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Multimodal Learning Jobs in New York (NOW HIRING)

KARGO

Senior Machine Learning Engineer

New York, NY

$150K - $175K/yr

Explain multimodal modeling tradeoffs to Product and Creative stakeholders in terms of business impact, partnering with Data Science and Platform Engineering as co-owners, not handoff points

Kargo

Senior Machine Learning Engineer

New York, NY · On-site

$150K - $175K/yr

Explain multimodal modeling tradeoffs to Product and Creative stakeholders in terms of business impact, partnering with Data Science and Platform Engineering as co-owners, not handoff points

Jobtailor

Applied AI Scientist, Senior/Staff

Manhattan, NY · On-site

$150 - $230/hr

Expertise in one or more of natural language processing, generative AI, computer vision, multimodal learning, reinforcement learning, recommendation systems, or speech * Proficiency in Python and ...

Meta

AI Research Scientist, Physical AI

New York, NY · On-site

$184K - $257K/yr

... learning, multimodal learning, robotics policy development, computer vision (3D), egocentric perception, embodied AI and/or LLMs, control theory, optimization algorithms • Experience in C/C++ and ...

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Multimodal Learning information

What is multimodal learning?

Multimodal learning is an area of machine learning that involves integrating and processing information from multiple types of data, such as text, images, audio, and video. The goal is to create models that can understand and make predictions based on more than one data modality, similar to how humans use various senses. This approach is used in applications like speech recognition with visual cues, image captioning, and video analysis. By combining different data types, multimodal learning systems can achieve better accuracy and more robust understanding.

What are the key skills and qualifications needed to thrive in multimodal learning, and why are they important?

To excel as a Multimodal Learning Specialist, you need a solid background in machine learning, data science, and computer vision, often supported by an advanced degree in a related field. Familiarity with deep learning frameworks like TensorFlow or PyTorch, experience integrating data from diverse sources (e.g., text, audio, images), and knowledge of relevant algorithms are crucial. Strong problem-solving abilities, creativity, and effective collaboration are standout soft skills for this role. These competencies are vital for developing innovative models that can process and interpret complex, multi-source data to drive impactful AI solutions.

What are some common challenges faced by professionals working in multimodal learning roles, and how can they be addressed?

Professionals in multimodal learning frequently encounter challenges related to integrating and aligning data from multiple sources, such as text, images, audio, or video. Ensuring data quality and consistency across modalities can be complex, and developing models that effectively combine heterogeneous information often requires advanced technical skills and innovative thinking. Collaboration with domain experts and other data scientists is key to overcoming these obstacles, as is staying up to date with the latest research and tools in machine learning. Regular team meetings and cross-disciplinary workshops can help foster a collaborative environment and promote knowledge sharing.

What is the difference between Multimodal Learning vs Data Scientist?

AspectMultimodal LearningData Scientist
Required CredentialsAdvanced degrees in AI, Machine Learning, or Computer ScienceBachelor's or Master's in Data Science, Statistics, or related fields
Work EnvironmentResearch labs, AI development teams, academiaBusiness, tech companies, analytics teams
Industry UsageAI research, multimedia applications, roboticsData analysis, predictive modeling, business insights

Multimodal Learning focuses on developing AI models that process and integrate multiple data types like images, text, and audio. Data Scientists analyze data to extract insights, build models, and support decision-making. While both roles involve data and algorithms, Multimodal Learning is specialized in AI model development for complex data integration, whereas Data Scientists work broadly across data analysis and interpretation.

What are popular job titles related to Multimodal Learning jobs in New York?

For Multimodal Learning jobs in New York, the most frequently searched job titles are:

What job categories do people searching Multimodal Learning jobs in New York look for?

The top searched job categories for Multimodal Learning jobs in New York are:

What cities in New York are hiring for Multimodal Learning jobs?

Cities in New York with the most Multimodal Learning job openings:

Multimodal Learning jobs near you
Infographic showing various Multimodal Learning job openings in New York as of August 2026, with employment types broken down into 42% Full Time, 29% Part Time, and 29% Contract. Highlights an 100% In-person job distribution.

Senior Machine Learning Engineer

KARGO

New York, NY

$150K - $175K/yr

Full-time

Re-posted 19 days ago

Job description

Who We Are

Kargo creates powerful moments of connection between brands and consumers to build businesses. Every day, our 600+ employees work to radically raise the bar on what agentic AI, CTV, eCommerce, social, and mobile can do to deliver unique ad experiences across the world's most premium platforms. Taking a creative science approach to all we do, we continuously innovate solutions that outperform industry benchmarks and client expectations. Now 20+ years strong, Kargo has offices in NYC, Chicago, LA,  Dallas, Sydney, Auckland, London and Waterford, Ireland. 

