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're looking to hire for our Data side of our AI team at Speechify. This role is responsible for all aspects of data collection to support our model training operations. We are able to build high-quality datasets at petabyte-scale and low cost through a tight integration of infrastructure, engineering, and research work. We are looking for a skilled Software Engineer to join us.

What You’ll Do

• Be scrappy to find new sources of audio data and bring it into our ingestion pipeline
• Operate and extend the cloud infrastructure for our ingestion pipeline, currently running on GCP and managed with Terraform.
• Collaborate closely with our Scientists to shift the cost/throughput/quality frontier, delivering richer data at bigger scale and lower cost to power our next-generation models.
• Collaborate with others on the AI Team and Speechify Leadership to craft the AI Team’s dataset roadmap to power Speechify’s next-generation consumer and enterprise products.

An Ideal Candidate Should Have

• BS/MS/PhD in Computer Science or a related field.
• 5+ years of industry experience in software development.
• Proficiency with bash/Python scripting in Linux environments
• Proficiency in Docker and Infrastructure-as-Code concepts and professional experience with at least one major Cloud Provider (we use GCP)
• Experience with web crawlers, large-scale data processing workflows is a plus
• Ability to handle multiple tasks and adapt to changing priorities.
• Strong communication skills, both written and verbal.

What we offer

• A fast-growing environment where you can help shape the company and product.
• An entrepreneurial-minded team that supports risk, intuition, and hustle.
• A hands-off management approach so you can focus and do your best work.
• An opportunity to make a big impact in a transformative industry.
• Competitive salaries, a friendly and laid-back atmosphere, and a commitment to building a great asynchronous culture.
• Opportunity to work on a life-changing product that millions of people use.
• Build products that directly impact and support people with learning differences like dyslexia, ADD, low vision, concussions, autism, and more.
• Work in one of the fastest-growing sectors of tech, the intersection of artificial intelligence and audio.

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.

About the role 

We are seeking an accomplished Senior Data Engineer to join our dynamic and rapidly expanding Tech Ops division. With significant projected growth, this is an opportunity to drive meaningful technical impact. In this role, you will work with internal engineering teams to design, implement, and optimize complex data integration solutions within a modern, large‑scale cloud environment. 

You will leverage advanced skills in distributed computing, data architecture, and cloud-native engineering to enable scalable, resilient, and high‑performance data ingestion and transformation pipelines. As a trusted technical advisor, you will ensure high-quality, compliant data operations across the lifecycle. 

Your day to day 

• Architect, design, and implement high‑volume batch and real‑time data pipelines using PySpark, SparkSQL, Databricks Workflows, and distributed processing frameworks.
• Build end‑to‑end ingestion frameworks integrating Databricks, Snowflake, AWS services (S3, SQS, Lambda), and vendor APIs, ensuring data quality, lineage, and schema evolution.
• Design and optimize data models (star/snowflake schemas) and apply performance tuning techniques for analytical workloads on cloud data warehouses.
• Translate complex business requirements into detailed technical specifications, reusable engineering components, and implementation artifacts.
• Establish and enforce data engineering best practices, including CI/CD for data pipelines, version control, automated testing, orchestration, logging, and observability.
• Drive performance and cost optimization through profiling, cluster tuning, partitioning, indexing, caching, and compute optimization across Databricks and Snowflake.
• Ensure operational excellence and team growth by producing high‑quality documentation (runbooks, architecture diagrams), monitoring and troubleshooting production pipelines, performing root‑cause analysis, and mentoring junior engineers.

What you bring to the team 

• Bachelor’s degree in Computer Science, Computer Engineering, or a closely related technical field, with 5+ years of hands‑on experience as a Data Engineer building and operating large‑scale, distributed data systems in modern cloud environments.
•  Proven ability to clearly communicate complex technical concepts and solutions to both technical and non‑technical stakeholders.
•  Expert‑level proficiency in Python, SQL, and PySpark, including development of distributed transformations and performance‑optimized queries.
• Demonstrated experience designing, building, and operating ETL/ELT pipelines using Databricks, Airflow, or similar orchestration and workflow automation tools.
• Proven experience architecting or operating large‑scale data platforms using DBT, Kafka, Delta Lake, and event‑driven or streaming architectures in cloud‑native data or platform engineering environments.
• Strong working knowledge of AWS data services (S3, SQS, Lambda, Glue, IAM or equivalents), structured and semi‑structured data formats (Parquet, ORC, JSON, Avro), schema evolution, and optimization techniques.
• Hands‑on experience with Terraform and CI/CD pipelines (e.g., GitLab), deep expertise in SQL and compute optimization (partitioning, clustering, Z‑Ordering, pruning, caching), and performance tuning on cloud data warehouses such as Snowflake (preferred), BigQuery, or Redshift

What we would like to see but not required: 

• Working knowledge of U.S. healthcare data domains—including claims, eligibility, and provider datasets—and experience applying this knowledge to complex ingestion and transformation workflows. 

What you’ll get in return 

• Competitive Leave & Benefits
• Comprehensive health coverage
• Equity for every employee – share in our success
• Growth-focused environment – your development matters here

Working Arrangements

• Standard hours: 9 hours/day, 5 working days
• Location: Onsite
• Shift: 10 AM – 7 PM local time

About the Role

The Site Reliability and Forward Deployed Engineer is an experienced individual contributor responsible for supporting production operations, incident response, and post‑launch reliability across the Abacus Insights platform.

