Staff Machine Learning Engineer

Mountain View, CA - USA

Monthly Salary: $ 210000 - 275000

Posted on: 18 hours ago

Vacancies: 1 Vacancy

Job Summary

The Opportunity
Do you want to lead projects to build and deploy cutting-edge AI technology to help people get unparalleled value from meetings and conversations Join our core AI team responsible for ML and work alongside industry-veteran scientists and engineers. As a Staff Machine Learning Engineer youll bring your strong software engineering mindset to machine learning in order to scale and optimize our ML systemscreating and transforming innovative research into production-ready features that power Otters summarization and conversational intelligence products.

Your Impact

Architect build and evolve large-scale SID / ASR / NLP / LLM systems that power mission-critical product experiences including summarization chat and speech understanding across millions of conversations.
Lead the design and implementation of training fine-tuning post-training and inference strategies for large language and speech models using PyTorch and/or JAX making principled trade-offs across quality latency cost and reliability.
Design and improve model architectures loss functions decoding strategies and training techniques for speech and language models informed by both research and production constraints.
Own end-to-end ML system lifecycles from research prototyping through production deployment monitoring iteration and long-term maintenance.
Partner deeply with product and infrastructure teams to develop and translate cutting-edge research into scalable production-grade systems that deliver measurable user and business impact.
Drive system-level improvements in model performance robustness observability and operational excellence using real-world conversational data at scale.
Set technical direction and best practices for ML infrastructure data pipelines evaluation frameworks and deployment workflows in a cloud environment.
Identify and resolve complex ambiguous problems in model behavior data quality scaling and system interactions often before they surface as user-visible issues.
Mentor and elevate other engineers influencing team standards reviewing designs and contributing to a culture of strong technical decision-making and execution.
Influence applied research and technical roadmaps by identifying promising speech and multimodal modeling approaches and driving their validation and adoption into production systems

Were Looking for Someone Who

Holds a Bachelors or Masters degree in Computer Science or a related field with 10 years of relevant industry experience; PhD is preferred.
Has deep hands-on experiencebuilding and fine-tuning large language or foundation models with production experience in ASR TTS multimodal or modern LLM/NLP systems and a strong understanding of model failure modes and trade-offs.
Demonstrates strong command of modern ML research with the ability to critically evaluate new papers and drive innovation by identifying what is production-worthy versus experimental.
Has extensive experience deploying scaling monitoring and operating ML systems in production across training inference and serving infrastructure including model versioning rollback strategies and performance regression detection while balancing cost latency and reliability constraints.
Is comfortable working with large-scale speech and conversational datasets including data preprocessing augmentation quality analysis and labeling strategies to support model training and evaluation.
Is highly effective at cross-functional collaboration working end-to-end with product infra research and data teams to deliver outcomes not just models.
Can lead technical projects independently driving clarity in ambiguous problem spaces and making sound architectural decisions.
Has experience with or strong interest in agentic systems tool-use frameworks or multi-model orchestration.
Has demonstrated ability to influence beyond their immediate team shaping technical direction standards or long-term strategy.
Experience with personalization recommendation systems or user modeling is a plus

About

We are in the business of shaping the future of work. Our mission is to make conversations more valuable.

With over 1B meetings transcribed is the worlds leading tool for meeting transcription summarization and collaboration. Using artificial intelligence Otter generates real-time automated meeting notes summaries and other insights from in-person and virtual meetings - turning meetings into accessible collaborative and actionable data that can be shared across teams and organizations. The company is backed by early investors in Google DeepMind Zoom and Tesla.

is an equal opportunity employer. We proudly celebrate diversity and are dedicated to inclusivity.

* does not accept unsolicited resumes from 3rd party recruitment agencies without a written agreement in place for permanent placements. Any resume or other candidate information submitted outside of established candidate submission guidelines (including through our website or via email to any employee) and without a written agreement otherwise will be deemed to be our sole property and no fee will be paid should we hire the candidate.

Salary range

Salary Range: $210000 to $275000 USD per year.

This salary range represents the low and high end of the estimated salary range for this position. The actual base salary offered for the role is dependent based on several factors. Our base salary is just one component of our comprehensive total rewards package.
#LI-Hybrid

Required Experience:

Staff IC

The OpportunityDo you want to lead projects to build and deploy cutting-edge AI technology to help people get unparalleled value from meetings and conversations Join our core AI team responsible for ML and work alongside industry-veteran scientists and engineers. As a Staff Machine Learning Engineer...

Your Impact

Architect build and evolve large-scale SID / ASR / NLP / LLM systems that power mission-critical product experiences including summarization chat and speech understanding across millions of conversations.
Lead the design and implementation of training fine-tuning post-training and inference strategies for large language and speech models using PyTorch and/or JAX making principled trade-offs across quality latency cost and reliability.
Design and improve model architectures loss functions decoding strategies and training techniques for speech and language models informed by both research and production constraints.
Own end-to-end ML system lifecycles from research prototyping through production deployment monitoring iteration and long-term maintenance.
Partner deeply with product and infrastructure teams to develop and translate cutting-edge research into scalable production-grade systems that deliver measurable user and business impact.
Drive system-level improvements in model performance robustness observability and operational excellence using real-world conversational data at scale.
Set technical direction and best practices for ML infrastructure data pipelines evaluation frameworks and deployment workflows in a cloud environment.
Identify and resolve complex ambiguous problems in model behavior data quality scaling and system interactions often before they surface as user-visible issues.
Mentor and elevate other engineers influencing team standards reviewing designs and contributing to a culture of strong technical decision-making and execution.
Influence applied research and technical roadmaps by identifying promising speech and multimodal modeling approaches and driving their validation and adoption into production systems

Were Looking for Someone Who

Holds a Bachelors or Masters degree in Computer Science or a related field with 10 years of relevant industry experience; PhD is preferred.
Has deep hands-on experiencebuilding and fine-tuning large language or foundation models with production experience in ASR TTS multimodal or modern LLM/NLP systems and a strong understanding of model failure modes and trade-offs.
Demonstrates strong command of modern ML research with the ability to critically evaluate new papers and drive innovation by identifying what is production-worthy versus experimental.
Has extensive experience deploying scaling monitoring and operating ML systems in production across training inference and serving infrastructure including model versioning rollback strategies and performance regression detection while balancing cost latency and reliability constraints.
Is comfortable working with large-scale speech and conversational datasets including data preprocessing augmentation quality analysis and labeling strategies to support model training and evaluation.
Is highly effective at cross-functional collaboration working end-to-end with product infra research and data teams to deliver outcomes not just models.
Can lead technical projects independently driving clarity in ambiguous problem spaces and making sound architectural decisions.
Has experience with or strong interest in agentic systems tool-use frameworks or multi-model orchestration.
Has demonstrated ability to influence beyond their immediate team shaping technical direction standards or long-term strategy.
Experience with personalization recommendation systems or user modeling is a plus