AI ML Engineering Jobs for OPT Students
AI ML Engineering roles are among the most OPT-friendly in tech, with high demand for F-1 students holding STEM OPT extensions. Most positions qualify for the 24-month STEM extension, giving you up to three years of work authorization while pursuing H-1B sponsorship through employers actively hiring international talent.
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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 centres 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 emphasise 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 5+ years of experience as an applied data scientist / machine learning engineer.
Essential Skills
- 5+ 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).
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.
Same Posting Description for Internal and External Candidates
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 centres 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 emphasise 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 5+ years of experience as an applied data scientist / machine learning engineer.
Essential Skills
- 5+ 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).
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.
Same Posting Description for Internal and External Candidates
How to Get Visa Sponsorship in AI ML Engineering
Target STEM-designated programs
Confirm your degree program carries an approved STEM CIP code before applying. AI and ML engineering roles almost universally qualify, but your specific degree classification determines STEM OPT eligibility, not the job title itself.
Apply to companies with H-1B sponsorship track records
Prioritize employers who have sponsored H-1B petitions in prior years. Companies regularly hiring ML engineers, including large tech firms and AI-focused startups, are far more likely to support your transition from OPT to long-term work authorization.
Highlight framework proficiency upfront
Employers screening for OPT candidates want to see TensorFlow, PyTorch, or JAX listed prominently on your resume. Burying technical skills below education or projects costs you interviews with hiring managers scanning for specific toolchain experience.
Negotiate a start date that aligns with your EAD
You cannot legally begin work before your EAD card arrives and your OPT start date passes. Be direct with employers about your availability date early in the process to avoid offers that require immediate starts you cannot meet.
Document your OPT reporting obligations for employers
Many small and mid-size companies have not hired OPT students before. Explaining the E-Verify requirement and DSO reporting timeline upfront positions you as informed and organized, which builds employer confidence in moving forward with your offer.
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Get Access To All JobsFrequently Asked Questions
Do AI ML Engineering jobs qualify for the 24-month STEM OPT extension?
Yes, in almost every case. AI and ML engineering roles typically fall under computer science, electrical engineering, or data science degree programs, all of which carry approved STEM CIP codes. You still need to confirm your specific degree qualifies through your DSO before applying for the STEM extension, since eligibility is based on degree classification, not the job title.
What does an employer need to do to hire me on OPT for an AI ML role?
For standard OPT, your employer simply needs to be a legitimate U.S. organization that can offer you work in a field related to your degree. For STEM OPT, the employer must be enrolled in E-Verify, sign a formal training plan with you (Form I-983), and agree to report material changes in your employment to your DSO. Most established tech companies and funded AI startups already meet these requirements.
Can I work as an AI ML contractor or consultant on OPT?
Self-employment and independent contracting are not permitted under OPT. You need to be employed by a U.S. company in a traditional employer-employee relationship. Third-party staffing arrangements can qualify, but USCIS scrutinizes them closely for STEM OPT extensions, requiring clear evidence that the end client, not just the staffing agency, supervises your day-to-day technical work.
How do I find AI ML Engineering jobs where employers are open to OPT students?
The most reliable approach is to search within platforms that filter specifically for visa-sponsoring employers. Migrate Mate is built for this, letting you browse AI and ML engineering roles where employers have an established record of hiring F-1 OPT candidates. Searching general job boards without visa filters means most of your applications go to companies that will screen you out at the authorization question.
What happens to my OPT if my AI ML Engineering job ends unexpectedly?
You have a 90-day aggregate unemployment limit across your entire OPT period, reduced to 60 days if you are on STEM OPT extension. Days start accumulating from your first authorized OPT start date, not from when your job ends. If you exceed the limit, your OPT authorization is automatically terminated. Report any employment changes to your DSO immediately, since they track this and can advise on your remaining days.
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