Machine Learning Engineer Jobs at Scale AI with Visa Sponsorship
Scale AI hires Machine Learning Engineers to build and evaluate the data pipelines and model training workflows powering frontier AI systems. The company sponsors a range of work visas for this function, making it a viable target for international candidates with strong ML research or production engineering backgrounds.
See All Machine Learning Engineer at Scale AI JobsOverview
Showing 5 of 77+ Machine Learning Engineer Jobs at Scale AI jobs


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 77+ Machine Learning Engineer Jobs at Scale AI
Sign up for free to unlock all listings, filter by visa type, and get alerts for new Machine Learning Engineer Jobs at Scale AI.
Get Access To All Jobs
INTRODUCTION
Scale AI is the data foundation for AI, helping organizations build and deploy reliable production AI applications. We partner with leading enterprises and government organizations to accelerate their AI initiatives through our data annotation platform, generative AI solutions, and enterprise AI capabilities.
ABOUT THE GENERAL AGENTS TEAM
The General Agents team, part of Scale’s Enterprise organization, builds robust general agents for customer use cases and applications. The team sits at the intersection of frontier agent development and real-world deployment, translating state-of-the-art reasoning and agentic capabilities into reliable, production-grade systems that drive real economic value. Our agents are scalable systems built around recurring enterprise problem domains, with a strong emphasis on generalization, extensibility, and deployment across many customers.
ABOUT THE ROLE
As a Senior/Staff Machine Learning Engineer (MLE) on the General Agents team, you’ll play a critical role in designing, building, and deploying production-ready AI agents that solve high-impact enterprise problems. You will work across the full agent lifecycle—from model and system design to evaluation, deployment, and iteration—bridging cutting-edge agentic techniques with the constraints and requirements of real customer environments. You will:
- Design and implement end-to-end agent systems that combine LLM reasoning, tool use, memory, and control logic to solve recurring enterprise use cases.
- Build scalable, reliable agent architectures that can be deployed across many customers with varying data, tools, and constraints.
- Develop evaluation frameworks, datasets, environments, and metrics to measure agent performance, reliability, and business impact in production settings.
- Collaborate closely with product managers, customers, data annotators, and other engineering teams to translate enterprise requirements into robust agent designs.
- Productionize frontier agent techniques (e.g., planning, multi-step reasoning and tool-use, multi-agent patterns) into maintainable, observable systems.
- Own deployment, monitoring, and iteration of agent systems, including failure analysis and continuous improvement based on real-world usage.
- Contribute to technical direction and architectural decisions for general agent development best practices and methods, with increasing scope and leadership at the Staff level.
BASIC QUALIFICATIONS
Ideally you’d have:
- 5+ years of experience building and deploying machine learning or AI systems for real-world, production use cases.
- Strong engineering fundamentals, supported by a Bachelor’s and/or Master’s degree in Computer Science, Machine Learning, AI, or equivalent practical experience.
- Deep understanding of modern LLMs, prompt-, context-, and system-level optimization, and agentic system design.
- Proven proficiency in Python, including writing production-quality, testable, and maintainable code.
- Experience building systems that integrate models with external tools, APIs, databases, and services.
- Ability to operate in ambiguous problem spaces, balancing research-driven approaches with pragmatic product constraints.
- Strong communication skills and comfort working in customer-facing or cross-functional environments.
PREFERRED QUALIFICATIONS
Nice-to-haves:
- Hands-on experience building AI agents using modern generative AI stacks (OpenAI APIs, commercial or open-source LLMs).
- Experience with agent frameworks, orchestration layers, or workflow systems (e.g., tool calling, planners, multi-agent setups).
- Familiarity with evaluation, monitoring, and observability for LLM-powered systems in production.
- Experience deploying ML systems in cloud environments and operating them at scale.
- Experience fine-tuning or adapting foundation models using methods like supervised fine-tuning (SFT), reinforcement learning with verifiable rewards (RLVR), and low-rank adaptation (LoRA) to improve agent performance on domain-specific tasks.
