What are the responsibilities and job description for the MLOps Engineer position at Otter.ai?
The Opportunity
Are you an experienced MLOps Engineer passionate about building robust, scalable, and efficient machine learning infrastructure? Do you thrive in a fast-paced AI research environment, empowering the team by building and maintaining infrastructure for both cutting-edge model training and production deployment? If so, this role is for you!
As an MLOps Engineer on our AI Research team, you will build and manage the infrastructure for both training and deploying AI models, ensuring scalability, reliability, and performance. You will work closely with researchers, ML engineers, and software developers to ensure seamless integration and optimization of machine learning workflows. Your contributions will empower our AI models to perform efficiently in real-world applications at scale.
Your Impact
We are in the business of shaping the future of work. Our mission is to make conversations more valuable.
With over 1B meetings transcribed, Otter.ai is the world’s 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.
Otter.ai is an equal opportunity employer. We proudly celebrate diversity and are dedicated to inclusivity.
Salary Range: $155,000 to $185,000 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.
Are you an experienced MLOps Engineer passionate about building robust, scalable, and efficient machine learning infrastructure? Do you thrive in a fast-paced AI research environment, empowering the team by building and maintaining infrastructure for both cutting-edge model training and production deployment? If so, this role is for you!
As an MLOps Engineer on our AI Research team, you will build and manage the infrastructure for both training and deploying AI models, ensuring scalability, reliability, and performance. You will work closely with researchers, ML engineers, and software developers to ensure seamless integration and optimization of machine learning workflows. Your contributions will empower our AI models to perform efficiently in real-world applications at scale.
Your Impact
- Infrastructure & System Administration: Design, deploy, and maintain scalable infrastructure on Linux, Kubernetes, and AWS to support machine learning workloads.
- CI/CD & GitOps: Develop and manage automated CI/CD pipelines for machine learning models and applications, ensuring seamless deployments and version control.
- Internal Tooling: Build internal web applications to improve ML workflow efficiency, model monitoring, and deployment processes.
- Experiment Tracking & Model Management: Utilize tools such as Weights & Biases, MLflow, and other experiment tracking systems to manage model lifecycle and metadata.
- Production Inference Optimization: Deploy and optimize ML models for inference using ONNX, TorchScript, TensorRT, or other relevant technologies to maximize performance.
- Performance Tuning: Conduct performance profiling and tuning of ML workloads, optimizing memory usage, compute efficiency, and model latency.
- Scalable Data Processing: Design and maintain large-scale data processing pipelines using Spark, Ray, or other distributed computing frameworks to support AI research and production systems.
- Has a Master’s degree 3 years of industry experience or a Ph.D. in computer science, machine learning, or a related field.
- Has hands-on experience with Linux system administration, Kubernetes, Terraform, and AWS.
- Is proficient in GitOps, CI/CD tools (e.g., ArgoCD, Jenkins, GitHub Actions).
- Has experience in writing internal web applications (e.g., using Django, Flask, FastAPI, or React).
- Is familiar with ML experiment tracking tools (e.g., Weights & Biases, MLflow).
- Has expertise in model deployment and inference optimization using ONNX, TorchScript, TensorRT, or similar frameworks.
- Has strong programming skills in Python, with additional experience in Rust or C .
- Understands large-scale distributed computing and has worked with Spark, Ray, or other big data processing frameworks.
- Has experience with performance tuning of ML models and infrastructure.
- Is comfortable collaborating with research and engineering teams to translate cutting-edge AI into scalable, production-ready solutions.
We are in the business of shaping the future of work. Our mission is to make conversations more valuable.
With over 1B meetings transcribed, Otter.ai is the world’s 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.
Otter.ai is an equal opportunity employer. We proudly celebrate diversity and are dedicated to inclusivity.
- Otter.ai 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 Otter.ai 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: $155,000 to $185,000 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.
Salary : $155,000 - $185,000
