Software Engineer, Platform - Aurora, USA at Speechify

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Full Time

Start Date

Immediate

Expiry Date

26 Jan, 26

Salary

0.0

Posted On

28 Oct, 25

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, Machine Learning, Cloud Environments, GCP, Docker, Containerized Deployments, Kubernetes, High-Availability Applications, ML Models, Production Services, User Experience, Technical Skills, Communication Skills, Innovation, Problem Solving, AI

Industry

Software Development

Description

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 App of the Day. 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. This is a key role and ideal for someone who thinks strategically, enjoys fast-paced environments, passionate about making product decisions, and has experience building great user experiences that delight users. We are a flat organization that allows anyone to become a leader by showing excellent technical skills and delivering results consistently and fast. Work ethic, solid communication skills, and obsession with winning are paramount. Our interview process involves several technical interviews and we aim to complete them within 1 week. What You’ll Do 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 An Ideal Candidate Should Have 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 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? Tell us more about yourself and why you're interested in the role when you apply. And don’t forget to include links to your portfolio and LinkedIn. Not looking but know someone who would make a great fit? Refer them! 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.

Responsibilities

Work alongside machine learning researchers and engineers to deploy and operate core ML inference workloads. Introduce new techniques and tools to improve the performance and efficiency of deployed models.