless (Lambda), data services (S3, DynamoDB, Redshift), orchestration (Step Functions), model serving (SageMaker), and infra-as-code (Terraform/CloudFormation).
Salary Range
The expected base salary for this Seattle, Washington United States-based position is $150,000-$250,000. In addition, you may be eligible for a discretionary bonus if you are an active employee as of fiscal year-end.
YOUR CAREER
Goldman Sachs is a meritocracy where you will be given all the tools to advance your career. At Goldman Sachs, you will have access to excellent training programs designed to improve multiple facets of your skill portfolio. Our in-house training program, “Goldman Sachs University” offers a comprehensive series of courses that you will have access to as your career progresses. Goldman Sachs University has an impressive catalogue of courses which span technical, business and leadership skills.
Finance Job Roles in Washington
See all Finance Jobs in Washington
Sign up for free to filter by visa type, set job alerts, and find employers with verified sponsorship history.
Search Finance Jobs in WashingtonFinance Jobs in Washington: Frequently Asked Questions
Which finance companies in Washington sponsor H-1B1 Chile visas?
Washington-based employers with documented H-1B1 sponsorship activity in finance include major banks such as JPMorgan Chase, Wells Fargo, and Washington Federal, along with regional investment firms and fintech companies concentrated in Seattle and Bellevue. Insurance carriers and asset management firms operating in the Puget Sound area have also filed H-1B1 Labor Condition Applications for finance roles. Sponsorship patterns vary by employer size and role type, so reviewing LCA disclosure data through OFLC is advisable.
Which cities in Washington have the most finance H-1B1 Chile sponsorship jobs?
Seattle accounts for the highest concentration of finance H-1B1 Chile sponsorship activity in Washington, driven by its density of bank headquarters, investment management offices, and fintech firms. Bellevue and Redmond host a growing number of financial services employers tied to the broader tech corridor. Spokane has a smaller but active regional banking presence. For roles in insurance and commercial lending, Tacoma and Olympia also see periodic H-1B1 LCA filings in finance.
What types of finance roles typically qualify for H-1B1 Chile sponsorship?
Roles that qualify must meet the H-1B1 specialty occupation standard, requiring at least a bachelor's degree in a directly related field. In finance, positions that commonly qualify include financial analyst, investment analyst, credit risk analyst, compliance officer, treasury analyst, and quantitative researcher. General bookkeeping or administrative finance roles typically do not qualify. O*NET occupation profiles can help verify whether a specific finance job title meets the degree-requirement threshold for specialty occupation classification.
How do I find finance H-1B1 Chile sponsorship jobs in Washington?
Migrate Mate is built specifically for international job seekers and surfaces finance roles at Washington employers with verified H-1B1 sponsorship history, drawn from DOL Labor Condition Application data. You can filter by visa type, state, and industry to see which Seattle, Bellevue, or Spokane employers have actively filed for H-1B1 Chile positions in financial analysis, compliance, or risk management, narrowing your search to companies already familiar with the H-1B1 process.
Are there state-specific considerations for H-1B1 Chile finance sponsorship in Washington?
Washington has no state income tax, which affects how compensation packages are structured at finance employers and can influence prevailing wage calculations. Finance professionals should confirm that a prospective employer holds an active certified LCA for their specific role and worksite location, since Washington worksites in Seattle and Bellevue are treated as separate for DOL prevailing wage purposes. Employers in Washington's heavily regulated banking sector may also require FINRA licensing alongside the H-1B1 filing process.
What is the prevailing wage for H-1B1 Chile finance 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.