- Interest in shaping the future of general-purpose enterprise agents and their real-world impact.
COMPENSATION
Compensation packages at Scale for eligible roles include base salary, equity, and benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position, determined by work location and additional factors, including job-related skills, experience, interview performance, and relevant education or training. Scale employees in eligible roles are also granted equity based compensation, subject to Board of Director approval. Your recruiter can share more about the specific salary range for your preferred location during the hiring process, and confirm whether the hired role will be eligible for equity grant. You’ll also receive benefits including, but not limited to: Comprehensive health, dental and vision coverage, retirement benefits, a learning and development stipend, and generous PTO. Additionally, this role may be eligible for additional benefits such as a commuter stipend.
LOCATION
Please reference the job posting's subtitle for where this position will be located. For pay transparency purposes, the base salary range for this full-time position in the locations of San Francisco, New York, Seattle is: $218,000—$273,000 USD.
PLEASE NOTE:
Our policy requires a 90-day waiting period before reconsidering candidates for the same role. This allows us to ensure a fair and thorough evaluation of all applicants.
About us:
At Scale, our mission is to develop reliable AI systems for the world's most important decisions. Our products provide the high-quality data and full-stack technologies that power the world's leading models, and help enterprises and governments build, deploy, and oversee AI applications that deliver real impact. We work closely with industry leaders like Meta, Cisco, DLA Piper, Mayo Clinic, Time Inc., the Government of Qatar, and U.S. government agencies including the Army and Air Force. We are expanding our team to accelerate the development of AI applications.
We believe that everyone should be able to bring their whole selves to work, which is why we are proud to be an inclusive and equal opportunity workplace. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability status, gender identity or Veteran status.
We are committed to working with and providing reasonable accommodations to applicants with physical and mental disabilities. If you need assistance and/or a reasonable accommodation in the application or recruiting process due to a disability, please contact us at accommodations@scale.com. Please see the United States Department of Labor's Know Your Rights poster for additional information.
We comply with the United States Department of Labor's Pay Transparency provision.
PLEASE NOTE:
We collect, retain and use personal data for our professional business purposes, including notifying you of job opportunities that may be of interest and sharing with our affiliates. We limit the personal data we collect to that which we believe is appropriate and necessary to manage applicants’ needs, provide our services, and comply with applicable laws. Any information we collect in connection with your application will be treated in accordance with our internal policies and programs designed to protect personal data. Please see our privacy policy for additional information.

INTRODUCTION
Scale AI is the data foundation for AI, helping organizations build and deploy reliable production AI applications. We partner with leading enterprises and government organizations to accelerate their AI initiatives through our data annotation platform, generative AI solutions, and enterprise AI capabilities.
ABOUT THE GENERAL AGENTS TEAM
The General Agents team, part of Scale’s Enterprise organization, builds robust general agents for customer use cases and applications. The team sits at the intersection of frontier agent development and real-world deployment, translating state-of-the-art reasoning and agentic capabilities into reliable, production-grade systems that drive real economic value. Our agents are scalable systems built around recurring enterprise problem domains, with a strong emphasis on generalization, extensibility, and deployment across many customers.
ABOUT THE ROLE
As a Senior/Staff Machine Learning Engineer (MLE) on the General Agents team, you’ll play a critical role in designing, building, and deploying production-ready AI agents that solve high-impact enterprise problems. You will work across the full agent lifecycle—from model and system design to evaluation, deployment, and iteration—bridging cutting-edge agentic techniques with the constraints and requirements of real customer environments. You will:
- Design and implement end-to-end agent systems that combine LLM reasoning, tool use, memory, and control logic to solve recurring enterprise use cases.
- Build scalable, reliable agent architectures that can be deployed across many customers with varying data, tools, and constraints.
- Develop evaluation frameworks, datasets, environments, and metrics to measure agent performance, reliability, and business impact in production settings.
