OPT Sr Staff Machine Learning Engineer Jobs
Sr Staff Machine Learning Engineer roles are among the most OPT-friendly positions in tech. Companies filing H-1B visa petitions for ML engineers do so at high rates, and your STEM OPT extension gives you up to three years of work authorization to land and grow in one of these roles.
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Get Access To All JobsINTRODUCTION
At Palo Alto Networks®, we’re united by a shared mission—to protect our digital way of life. We thrive at the intersection of innovation and impact, solving real-world problems with cutting-edge technology and bold thinking. Here, everyone has a voice, and every idea counts. If you’re ready to do the most meaningful work of your career alongside people who are just as passionate as you are, you’re in the right place.
Who We Are
In order to be the cybersecurity partner of choice, we must trailblaze the path and shape the future of our industry. This is something our employees work at each day and is defined by our values: Disruption, Collaboration, Execution, Integrity, and Inclusion. We weave AI into the fabric of everything we do and use it to augment the impact every individual can have. If you are passionate about solving real-world problems and ideating beside the best and the brightest, we invite you to join us!
We believe collaboration thrives in person. That’s why most of our teams work from the office full time, with flexibility when it’s needed. This model supports real-time problem-solving, stronger relationships, and the kind of precision that drives great outcomes.
JOB SUMMARY
Your Impact
Key Responsibilities
- Design, build, and operate production machine learning systems that balance model quality, cost, latency, and reliability in a security-sensitive environment.
- Own the end-to-end lifecycle of ML and LLM components, from problem formulation and model development to production deployment, monitoring, and iterative improvement.
- Integrate ML and LLM-based services with backend systems and data pipelines, ensuring scalability, observability, and safe operation in production.
- Develop and maintain automated training, evaluation, and retraining pipelines, and build data analysis tools to continuously improve model performance as data and threats evolve.
- Partner closely with Product Managers and domain experts to translate product and security requirements into robust ML solutions with clear success metrics.
- Collaborate with software engineers and SREs on release planning, deployment strategies, monitoring, and incident response to ensure reliable and predictable production behavior.
QUALIFICATIONS
Required Qualifications
- MS or Ph.D. in Computer Science or a related field, with a focus on Machine Learning, and 4+ years of industry experience delivering ML systems in production environments.
- Strong problem solver with collaborative team player with clear communication skills, able to work effectively across engineering, product, and SRE teams.
- Solid foundation in Machine Learning, Deep Learning, and NLP, with hands-on experience using modern architectures such as transformer-based models and representation learning techniques.
- Practical experience applying Large Language Models (LLMs) to real-world problems, including text understanding, classification, extraction, summarization, or reasoning over large-scale and noisy data.
- Experience designing, implementing, and operating LLM-powered components in production, including prompt design, model adaptation or fine-tuning, evaluation, and cost/performance optimization.
- Experience with MLOps / AIOps practices for operating ML and LLM systems in production, including model lifecycle management, monitoring, logging, alerting, retraining workflows, and debugging production issues.
- Understanding of model quality, robustness, and safety considerations, including evaluation methodologies, failure modes, and guardrails required for production ML systems in security-sensitive environments.
- Strong experience with ML frameworks, libraries, and tooling (e.g., PyTorch, Tensorflow, Keras, Scikit-learn, Kubeflow), and solid software engineering fundamentals.
- Ability to independently own ML features end-to-end, from problem formulation and system design to implementation, deployment, and iterative improvement in production.
- Proficient in Python, working knowledge of Java, Linux, and shell scripting.
- Experience building and operating services on cloud platforms (GCP and/or AWS) and in containerized environments (Docker, Kubernetes).
Preferred Qualifications
- Familiarity with AI agent–based approaches, such as multi-step inference pipelines, tool-augmented LLM workflows, or systems that combine models, heuristics, and external signals to drive reliable decisions.
- Experience with website content understanding, website classifications, security, or large-scale internet data is a strong plus.
- Familiarity with relational and NoSQL data stores such as MySQL, MongoDB, or similar systems.
- Experience applying LLMs and agentic systems in security-sensitive or high-precision domains is a strong plus.
COMPENSATION DISCLOSURE
The compensation offered for this position will depend on qualifications, experience, and work location. For candidates who receive an offer at the posted level, the starting base salary (for non-sales roles) or base salary + commission target (for sales/commissioned roles) is expected to be the annual range listed below. The offered compensation may also include restricted stock units and a bonus. A description of our employee benefits may be found here.
- $141,000.00 - $228,075.00/yr
OUR COMMITMENT
We’re trailblazers that dream big, take risks, and challenge cybersecurity’s status quo. It’s simple: we can’t accomplish our mission without diverse teams innovating, together.
