Software Engineer – Platform
Speechify
full-time
Posted on:
Location Type: Remote
Location: Remote • Illinois, Kansas • 🇺🇸 United States
Visit company websiteJob Level
Mid-LevelSenior
Tech Stack
CloudDockerGoogle Cloud PlatformKubernetesPython
About the role
- Work alongside machine learning researchers, engineers, and product managers to bring our AI Voices to their customers for a diverse range of use cases
- Deploy and operate the core ML inference workloads for our AI Voices serving pipeline
- Introduce new techniques, tools, and architecture that improve the performance, latency, throughput, and efficiency of our deployed models
- Build tools to give us visibility into our bottlenecks and sources of instability and then design and implement solutions to address the highest priority issues
Requirements
- Experience shipping Python-based services
- Experience being responsible for the successful operation of a critical production service
- Experience with public cloud environments, GCP preferred
- Experience with Infrastructure such as Code, Docker, and containerized deployments.
- Preferred: Experience deploying high-availability applications on Kubernetes.
- Preferred: Experience deploying ML models to production
Benefits
- A dynamic environment where your contributions shape the company and its products
- A team that values innovation, intuition, and drive
- Autonomy, fostering focus and creativity
- The opportunity to have a significant impact in a revolutionary industry
- Competitive compensation, a welcoming atmosphere, and a commitment to an exceptional asynchronous work culture
- The privilege of working on a product that changes lives, particularly for those with learning differences like dyslexia, ADD, and more
- An active role at the intersection of artificial intelligence and audio – a rapidly evolving tech domain
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard skills
Pythonmachine learningML inferencehigh-availability applicationsKubernetesInfrastructure as CodeDockercontainerized deploymentsdeploying ML modelsperformance optimization