- Collaborate closely with product managers, customers, data annotators, and other engineering teams to translate enterprise requirements into robust agent designs.
- Productionize frontier agent techniques (e.g., planning, multi-step reasoning and tool-use, multi-agent patterns) into maintainable, observable systems.
- Own deployment, monitoring, and iteration of agent systems, including failure analysis and continuous improvement based on real-world usage.
- Contribute to technical direction and architectural decisions for general agent development best practices and methods, with increasing scope and leadership at the Staff level.
BASIC QUALIFICATIONS
Ideally you’d have:
- 5+ years of experience building and deploying machine learning or AI systems for real-world, production use cases.
- Strong engineering fundamentals, supported by a Bachelor’s and/or Master’s degree in Computer Science, Machine Learning, AI, or equivalent practical experience.
- Deep understanding of modern LLMs, prompt-, context-, and system-level optimization, and agentic system design.
- Proven proficiency in Python, including writing production-quality, testable, and maintainable code.
- Experience building systems that integrate models with external tools, APIs, databases, and services.
- Ability to operate in ambiguous problem spaces, balancing research-driven approaches with pragmatic product constraints.
- Strong communication skills and comfort working in customer-facing or cross-functional environments.
PREFERRED QUALIFICATIONS
Nice-to-haves:
- Hands-on experience building AI agents using modern generative AI stacks (OpenAI APIs, commercial or open-source LLMs).
- Experience with agent frameworks, orchestration layers, or workflow systems (e.g., tool calling, planners, multi-agent setups).
- Familiarity with evaluation, monitoring, and observability for LLM-powered systems in production.
- Experience deploying ML systems in cloud environments and operating them at scale.
- Experience fine-tuning or adapting foundation models using methods like supervised fine-tuning (SFT), reinforcement learning with verifiable rewards (RLVR), and low-rank adaptation (LoRA) to improve agent performance on domain-specific tasks.
- Interest in shaping the future of general-purpose enterprise agents and their real-world impact.
COMPENSATION
Compensation packages at Scale for eligible roles include base salary, equity, and benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position, determined by work location and additional factors, including job-related skills, experience, interview performance, and relevant education or training. Scale employees in eligible roles are also granted equity based compensation, subject to Board of Director approval. Your recruiter can share more about the specific salary range for your preferred location during the hiring process, and confirm whether the hired role will be eligible for equity grant. You’ll also receive benefits including, but not limited to: Comprehensive health, dental and vision coverage, retirement benefits, a learning and development stipend, and generous PTO. Additionally, this role may be eligible for additional benefits such as a commuter stipend.
LOCATION
Please reference the job posting's subtitle for where this position will be located. For pay transparency purposes, the base salary range for this full-time position in the locations of San Francisco, New York, Seattle is: $218,000—$273,000 USD.
PLEASE NOTE:
Our policy requires a 90-day waiting period before reconsidering candidates for the same role. This allows us to ensure a fair and thorough evaluation of all applicants.
About us:
At Scale, our mission is to develop reliable AI systems for the world's most important decisions. Our products provide the high-quality data and full-stack technologies that power the world's leading models, and help enterprises and governments build, deploy, and oversee AI applications that deliver real impact. We work closely with industry leaders like Meta, Cisco, DLA Piper, Mayo Clinic, Time Inc., the Government of Qatar, and U.S. government agencies including the Army and Air Force. We are expanding our team to accelerate the development of AI applications.
We believe that everyone should be able to bring their whole selves to work, which is why we are proud to be an inclusive and equal opportunity workplace. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability status, gender identity or Veteran status.
We are committed to working with and providing reasonable accommodations to applicants with physical and mental disabilities. If you need assistance and/or a reasonable accommodation in the application or recruiting process due to a disability, please contact us at accommodations@scale.com. Please see the United States Department of Labor's Know Your Rights poster for additional information.
We comply with the United States Department of Labor's Pay Transparency provision.
