Technical Program Manager – AI/ML

DynPro Inc.; Santa Clara (CA); On-site; USD 140,000 - 210,000; Full time

Job summary

DynPro Inc. seeks an experienced Technical Program Manager to lead cross-functional AI and ML initiatives, translating research into production capabilities.

You will partner with research, engineering, product, and infrastructure teams to drive ML lifecycle tasks, MLOps, model evaluation, and deployment while aligning roadmaps and milestones.

Qualifications

• 8+ years of technical program management experience.
• Experience leading complex cross-functional software, AI, or ML programs.
• Working knowledge of ML lifecycle: data prep, training, evaluation, deployment, monitoring.
• Ability to translate technical objectives into measurable roadmaps and milestones.
• Strong communication skills with senior and executive stakeholders.
• Experience managing dependencies across research, engineering, infrastructure, and product teams.
• Experience with MLOps and model deployment.

Responsibilities

• Lead cross-functional AI initiatives across research, engineering, infrastructure, and product teams.
• Drive execution of AI and ML capabilities from research to production.
• Coordinate ML lifecycle programs including data prep, training, evaluation, deployment, and monitoring.
• Collaborate on sprint planning and roadmap definition with engineering and product teams.
• Translate research concepts into actionable execution plans and decisions.
• Provide executive-level updates on progress, risks, and resource needs.

Skills

Cross-functional leadership

Communication

Program management

Stakeholder management

Strategic planning

Education

Bachelor's degree in Computer Science, Engineering, Data Science

Tools

OpenAI

Anthropic

Hugging Face

Job description

We are looking for an experienced Technical Program Manager to lead complex, cross-functional AI and machine learning initiatives. In this role, you will work closely with research, engineering, product, and infrastructure teams to help bring advanced AI capabilities from research and development into production.

The ideal candidate combines strong technical program management experience with a solid understanding of the machine learning lifecycle, MLOps, model development, evaluation, and production deployment.

What You'll Be Doing

• Lead cross-functional AI initiatives across research, engineering, infrastructure, and product teams.
• Drive the execution and delivery of agentic AI, machine learning, and related AI capabilities.
• Coordinate programs across the full ML lifecycle, including data preparation, training, evaluation, deployment, and production monitoring.
• Drive MLOps capabilities supporting reproducible training, experiment tracking, model versioning, evaluation, release automation, and monitoring.
• Manage model-development dependencies involving datasets, compute capacity, training infrastructure, inference platforms, and evaluation tooling.
• Coordinate model evaluations across accuracy, quality, latency, robustness, reliability, safety, and production-readiness criteria.
• Support speech and conversational AI initiatives, including areas such as automatic speech recognition, text-to-speech, speech translation, audio understanding, and conversational AI.
• Partner with engineering teams on sprint planning, stand-ups, sprint reviews, and retrospectives.
• Work with engineering and product teams to shape roadmaps, establish plans of record, define milestones, and drive execution.
• Translate research concepts and technical objectives into actionable execution plans and production-ready capabilities.
• Lead readiness reviews and ensure technical issues result in remediation, retesting, or clearly documented decisions.
• Manage dependencies, risks, milestones, and resource requirements across multiple AI programs.
• Identify opportunities to improve model-development velocity, operational efficiency, reproducibility, and release quality.
• Establish program management and development best practices that improve team productivity.
• Provide concise executive-level program updates covering progress, risks, tradeoffs, resource requirements, and key decisions.

What We're Looking For

• Bachelor's degree or equivalent practical experience in Computer Science, Engineering, Data Science, or a related technical field.
• 8+ years of experience in technical program management, engineering, product development, technical operations, or a related field.
• Demonstrated experience leading complex, cross-functional software, infrastructure, AI, or machine learning programs.
• Working knowledge of the machine learning lifecycle, including data preparation, training, evaluation, deployment, and monitoring.
• Experience managing technical dependencies across research, engineering, infrastructure, and product organizations.
• Strong ability to translate technical objectives into measurable roadmaps, milestones, and execution plans.
• Excellent communication skills with the ability to work effectively with highly technical teams as well as senior and executive stakeholders.
• Strong organizational and program management skills.
• Demonstrated success working in ambiguous, fast-moving, and rapidly evolving technical environments.

Preferred Qualifications

• Strong understanding of Large Language Models, including model architectures, development frameworks, and evaluation methodologies.
• Familiarity with AI ecosystems and platforms such as OpenAI, Anthropic, Hugging Face, or similar technologies.
• Experience managing speech, audio, conversational AI, or multimodal model development from research through production deployment.
• Understanding of how data quality, model architecture, training infrastructure, evaluation methodology, and inference systems impact model performance.
• Experience building or scaling MLOps processes that improved development speed, reproducibility, operational efficiency, or release quality.
• Experience managing multilingual datasets, model variants, and evaluation coverage across different products or markets.
• Experience coordinating large-scale model training programs with significant compute and infrastructure dependencies.
• Ability to convert research uncertainty into an actionable roadmap without introducing unnecessary process.
• Experience aligning research, engineering, product, and infrastructure teams around major model or AI product releases.
• Strong ability to communicate complex technical risks, dependencies, and tradeoffs to leadership.