Senior Machine Learning Engineer at Kargo

New York, New York, United States -

Full Time

Start Date

Immediate

Expiry Date

19 Sep, 26

Salary

175000.0

Posted On

21 Jun, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning Engineering, MLOps, Python, PyTorch, Distributed Training, LLMs, VLMs, Multimodal Learning, MLflow, Kubeflow, AWS SageMaker, Docker, Kubernetes, CI/CD, SQL, Ray

Industry

Logistics;Transportation;Supply Chain and Storage

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 In accordance with applicable federal, state, and local pay transparency laws, the anticipated base salary range for this position is listed below. 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 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.

Responsibilities

Lead the design and production deployment of multimodal ML models for Kargo's creative scoring system, Finetouch. Establish production-grade MLOps pipelines and scale distributed training and inference to improve predictive accuracy and system reliability.