Role: AI Technical Program Manager
Location: Santa Clara, CA – Need only Locals – Onsite – No Relocations
Duration: 12+ Months Contract
Semiconductor industry experience is highly preferred and would be a strong advantage for this role.
Client''''s Simulation division creates industry leading technology for high-end virtual experiences. As a Technical Program Manager, you will help manage, test and improve Client''''s simulation products. You will define best practices for defining and delivery products, project planning and development methodologies and represent the voice of the customer in the product planning process. You will have a strong passion for planning, development and a broad knowledge of realtime technology, and good sensibility for building products.
Lead cross-functional AI initiatives, coordinating between research, engineering, and product teams to deliver agentic AI features and systems.
Drive MLOps capabilities supporting reproducible training, experiment tracking, model versioning, evaluation, release automation, and production monitoring.
Manage model-development dependencies involving datasets, compute capacity, training infrastructure, inference platforms, and evaluation tooling.
Coordinate evaluation across accuracy, quality, latency, robustness, reliability, safety, and production-readiness dimensions.
Support speech-model development across areas such as automatic speech recognition, text-to-speech, speech translation, audio understanding, and conversational AI.
Drive readiness reviews and ensure that technical issues lead to remediation, retesting, or documented decisions.
Collaborate with engineering on sprint planning, stand ups and sprint review and retrospective
Work with engineering and product on shaping the roadmap and providing PORs to help with execution
Ensure technical alignment and execution, translating research concepts into production-ready capabilities.
Establish best practices to help improve productivity
Manage dependencies and risks across multiple AI projects, ensuring milestones are met and resources are effectively allocated.
Identify opportunities to improve model-development velocity, operational efficiency, and release quality.
Provide concise program updates covering progress, risks, tradeoffs, resource needs, and decisions.
Bachelor’s degree or equivalent practical experience in computer science, engineering, data science, or a related field.
8+ years of experience in technical program management, engineering, product development, or technical operations.
Experience leading complex, cross-functional software, infrastructure, AI, or machine-learning programs.
Working knowledge of the ML lifecycle, including data preparation, training, evaluation, deployment, and monitoring.
Experience managing technical dependencies across research, engineering, infrastructure, and product teams.
Ability to communicate effectively with both highly technical teams and executive stakeholders.
Strong organizational skills and demonstrated success operating in ambiguous, rapidly changing environments.
Experience translating technical objectives into measurable roadmaps, milestones, and execution plans.
Ways to stand out from the crowd:
Strong understanding of LLM (Large Language Model) architecture, frameworks (e.g., OpenAI, Anthropic, Hugging Face), and model evaluation.
Managed a speech or audio model from initial research through production deployment.
Understand how data quality, model architecture, training infrastructure, evaluation, and inference affect model performance.
Built or scaled an MLOps process that improved model-development speed, reproducibility, or release quality.
Describe how you managed multilingual data, evaluation coverage, or model variants across different products.
You have coordinated large-scale training programs involving significant compute and infrastructure dependencies.
You can translate research uncertainty into an actionable roadmap without creating unnecessary process.
You have successfully aligned research, engineering, product, and infrastructure teams around a shared model release.
You communicate technical risks and tradeoffs clearly to leadership.
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