PLEASE NOTE:
We collect, retain and use personal data for our professional business purposes, including notifying you of job opportunities that may be of interest and sharing with our affiliates. We limit the personal data we collect to that which we believe is appropriate and necessary to manage applicants’ needs, provide our services, and comply with applicable laws. Any information we collect in connection with your application will be treated in accordance with our internal policies and programs designed to protect personal data. Please see our privacy policy for additional information.
See all 77+ Machine Learning Engineer at Scale AI jobs
Sign up for free to unlock all listings, filter by visa type, and get alerts for new Machine Learning Engineer at Scale AI roles.
Get Access To All JobsTips for Finding Machine Learning Engineer Jobs at Scale AI Jobs
Align Your Portfolio to Frontier AI Work
Scale AI evaluates ML Engineers on hands-on model evaluation, RLHF pipelines, and large-scale data labeling infrastructure. Before applying, ensure your GitHub and resume reflect production-level ML work, not just academic or Kaggle projects.
Target Roles That Match Your Degree Field
H-1B specialty occupation approval requires a direct connection between your degree field and the ML Engineer role. Computer science, electrical engineering, or applied mathematics degrees are the strongest match for Scale AI's job descriptions.
Use Migrate Mate to Filter Scale AI ML Roles
Not every ML Engineer posting at Scale AI lists sponsorship eligibility upfront. Search and filter confirmed sponsoring roles for your visa type using Migrate Mate so you apply only to positions where your immigration status is already accounted for.
Machine Learning Engineer at Scale AI jobs are hiring across the US. Find yours.
Find Machine Learning Engineer at Scale AI JobsFrequently Asked Questions
Does Scale AI sponsor H-1B visas for Machine Learning Engineers?
Yes, Scale AI sponsors H-1B visas for Machine Learning Engineers. The role qualifies as a specialty occupation given its requirement for a bachelor's degree or higher in computer science, applied mathematics, or a related technical field. Your employer initiates the process by filing a Labor Condition Application with the DOL, followed by an H-1B petition with USCIS, so confirm the internal timeline with your recruiter early in the offer process.
Which visa types does Scale AI commonly use for Machine Learning Engineers?
Scale AI sponsors H-1B, E-3, TN, F-1 OPT, F-1 CPT, J-1, and EB-2 or EB-3 Green Card pathways for Machine Learning Engineers. Australian citizens are eligible for the E-3, which has no lottery and is filed directly with USCIS. Canadian and Mexican nationals may qualify under TN status. The right visa depends on your nationality, degree, and career stage.
What qualifications does Scale AI expect for Machine Learning Engineers?
Scale AI typically looks for ML Engineers with a bachelor's or master's degree in computer science, statistics, or a related field, combined with hands-on experience in model training, evaluation pipelines, or large-scale data infrastructure. For immigration purposes, your degree field needs to align with the job description to satisfy H-1B specialty occupation requirements. Research or industry experience with large language models or reinforcement learning from human feedback strengthens your candidacy.
How do I apply for Machine Learning Engineer jobs at Scale AI?
You can browse current Machine Learning Engineer openings at Scale AI through Migrate Mate, which filters roles by visa sponsorship type so you can identify positions that match your immigration status. When you apply, tailor your resume to reflect production ML systems experience rather than general software engineering. During the offer stage, confirm sponsorship details, your target start date, and whether Scale AI files H-1B petitions in the regular cap or the advanced degree exemption.
How do I plan my timeline when switching to Scale AI on a sponsored visa?
Timeline depends on your current visa status. H-1B transfers can use portability rules, allowing you to start with Scale AI once the transfer petition is filed and received by USCIS, without waiting for approval. F-1 OPT holders must confirm their authorization end date and whether cap-gap applies. E-3 and TN renewals are faster but still require a certified LCA before the visa is issued. Build at least six to eight weeks of lead time into any offer negotiation to avoid gaps in work authorization.
See which Machine Learning Engineer at Scale AI employers are hiring and sponsoring visas right now.
Search Machine Learning Engineer at Scale AI Jobs