Senior Machine Learning Engineer

Kargo; New York (NY); On-site; USD 120,000 - 160,000; Full time

Benefits offered by this job

Health benefits

Remote work options

Flexible schedule

Job summary

Kargo is searching for a Machine Learning Engineer to enhance Finetouch, their creative scoring system. You will lead the development of multimodal ML models to predict ad performance and improve creative quality. Your efforts will focus on refining production systems and optimizing deployment pipelines.

Ideal candidates have over 5 years in ML engineering, proficiency in Python and MLOps tools, and strong experience in cloud-native deployment. Join Kargo to innovate and make an impact in ad technology.

Qualifications

• Expert in Python and PyTorch, with experience in distributed training frameworks.
• Hands-on with MLOps tooling and infrastructure-as-code.
• Production fluency with Docker, Kubernetes, and CI/CD patterns.

Responsibilities

• Lead design and deployment of multimodal ML models for creative score.
• Establish end-to-end pipelines for effective ML operations.
• Build APIs and services exposing the scoring model.

Skills

ML engineering

MLOps

Python

PyTorch

AWS

SQL

Education

5+ years in ML engineering or MLOps

Tools

MLflow

Kubeflow

Docker

Kubernetes

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.

Mission

Own the evolution of Finetouch, Kargo's creative scoring system, by 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.

Outcomes - What Success Looks like in 6-12 months

• Ship the next generation of Finetouch. Deliver a measurably improved version of the creative scoring model — 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 for Creative Sciences. Establish end-to-end pipelines (training, fine-tuning, deployment, monitoring) on MLflow/Kubeflow/Ray Train so model iterations move from notebook to production in days, not weeks, with full reproducibility.
• Scale distributed training and inference. Reduce training time and inference cost on multimodal/VLM workloads through Ray, PyTorch Distributed, and right-sized cloud infrastructure — enabling larger models and faster experimentation cycles.
• Expose Finetouch as a platform. 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.
• Operationalize model reliability. Deploy real-time monitoring, drift detection, and alerting so production model degradation is caught before it affects creative decisions, with clear runbooks and on-call ownership.

Skills - Core Technical Capabilities

Required:

• 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
• Background in creative scoring, aesthetic modeling, or ad performance prediction

Competencies - Behaviors We Like to See

Research‑to‑Production Judgment

• Knows when a model is good enough to ship vs. when it needs another iteration — doesn't over‑engineer or under‑validate
• Translates papers and prototypes into systems that survive production traffic, monitoring, and on‑call

Systems Thinking at Scale

• Designs for the second and third version of the model, not just the first — pipelines, abstractions, and infra that compound over time
• Optimizes the full stack: training cost, inference latency, and developer iteration speed, not just model accuracy

Cross‑Functional Translation

• Explains multimodal modeling tradeoffs to Product and Creative stakeholders in terms of business impact, not architecture diagrams
• Partners with Data Science and Platform Engineering as co‑owners, not handoff points

Operational Ownership

• Treats drift, latency regressions, and silent failures as personal — instruments systems so problems are caught early and root‑caused fast
• Documents architecture, decisions, and runbooks so the platform outlives any single contributor

Our Laurels

• 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

Follow Our Lead

• Big Picture: kargo.com
• The Latest: Instagram (@kargomobile) 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.