Senior Product Manager

Talently; New York (NY); On-site; USD 130,000 - 170,000; Full time

Job summary

Talently is seeking a Senior Product Manager to own experimentation and post-training products, shaping intuitive workflows that accelerate model iteration from research to production.

You will work with ML engineers and researchers to define the roadmap, collaborate with data tools and registries, and translate complex ML workflows into developer-friendly experiences.

Qualifications

• 7+ years of product management experience, including at least 3 years building infrastructure, platform, developer-tooling, or ML products.
• Hands‑on experience building products for ML engineers, AI researchers, or data scientists.
• Strong understanding of experimentation and post-training workflows, including jobs, checkpoints, metrics, artifacts, comparison, reproducibility, and model evaluation.
• Experience with post-training techniques such as supervised fine-tuning, preference optimization, reinforcement learning, distributed training, or hyperparameter optimization.
• Experience designing or working closely with model evaluations and applying qualitative and quantitative signals to product decisions.
• Technical depth across APIs, SDKs, execution systems, distributed workloads, observability, data and artifact management, and failure handling.
• Strong product judgment and the ability to simplify technically complex workflows into intuitive developer experiences.
• Demonstrated end-to-end ownership across discovery, prioritization, implementation, validation, launch, and go-to-market.

Responsibilities

• Own the product vision, strategy, and roadmap for experimentation and post-training capabilities.
• Understand how ML engineers and AI researchers run experiments, compare models, diagnose failures, and transition work into production.
• Determine where to build differentiated experiences and where to integrate with experiment trackers, evaluation frameworks, data tools, and model registries.

Skills

PM experience

ML infrastructure

Experimentation workflows

Post-training techniques

Model evaluation

API/SDK design

End-to-end ownership

Tools

Weights & Biases

MLflow

Hugging Face

Ray

Slurm

Kubernetes

Job description

Join an innovative company in the technology, information and internet industry building the next generation of AI development infrastructure. Help researchers, engineers, startups, and enterprises move efficiently from experimentation and post-training to reliable, production-ready AI systems. As Senior Product Manager, you will own experimentation and post-training products, shaping intuitive workflows that improve model quality, accelerate iteration, and connect research with production.

Responsibilities

• Own the product vision, strategy, and roadmap for experimentation and post-training capabilities.
• Understand how ML engineers and AI researchers run experiments, compare models, diagnose failures, and transition work into production.
• Determine where to build differentiated experiences and where to integrate with experiment trackers, evaluation frameworks, data tools, and model registries.

Must-Have Skills

• 7+ years of product management experience, including at least 3 years building infrastructure, platform, developer-tooling, or machine-learning products.
• Hands‑on experience building products for ML engineers, AI researchers, or data scientists.
• Strong understanding of experimentation and post-training workflows, including jobs, checkpoints, metrics, artifacts, comparison, reproducibility, and model evaluation.
• Experience with post-training techniques such as supervised fine-tuning, preference optimization, reinforcement learning, distributed training, or hyperparameter optimization.
• Experience designing or working closely with model evaluations and applying qualitative and quantitative signals to product decisions.
• Technical depth across APIs, SDKs, execution systems, distributed workloads, observability, data and artifact management, and failure handling.
• Strong product judgment and the ability to simplify technically complex workflows into intuitive developer experiences.
• Demonstrated end-to-end ownership across discovery, prioritization, implementation, validation, launch, and go-to-market.

Nice-to-Have Skills

• Familiarity with distributed training, GPU infrastructure, or large-scale fine-tuning.
• Experience with Weights & Biases, MLflow, Hugging Face, Ray, Slurm, Kubernetes, or comparable ML platforms.