This role combines:

• Production operations and reliability engineering
• Deep technical problem-solving during incidents and escalations
• Customer‑facing technical support for deployments and issues
• Hands‑on software development and automation

You will work on complex production issues involving AWS infrastructure, Databricks workloads, and data pipelines, partnering closely with senior engineers and platform teams. You will engage with customers during escalations and deployments and help translate learnings into product and operational improvements. This is a hands‑on role. You are expected to actively participate in incidents, debug live systems, and write production code that improves system reliability and operability, with guidance from senior technical leaders.

Your day to day

• Act as a technical escalation point during production incidents, leading real‑time triage, mitigation, and recovery efforts.
• Drive root cause analysis (RCA) and ensure follow‑up remediation and reliability improvements are completed and tracked.
• Support post‑launch reliability by investigating and resolving production defects and field‑reported issues.
• Partner closely with Engineering, Data, and Customer Success teams to support customers during high‑impact incidents and escalations.
• Improve operational readiness through better runbooks, monitoring, alerting, and incident response practices.
• Work directly with customers on complex deployments, integrations, and production troubleshooting in customer environments.
• Write production‑quality code (primarily Python) to automate operational workflows, improve observability, and reduce manual toil.
• Support and troubleshoot AWS‑ and Databricks‑based production systems, including performance, scalability, and data correctness issues.
• Capture learnings from incidents and customer engagements and translate them into reusable tools, documentation, and product improvements.

What you bring to the team

• 3 - 5 years of experience in software engineering, SRE, sustaining engineering, or production operations
• Hands‑on experience supporting production systems in AWS
• Experience troubleshooting large‑scale data platforms or Databricks
• Proficiency in Python and experience building or supporting production services or tooling
• Working knowledge of:
  • Distributed systems fundamentals
  • Incident response and RCA practices
  • Monitoring, alerting, and observability
  • CI/CD pipelines and Infrastructure as Code concepts
• Strong communication skills during incidents and customer interactions
• Ability to take ownership of issues through resolution with support and guidance
• Good operational judgment and willingness to learn how to proactively reduce risk

What we would like to see, but not required

• Experience in regulated or healthcare data environments
• Familiarity with Kubernetes (EKS), EMR, Lambda, Spark internals, Snowflake, Databricks or similar data warehouses
• Experience participating in on‑call or SRE programs
• Prior customer‑facing or escalation engineering experience

What you’ll get in return 

• Competitive Leave & Benefits
• Comprehensive health coverage
• Equity for every employee – share in our success
• Growth-focused environment – your development matters here

Working Arrangements

• Standard hours: 9 hours/day, 5 working days
• Location: Onsite
• Shift: 1 PM - 10 PM local time

About the Role:

We are seeking an experienced Senior Data QA Engineer to lead the design and implementation of comprehensive data quality testing strategies for our data platforms and pipelines. In this role, you will be responsible for ensuring data integrity, accuracy, and reliability across our data ecosystem through advanced testing methodologies and automation frameworks.

Your day to day:

• Design and implement end-to-end data quality testing strategies for data pipelines, ETL/ELT processes, and data warehouses
• Develop automated data validation frameworks to verify data accuracy, completeness, and consistency
• Create and maintain data quality metrics, monitor dashboards, and alert mechanisms
• Perform complex SQL queries to validate data transformations and business logic
• Design test cases for data ingestion, processing, and consumption layers
• Collaborate with data engineers, data scientists, and analysts to understand data requirements and quality standards
• Lead root cause analysis for data quality issues and implement preventive measures
• Mentor junior QA engineers and establish data testing best practices

What We're Looking For:

• Bachelor’s or master’s degree in computer science, Business Administration, or comparable professional experience
• Minimum 5 years of data quality assurance expertise within healthcare technology companies or health insurance organizations
• Proficient in complex data transformation and manipulation techniques
• Advanced SQL expertise with proven experience in data integration and validation
• Comprehensive understanding of ETL/ELT workflows and quality assurance methodologies for data processing validation
• In-depth expertise in data pipeline architecture and implementing quality control frameworks
• Practical experience with Spark SQL for distributed data processing
• Extensive knowledge of healthcare data domains including member enrollment, medical claims processing, and prescription drug claims; familiarity with emerging data sources (wellness programs, productivity metrics, and electronic medical records) is advantageous
• Outstanding verbal and written communication abilities with demonstrated success in managing multiple priorities and delivering on tight timelines
• Exceptional collaborative and relationship-building capabilities
• Self-directed professional who excels both as an individual contributor and team player, with proven mentoring experience
• Hands-on experience with scripting languages, particularly Python and Java
• Comprehensive expertise in AWS cloud services including S3, EC2, Systems Manager, Athena, and Databricks platform
• Thorough understanding of software testing methodologies, debugging techniques, and quality assurance protocols

Nice to Have:

• Experience with Big Data, Snowflake, and ETL test automation
• Hands‑on API testing experience
• Healthcare domain knowledge, including HEDIS quality measures
• Machine Learning and Artificial Intelligence concepts with practical application experience in production environments
• Proficiency in testing automation frameworks and database technologies

What you’ll get in return 

• Competitive Leave & Benefits
• Comprehensive health coverage
• Equity for every employee – share in our success
• Growth-focused environment – your development matters here

Working Arrangements

• Standard hours: 9 hours/day, 5 working days
• Location: Onsite
• Shift: 10 AM – 7 PM local time