We are committed to providing reasonable accommodations for all qualified individuals with a disability. If you require assistance or accommodation due to a disability or special need, please contact us at accommodations@paloaltonetworks.com.
Palo Alto Networks is an equal opportunity employer. We celebrate diversity in our workplace, and all qualified applicants will receive consideration for employment without regard to age, ancestry, color, family or medical care leave, gender identity or expression, genetic information, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran status, race, religion, sex (including pregnancy), sexual orientation, or other legally protected characteristics.
All your information will be kept confidential according to EEO guidelines.
Is role eligible for Immigration Sponsorship?: Yes
LOCATION
Santa Clara, California, United States
INTRODUCTION
At Palo Alto Networks®, we’re united by a shared mission—to protect our digital way of life. We thrive at the intersection of innovation and impact, solving real-world problems with cutting-edge technology and bold thinking. Here, everyone has a voice, and every idea counts. If you’re ready to do the most meaningful work of your career alongside people who are just as passionate as you are, you’re in the right place.
Who We Are
In order to be the cybersecurity partner of choice, we must trailblaze the path and shape the future of our industry. This is something our employees work at each day and is defined by our values: Disruption, Collaboration, Execution, Integrity, and Inclusion. We weave AI into the fabric of everything we do and use it to augment the impact every individual can have. If you are passionate about solving real-world problems and ideating beside the best and the brightest, we invite you to join us!
We believe collaboration thrives in person. That’s why most of our teams work from the office full time, with flexibility when it’s needed. This model supports real-time problem-solving, stronger relationships, and the kind of precision that drives great outcomes.
JOB SUMMARY
Your Impact
Key Responsibilities
- Design, build, and operate production machine learning systems that balance model quality, cost, latency, and reliability in a security-sensitive environment.
- Own the end-to-end lifecycle of ML and LLM components, from problem formulation and model development to production deployment, monitoring, and iterative improvement.
- Integrate ML and LLM-based services with backend systems and data pipelines, ensuring scalability, observability, and safe operation in production.
- Develop and maintain automated training, evaluation, and retraining pipelines, and build data analysis tools to continuously improve model performance as data and threats evolve.
- Partner closely with Product Managers and domain experts to translate product and security requirements into robust ML solutions with clear success metrics.
- Collaborate with software engineers and SREs on release planning, deployment strategies, monitoring, and incident response to ensure reliable and predictable production behavior.
QUALIFICATIONS
Required Qualifications
- MS or Ph.D. in Computer Science or a related field, with a focus on Machine Learning, and 4+ years of industry experience delivering ML systems in production environments.
- Strong problem solver with collaborative team player with clear communication skills, able to work effectively across engineering, product, and SRE teams.
- Solid foundation in Machine Learning, Deep Learning, and NLP, with hands-on experience using modern architectures such as transformer-based models and representation learning techniques.
- Practical experience applying Large Language Models (LLMs) to real-world problems, including text understanding, classification, extraction, summarization, or reasoning over large-scale and noisy data.
- Experience designing, implementing, and operating LLM-powered components in production, including prompt design, model adaptation or fine-tuning, evaluation, and cost/performance optimization.
- Experience with MLOps / AIOps practices for operating ML and LLM systems in production, including model lifecycle management, monitoring, logging, alerting, retraining workflows, and debugging production issues.
- Understanding of model quality, robustness, and safety considerations, including evaluation methodologies, failure modes, and guardrails required for production ML systems in security-sensitive environments.
- Strong experience with ML frameworks, libraries, and tooling (e.g., PyTorch, Tensorflow, Keras, Scikit-learn, Kubeflow), and solid software engineering fundamentals.
- Ability to independently own ML features end-to-end, from problem formulation and system design to implementation, deployment, and iterative improvement in production.
- Proficient in Python, working knowledge of Java, Linux, and shell scripting.
- Experience building and operating services on cloud platforms (GCP and/or AWS) and in containerized environments (Docker, Kubernetes).
Preferred Qualifications
- Familiarity with AI agent–based approaches, such as multi-step inference pipelines, tool-augmented LLM workflows, or systems that combine models, heuristics, and external signals to drive reliable decisions.
- Experience with website content understanding, website classifications, security, or large-scale internet data is a strong plus.
- Familiarity with relational and NoSQL data stores such as MySQL, MongoDB, or similar systems.
- Experience applying LLMs and agentic systems in security-sensitive or high-precision domains is a strong plus.
COMPENSATION DISCLOSURE
The compensation offered for this position will depend on qualifications, experience, and work location. For candidates who receive an offer at the posted level, the starting base salary (for non-sales roles) or base salary + commission target (for sales/commissioned roles) is expected to be the annual range listed below. The offered compensation may also include restricted stock units and a bonus. A description of our employee benefits may be found here.
