Principal Product Manager, Model Training & Fine-Tuning

Seekr is looking for a Principal Product Manager to lead the model training and fine-tuning capabilities within SeekrFlow. This role will own the product strategy, roadmap, and execution for how customers customize, improve, evaluate, and operationalize models using SeekrFlow. You will work closely with engineering, design, ML, infrastructure, field teams, and customers to design and ship product capabilities that make model training and fine-tuning accessible, reliable, and production-ready.

The ideal candidate has experience building technical AI/ML products, understands the tradeoffs involved in model customization, and can translate complex ML workflows into intuitive platform capabilities for enterprise users.

What You’ll Own

As the Principal PM for model training and fine-tuning, you will define and drive the product direction for capabilities such as:

• Supervised fine-tuning and instruction fine-tuning workflows
• Preference optimization techniques such as DPO and RL-based approaches
• LoRA / adapter-based fine-tuning
• Dataset preparation, validation, and versioning
• Training job configuration, orchestration, monitoring, and troubleshooting
• Model lineage, experiment tracking, and reproducibility
• Evaluation workflows to determine whether a fine-tuned model is actually better
• Promotion workflows from training to evaluation to deployment
• Cost, performance, and quality tradeoffs across training workflows
• Enterprise controls for governance, permissions, auditability, and deployment readiness

You will help customers answer questions like:

• When should I fine-tune versus use prompting, RAG, or an off-the-shelf model?
• How do I know whether my fine-tuned model is actually better?
• How do I prepare and validate the right training data?
• How do I move from experimentation to production safely?
• How do I govern, evaluate, and monitor customized models over time?

Responsibilities

• Define the product vision, strategy, and roadmap for model training and fine-tuning within SeekrFlow.
• Partner closely with engineering and design to design, build, launch, and iterate on product capabilities across the model training and fine-tuning lifecycle.
• Translate complex ML workflows into clear, usable, and scalable product experiences for technical and enterprise users.
• Work with ML engineering, infrastructure, design, field teams, and customers to identify the highest- impact problems to solve.
• Develop a deep understanding of customer use cases across enterprise, regulated, and deployment- constrained environments.
• Prioritize across competing needs such as model quality, ease of use, flexibility, cost, latency, governance, and operational complexity.
• Define requirements for training workflows, dataset management, job orchestration, evaluation, monitoring, model lineage, and model promotion.
• Drive product discovery, solution definition, feature design, launch planning, and post-launch iteration in partnership with cross-functional teams.
• Establish success metrics for model customization workflows, including adoption, time-to-value, training success rates, model quality improvements, deployment conversion, and customer retention.
• Work with go-to-market and customer-facing teams to support strategic customer conversations and translate field learnings into product direction.
• Communicate roadmap, tradeoffs, and product decisions clearly to executives, engineering teams, design partners, and customer stakeholders.
• Help shape SeekrFlow’s broader platform strategy across data, fine-tuning, evaluation, inference, agents, observability, and explainability.

What We’re Looking For

Must-Haves

• 8+ years of product management experience, including experience owning technical platform products.
• Strong understanding of AI/ML product development workflows, especially model training, fine- tuning, evaluation, and deployment.
• Ability to reason through tradeoffs across model quality, data quality, cost, performance, usability, and production readiness.
• Experience working closely with ML engineers, platform engineers, infrastructure teams, design teams, and/or developer-facing product teams.
• Strong product judgment in ambiguous problem spaces.
• Demonstrated ability to define product strategy, prioritize roadmaps, and drive execution across cross-functional teams.
• Experience designing and shipping complex product capabilities from discovery through launch and iteration.
• Strong written and verbal communication skills, with the ability to explain complex technical concepts clearly to technical and non-technical audiences.
• Customer-centric mindset and ability to translate enterprise customer needs into scalable platform capabilities.

Nice-to-Haves

• Experience building AI development platforms, MLOps platforms, LLMOps platforms, developer tools, or technical enterprise platforms.
• Hands-on familiarity with LLM fine-tuning techniques such as SFT/IFT, LoRA, DPO, RLHF, or related approaches.
• Experience with dataset management, evaluation workflows, experiment tracking, model registries, or deployment pipelines.
• Experience building products for regulated, security-sensitive, self-hosted, or air-gapped environments.
• Familiarity with open-source model ecosystems and enterprise model deployment patterns.
• Experience working with GPU-based infrastructure, training job orchestration, or distributed ML systems.
• Prior experience as a Principal PM, Staff PM, Group PM, or product lead for a technical platform area.