Mission
The mission of Speechify is to make sure that reading is never a barrier to learning.
Over 50 million people use Speechify’s text-to-speech products to turn whatever they’re reading – PDFs, books, Google Docs, news articles, websites – into audio, so they can read faster, read more, and remember more. Speechify’s text-to-speech reading products include its iOS app, Android App, Mac App, Chrome Extension, and Web App. Google recently named Speechify the Chrome Extension of the Year and Apple named Speechify its 2025 Design Award winner for Inclusivity.
Today, nearly 200 people around the globe work on Speechify in a 100% distributed setting – Speechify has no office. These include frontend and backend engineers, AI research scientists, and others from Amazon, Microsoft, and Google, leading PhD programs like Stanford, high growth startups like Stripe, Vercel, Bolt, and many founders of their own companies.
Overview
We are looking for AI Infrastructure engineer to help build and scale the infrastructure that powers our machine learning initiatives. In this role, you will design, develop, and optimize the core platforms and services that enable data scientists and ML engineers to train, deploy, and monitor models efficiently. You’ll partner closely with Data Science, Data Engineering, and Product teams to create a robust, self-service ML ecosystem that accelerates innovation.
What You’ll Do
Build Scale AI Infrastructure: Design, implement, and maintain high-performance ML training and inference platforms. Develop MLOps Tools: Ship tools that allow any ML engineer to deploy a model in minutes, not days. Optimize Performance: Improve scalability, reliability, and cost efficiency of model training and serving systems. Collaborate Across Teams: Partner with researchers to turn experimental voice models into production-ready systems. Ensure Best Practices: Establish standards for model versioning, testing, monitoring, and governance. Drive Automation: Automate data/model pipelines to reduce manual intervention and speed up experimentation.
- Experience: 3+ years in Software Engineering or ML Platform/Infrastructure roles, with a focus on distributed systems, cloud services, or MLOps.
- Technical Expertise: Proficiency in Python (or similar), containerization (Docker, Kubernetes), CI/CD pipelines, Kubernetes, Cloud proficiency
- Strong knowledge of cloud platforms (AWS, GCP, or Azure) and infrastructure-as-code tools (Terraform, CloudFormation).
- Experience with ML frameworks (TensorFlow, PyTorch, or similar) and orchestration tools (Kubeflow, Airflow, MLflow).
- Deep understanding of data pipelines, model deployment, real-time inference systems, and reliability of AI systems
- Strong communication skills and the ability to work across engineering and data science teams.
- Hands-on with CI/CD and Model Serving
Nice to Have
- Experience with feature stores, vector databases, or large-scale model training.
- Familiarity with streaming data technologies (Kafka, Spark Streaming, Flink).
- Knowledge of monitoring/observability tools (Prometheus, Grafana, Datadog).
- Contributions to open-source ML/MLOps projects.
- GPU optimization (TensorRT, ONNX, vLLM, Triton)
- Experience with low-latency audio/streaming systems
- Familiarity with vector DBs or feature stores
What we offer
- 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
Think you’re a good fit for this job?
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Speechify is committed to a diverse and inclusive workplace.
Speechify does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status.
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