Who We Hire

Techies who want to build the future. Creatives who want to design it better. Communicators to win business. Collaborators to build it. Data pros who turn numbers into insights. Product builders who turn ideas into innovations. Anyone eager to be on a team that doesn't stop to ask what's next, because they're already building it. 

The Opportunity

Kargo is hiring a senior machine learning engineer to own the evolution of Finetouch, our creative scoring system—leading the design and production deployment of multimodal ML models that quantify creative quality and predict ad performance. This role is the technical anchor for the Creative Sciences Platform, translating research in LLMs, VLMs, and multimodal learning into scalable, reliable systems that creative and product teams build on. Success means Finetouch becomes faster, smarter, and more trusted as the intelligence layer behind Kargo's creative analytics.

The Daily To-Do

  • Ship the next generation of Finetouch—delivering better predictive accuracy on creative performance, expanded multimodal signal coverage (visual + text + engagement), and validated lift over the current baseline
  • Stand up production-grade MLOps pipelines—training, fine-tuning, deployment, monitoring—on MLflow/Kubeflow/Ray Train so model iterations move from notebook to production in days, not weeks
  • Scale distributed training and inference on multimodal/VLM workloads through Ray, PyTorch Distributed, and right-sized cloud infrastructure—enabling larger models and faster experimentation cycles
  • Build and operate the APIs, embedding services, and model endpoints that let Glossi and other Kargo creative platforms consume scoring in real time, with documented SLAs and integration patterns
  • Deploy real-time monitoring, drift detection, and alerting so production model degradation is caught before it affects creative decisions
  • Explain multimodal modeling tradeoffs to Product and Creative stakeholders in terms of business impact, partnering with Data Science and Platform Engineering as co-owners, not handoff points
  • Document architecture, decisions, and runbooks so the platform outlives any single contributor

Qualifications

  • 5+ years in ML engineering or MLOps, with shipped production systems involving LLMs, VLMs, or multimodal architectures
  • Expert in Python and PyTorch (or TensorFlow), plus distributed training frameworks (Ray, PyTorch Lightning, Horovod)
  • Hands-on with MLOps tooling: MLflow, Weights & Biases, Kubeflow, Argo, or Airflow for orchestration, experiment tracking, and automated retraining
  • Cloud-native ML deployment on AWS (SageMaker), GCP (Vertex AI), or Azure ML, with infrastructure-as-code (Terraform, Helm)
  • Production fluency with Docker, Kubernetes, and CI/CD patterns for ML
  • Strong SQL, data pipeline, and feature store design for scalable experimentation
  • Preferred: experience with vector databases, embedding pipelines, and real-time retrieval systems, plus a background in creative scoring, aesthetic modeling, or ad performance prediction

In accordance with applicable federal, state, and local pay transparency laws, the anticipated base salary range for this position is listed below. In addition to base salary, this role is eligible for variable compensation — either the Kargo Sales Incentive Plan (sales roles) or an annual discretionary bonus (all other roles). Actual compensation may vary based on factors such as geographic location, work experience, education, and skills.

U.S Salary Range
$150,000—$175,000 USD

What We're Proud Of

  • AdAge Best Places to Work 
  • ThinkLA Partner of the Year
  • Built In Best Places to Work 
  • Cynopsis 2025 Top Women in Media - Jeannine Shao Collins
  • Martech Breakthrough Awards - Best Overall Adtech Company
  • Digiday Media Awards Best Event
  • Cynopsis Media Impact Awards-Best CTV Platform
  • Martech Breakthrough Awards-CTV Innovation
  • Adweek Media Plan of the Year Awards - Best Use of Insights

Following Our Lead

  • Big Picture:  kargo.com
  • The Latest:  Instagram (@kargo.hq) and LinkedIn (Kargo)

Kargo is an Equal Opportunity Employer. We are committed to building an inclusive and diverse workplace where all employees and applicants are treated with respect and dignity. We do not discriminate on the basis of race, color, ethnic origin, religion or belief, sex, sexual orientation, gender identity or expression, age, disability, marital or family status, national origin, veteran status, or any other characteristic protected by applicable local, state, or federal law. All qualified applicants will receive consideration for employment.

Pursuant to applicable fair chance laws, including the San Francisco Fair Chance Ordinance and the Los Angeles Fair Chance Initiative for Hiring, Kargo will consider qualified applicants with arrest and conviction records for employment.

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