- $141,000.00 - $228,075.00/yr
OUR COMMITMENT
We’re trailblazers that dream big, take risks, and challenge cybersecurity’s status quo. It’s simple: we can’t accomplish our mission without diverse teams innovating, together.
We are committed to providing reasonable accommodations for all qualified individuals with a disability. If you require assistance or accommodation due to a disability or special need, please contact us at accommodations@paloaltonetworks.com.
Palo Alto Networks is an equal opportunity employer. We celebrate diversity in our workplace, and all qualified applicants will receive consideration for employment without regard to age, ancestry, color, family or medical care leave, gender identity or expression, genetic information, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran status, race, religion, sex (including pregnancy), sexual orientation, or other legally protected characteristics.
All your information will be kept confidential according to EEO guidelines.
Is role eligible for Immigration Sponsorship?: Yes
LOCATION
Santa Clara, California, United States
Tips for Finding OPT Sponsorship as a Sr Staff Machine Learning Engineer
Target companies with active H-1B ML hiring history
Search DOL LCA disclosure data for employers who have filed Labor Condition Applications for machine learning roles. Companies with consistent ML filings are already set up to sponsor OPT students and are far less likely to reject your application over visa status.
Position your STEM OPT extension as a runway, not a risk
Sr Staff roles involve long ramp-up periods. Frame your three-year STEM OPT extension as time for meaningful impact before H-1B visa sponsorship is needed. That framing shifts the conversation from immigration overhead to hiring timeline alignment.
Demonstrate end-to-end ML ownership, not just model building
Sr Staff roles require systems thinking across the full ML lifecycle. Highlight experience owning production pipelines, model governance, and cross-functional technical strategy. Generic model-training experience alone won't clear the bar at this seniority level.
Apply to large tech and AI-native companies first
Organizations like Google, Meta, Amazon, and dedicated AI labs have established immigration programs and dedicated legal teams. They can move faster on OPT authorization and H-1B sponsorship than companies sponsoring their first international hire.
Get your OPT STEM extension application in early
USCIS recommends filing your STEM OPT extension up to 90 days before your initial OPT expires. Processing delays are common. Filing late puts your work authorization at risk and can complicate an active job search at this level.
Prepare a sponsorship conversation for late-stage interviews
At Sr Staff level, hiring decisions involve legal and finance teams. Be ready to explain your OPT timeline, STEM extension eligibility, and H-1B cap-subject filing window clearly. Employers who are undecided often need factual clarity, not reassurance.
Sr Staff Machine Learning Engineer OPT: Frequently Asked Questions
Can I work as a Sr Staff Machine Learning Engineer on OPT?
Yes. F-1 students with approved OPT can work in Sr Staff Machine Learning Engineer roles as long as the job is directly related to your field of study. Most ML and computer science degrees satisfy this requirement. If you have a STEM-designated degree, you can apply for a 24-month extension beyond your initial 12 months of OPT, giving you up to three years total.
Do Sr Staff Machine Learning Engineer roles commonly offer H-1B sponsorship?
Yes, more consistently than most job categories. Sr Staff ML engineers are in high demand, and companies competing for this talent routinely sponsor H-1B petitions. Employers filing Labor Condition Applications for machine learning titles include major tech companies and AI-focused startups alike. You can browse OPT-friendly Sr Staff ML roles on Migrate Mate, which filters specifically for positions open to F-1 students.
What qualifies as a STEM-eligible degree for a Sr Staff ML Engineer role?
Degrees in computer science, electrical engineering, statistics, applied mathematics, and data science are all typically STEM-designated under ICE's STEM Designated Degree Program List. Your school's DSO can confirm whether your specific program qualifies. If it does, you can apply for the 24-month STEM OPT extension, which must be filed with USCIS before your initial OPT expires.
How does the 60-day grace period affect my job search for Sr Staff ML roles?
If your OPT employment ends, you have a 60-day grace period to find new employment, transfer your SEVIS record, or depart the United States. Sr Staff-level hiring processes often take 6 to 12 weeks, so starting your search before your current authorization ends is critical. You cannot work during the grace period, only remain in status while you search.
What should I look for to confirm a Sr Staff ML Engineer role is OPT-compatible?
The role must be full-time (or authorized part-time if your OPT is part-time authorized), directly related to your degree field, and with a company that has an EIN and meets DSS employer requirements for STEM OPT. Check that the employer is willing to sign your Form I-983 Training Plan if you are on STEM OPT extension, as that reporting obligation is mandatory and some smaller employers are unfamiliar with it.