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Closed listing

FIS Global

Engineer Sr Lead, Artificial Intelligence / Machine Learning

New York, NY, United States

The pay range for this full-time position is $173,680.00 - $295,250.00 and reflects the minimum and maximum target for new hire salaries for this position based on the posted role, level, and location.

This opening has closed.

The original listing is kept below for reference.

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Job details

Pay: The pay range for this full-time position is $173,680.00 - $295,250.00 and reflects the minimum and maximum target for new hire salaries for this position based on the posted role, level, and location.
Experience: 4+ years of experience building and deploying machine learning models in production.
Posting language: English
Posted: Nov 4, 2026

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Original job description

Archived listing. The details below describe a past opening.

Position Type :

Full time Type Of Hire :

Experienced (relevant combo of work and education)

Job Description

Every day at FIS, innovators, problem solvers and changemakers work together to create technology that advances the world of finance. We embrace new ideas, challenge what's possible and empower our teams to make a lasting impact. If you're ready to help shape the future of fintech, unlock your next chapter with FIS.

About the Role:

FIS's AI Platform organization builds the models, infrastructure, and practices that help teams across FIS bring AI into production safely and at scale. We are looking for a Software Machine Learning Engineer to build domain-specific foundation and embedding models for financial data. These models learn useful representations from large volumes of financial activity, and many products and teams can reuse them across a range of use cases.

You will work across the full model lifecycle: data preparation, pretraining, fine-tuning, evaluation, and packaging models so other teams can use them. You will work with platform, data, and product engineering teams to turn research into reliable, well-governed models in production.

What You Will Do:

Build and improve deep learning models on large-scale financial data, including sequence modeling, pretraining, and fine-tuning of transformer-based models.
Produce reusable, versioned embeddings and model artifacts that downstream teams can easily use.
Design models that generalize across tasks and datasets, and measure how well they perform on downstream problems.
Work with data and infrastructure engineers on training pipelines, data lineage, and reproducible experiments.
Work with serving engineers to optimize models for efficient, low-latency inference.
Build sound evaluation into the workflow, including time-based testing and imbalanced or delayed labels.
Support product and engineering teams as they integrate models, with clear documentation and interfaces.
Balance model quality against compute cost, and report results in terms of business impact.
Work with risk and governance partners so models are explainable, well documented, and meet regulatory expectations.

Required Qualifications:

Bachelor's or Master's degree in Computer Science, Engineering, Mathematics, or a related field, or equivalent practical experience.
4+ years of experience building and deploying machine learning models in production.
Strong programming skills in Python and experience with modern deep learning tools such as PyTorch.
Hands-on experience pretraining, training, or fine-tuning transformer or other deep learning models on GPUs.
Experience working with large datasets and ML pipelines on cloud platforms (AWS, Azure, or GCP), with tools such as Spark, Snowflake, or Databricks.
Solid ML fundamentals, including representation learning, offline evaluation, handling imbalanced data, and choosing the right metrics.
Good engineering habits: version control, testing, reproducible experiments, and code that others can build on.
Ability to explain technical results clearly to non-technical audiences.
Strong collaboration skills and comfort working across engineering, product, and risk teams.

Preferred Qualifications:

Experience with sequence modeling, self-supervised learning, or embedding models for tabular or event data.
Experience with financial services or payments data.
Experience with distributed training and GPU-accelerated ML frameworks.
Experience with inference optimization (quantization, distillation, batching) or real-time ML systems.
Experience with MLflow, model registries, feature stores, or other MLOps tools.
Experience working in a regulated environment, including model risk management and model documentation.

What Success Looks Like:

High-quality, reusable models that perform well across more than one downstream task.
Reproducible, well-documented models and artifacts that pass internal review.
Models adopted by product teams in production, with measurable improvement over existing approaches.
Strong working relationships across platform, data, product, and risk teams.

Why Join Us? You will help build core AI capabilities at one of the world's largest fintech companies, with work that reaches financial institutions and the people they serve. You will work with experienced ML engineers, platform engineers, and data scientists.

What We Offer you:

At FIS, you can grow your career as far as you want to take it. Here’s what else we offer:

Opportunities to make an impact in fintech
Personal and professional learning
Inclusive, diverse work environment
Resources to give back to your community
Competitive salary and benefits

FIS is committed to providing its employees with an exciting career opportunity and competitive compensation. The pay range for this full-time position is $173,680.00 - $295,250.00 and reflects the minimum and maximum target for new hire salaries for this position based on the posted role, level, and location. Within the range, actual individual starting pay is determined by additional factors, including job-related skills, experience, and relevant education or training. Any changes in work location will also impact actual individual starting pay. Please consult with your recruiter about the specific salary range for your preferred location during the hiring process.Privacy Statement

FIS is committed to protecting the privacy and security of all personal information that we process in order to provide services to our clients. For specific information on how FIS protects personal information online, please see the Online Privacy Notice.

EEOC Statement

FIS is an equal opportunity employer. We evaluate qualified applicants without regard to race, color, religion, sex, sexual orientation, gender identity, marital status, genetic information, national origin, disability, veteran status, and other protected characteristics. The EEO is the Law poster is available here supplement document available here

For positions located in the US, the following conditions apply. If you are made a conditional offer of employment, you will be required to undergo a drug test. ADA Disclaimer: In developing this job description care was taken to include all competencies needed to successfully perform in this position. However, for Americans with Disabilities Act (ADA) purposes, the essential functions of the job may or may not have been described for purposes of ADA reasonable accommodation. All reasonable accommodation requests will be reviewed and evaluated on a case-by-case basis.

Sourcing Model

Recruitment at FIS works primarily on a direct sourcing model; a relatively small portion of our hiring is through recruitment agencies. FIS does not accept resumes from recruitment agencies which are not on the preferred supplier list and is not responsible for any related fees for resumes submitted to job postings, our employees, or any other part of our company.

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