MLOps Engineer, Otter.ai
$155-185k
Office located in Mountain View, CA
AI notetaker, transcription & insights
Job no longer available
AI notetaker, transcription & insights
201-500 employees
Job no longer available
$155-185k
Office located in Mountain View, CA
201-500 employees
Company mission
To create smarter tools that free people up to do their best work.
Role
Who you are
- 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!
- 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
What the job involves
- 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
- 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
Our take
Otter specifically thought of business professionals who are running from one meeting to the next, while juggling multiple projects with various teams. The company's technology integrates with platforms like Zoom, providing a way to record meetings and convert speech to text in real-time. It then streamlines communications for smarter, more collaborative, and more productive meetings—the first use of conversational AI to turn meetings into an actionable workflow.
This allows people to benefit from captioning, as well as teams to have a written record of the conversation, which can be edited and shared. Its AI can also highlight and comment on important sections of a meeting transcript, and automatically tag action items. In addition to businesses, students and universities – including UCLA, Tulane, and Western Kentucky – use the tool to increase accessibility.
The platform is used in more than 230 countries and has transcribed over 100 million meetings. Otter is far from alone in the audio transcription market, estimated to be worth nearly $41.89 billion by 2030, with Google’s free transcription tool posing a potential threat, as well as competitors such as Rev.
Kirsty
Company Specialist at Welcome to the Jungle
Insights
27% employee growth in 12 months
Company
Funding (last 2 of 5 rounds)
Feb 2021
$10m
CONVERTIBLE
Feb 2021
$40m
SERIES B
Total funding: $73m
Company benefits
- Compensation: With highly competitive salaries and equity option packages, everyone's an owner here.
- Hybrid Working: You can mix and match working from our Mountain View or San Francisco office, plus a couple days from home.
- Social Gatherings: Quarterly gatherings in the Bay Area for some team real fun and authentic connection.
- Health and Wellness: Comprehensive medical, dental, vision, life insurance, disability, and wellness benefits that start the first day you do.
- PTO: Start with a minimum of 15 days of paid time off, plus up to 18 weeks for maternity leave and 8 weeks for paternity leave.
- 401(k): Plans to help meet your retirement goals because we care about your future.
Company HQ
Old Mountain View, Mountain View, CA
Leadership
Sam Liang
(Co-Founder & CEO)Earned a PhD from Stanford and then joined Cisco as a Senior Software Engineer. Worked as a Tech Lead at Google and is a member of the Forbes Technology Council.
Yun Fu
(Co-Founder & CTO)Has a PhD in Computer Science from Duke. Worked as Yahoo's Principal Software Engineer and was Head of Infrastructure at Particle Media.
Salary benchmarks
We don't have enough data yet to provide salary benchmarks for this role.
Submit your salary to help other candidates with crowdsourced salary estimates.
Share this job
View 20 more jobs at Otter.ai