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We are looking for a Senior Mobile Engineer who is passionate about building high-quality products, solving complex technical challenges, and delivering experiences that make a meaningful impact on people's lives. You will partner closely with Product, Design, Data Science, and Engineering teams to take products from concept to production while helping shape the future of our platform.
You will join an experienced engineering organization building products used by people around the world, working in an AI-augmented development environment that embraces modern tools to accelerate delivery, improve code quality, and increase engineering effectiveness.
Our Technology Stack: React Native, React, TypeScript, Redux, Scala, Play Framework, Microservices, AWS, Docker, Kubernetes, MongoDB, Redis, PostgreSQL, Spark, Elasticsearch, Kafka, and more.
Responsibilities:
Build and deliver scalable, reliable, and maintainable software that powers products used daily by members around the world.
Design and develop frontend and backend solutions with a focus on performance, scalability, and user experience.
Create reusable frontend architecture, components, and engineering patterns that improve development velocity and consistency.
Partner with Data Science teams to integrate algorithms and data-driven capabilities into new and existing products.
Collaborate closely with Product Managers, Designers, Data Engineers, and fellow engineers throughout the full product development lifecycle, including discovery, design, implementation, experimentation, and iteration.
Take ownership of projects from technical design through production deployment and ongoing optimization.
Contribute to architectural decisions and continuously improve engineering standards, processes, and platform capabilities.
Leverage AI tools throughout the software development lifecycle to accelerate development, improve code quality, and increase team productivity while maintaining strong engineering judgment.

We are looking for a Senior Full-Stack Engineer who specializes in Mobile application development and is passionate about building high-quality products, solving complex technical challenges, and delivering experiences that make a meaningful impact on people's lives. You will partner closely with Product, Design, Data Science, and Engineering teams to take products from concept to production while helping shape the future of our platform.
You will join an experienced engineering organization building products used by people around the world, working in an AI-augmented development environment that embraces modern tools to accelerate delivery, improve code quality, and increase engineering effectiveness.
Our Technology Stack: React Native, React, TypeScript, Redux, Scala, Play Framework, Microservices, AWS, Docker, Kubernetes, MongoDB, Redis, PostgreSQL, Spark, Elasticsearch, Kafka, and more.
Responsibilities:
Build and deliver scalable, reliable, and maintainable software that powers products used daily by members around the world.
Design and develop frontend and backend solutions with a focus on performance, scalability, and user experience.
Create reusable frontend architecture, components, and engineering patterns that improve development velocity and consistency.
Partner with Data Science teams to integrate algorithms and data-driven capabilities into new and existing products.
Collaborate closely with Product Managers, Designers, Data Engineers, and fellow engineers throughout the full product development lifecycle, including discovery, design, implementation, experimentation, and iteration.
Take ownership of projects from technical design through production deployment and ongoing optimization.
Contribute to architectural decisions and continuously improve engineering standards, processes, and platform capabilities.
Leverage AI tools throughout the software development lifecycle to accelerate development, improve code quality, and increase team productivity while maintaining strong engineering judgment.

We are looking for an experienced QA Engineer who is passionate about quality, adversarial thinking, and bug hunting. You will own the quality of your product domains from feature planning through production release, working closely with Product, Design, and Engineering teams throughout the development lifecycle.
In this role, you will design comprehensive test plans, execute exploratory, functional, regression, and end-to-end testing across mobile and backend systems, and drive issues to resolution. You will take ownership of the quality of your domain, prioritize risks, communicate findings clearly across the organization, and continuously improve QA processes and testing methodologies.
We are looking for someone who enjoys challenging assumptions, works independently, embraces AI tools to improve productivity, and has a strong sense of ownership over product quality.
Responsibilities:
Design and execute comprehensive test plans for new features and enhancements.
Perform exploratory, functional, regression, and end-to-end testing across mobile applications and backend services.
Use AI tools to improve productivity, testing efficiency, documentation, and problem solving.
Own the quality of assigned product domains from feature planning through production release.
Identify, investigate, prioritize, and communicate defects and product risks across teams.
Collaborate closely with Product, Design, and Engineering to ensure high-quality feature delivery.
Drive continuous improvements to QA processes, testing methodologies, and product quality.
Take ownership of your domain by proactively identifying risks, proposing solutions, and driving quality initiatives.

We're backed by tier-1 global VCs, led by second-time founders, and moving fast because the problem demands it. There are no silos here. No "that's not my area." If it ships, you own it.
We're hiring a full-stack software engineer who wants end-to-end ownership - backend, frontend, infrastructure touchpoints, and AI-driven workflows.
If you're looking for narrow scope or strict boundaries, this is not the role. If you like building real systems and taking responsibility for outcomes, read on.
What you'll do:
Own systems end-to-end - infrastructure, pipelines, and their behavior in production
Run and evolve our cloud infrastructure - Kubernetes, Kafka, Terraform, GitOps (AWS today, multi-cloud ahead)
Build the data backbone - ingestion, streaming, and pipelines into our lakehouse that stay correct and affordable as volume grows
Build AI-native engineering workflows: Sandboxes and dev environments, Automated verification and guardrails, Feedback loops that keep quality up as output scales.
Build golden paths - repo to production without a human bottleneck
Use Claude / Codex / AI copilots as default tools - not optional helpers
Debug production issues across the stack (no handoffs)
Ship fast, learn fast, fix fast
Take responsibility for what you build - including after it's live

We're backed by tier-1 global VCs, led by second-time founders, and moving fast because the problem demands it. There are no silos here. No "that's not my area." If it ships, you own it.
We're hiring a full-stack software engineer who wants end-to-end ownership - backend, frontend, infrastructure touchpoints, and AI-driven workflows.
If you're looking for narrow scope or strict boundaries, this is not the role. If you like building real systems and taking responsibility for outcomes, read on.
What you'll do:
Own features end-to-end - backend, frontend, APIs, and behavior in production
Build AI-native workflows:
Agent-driven actions
Human-in-the-loop interfaces
Explainability and control surfaces
Use Claude / Codex / AI copilots as default tools - not optional helpers
Work directly with product and customers to refine behavior
Debug production issues across the stack (no handoffs)
Ship fast, learn fast, fix fast
Take responsibility for what you build - including after it's live

Were seeking a Senior Machine Learning Engineer with strong MLOps abilities to own the production ML systems behind user engagement, cardiovascular risk stratification, and personalized health recommendations in the app. You'll build and run the CI/CD, real-time serving, versioning, and monitoring infrastructure that models depend on, working closely with data scientists who own the modeling to make sure what they build runs reliably at scale. You should have a proven track record of shipping ML systems to production and be comfortable using AI coding assistants as a core part of your workflow.
Responsibilities:
Build and own production ML infrastructure, including CI/CD pipelines, real-time model serving, model versioning and registries, automated evaluation pipelines, monitoring, and observability.
Serve models in real time at scale, with a strong focus on low-latency inference, reliability, and graceful degradation under load, and operate batch inference with clear standards for latency, availability, correctness, and cost.
Write high-quality, maintainable, well-tested production-grade code, and own its observability, debugging, reliability, and scalability in production.
Implement model monitoring for drift, data quality, latency, and performance degradation, with automated retraining.

We're backed by tier-1 global VCs, led by second-time founders, and already deployed with organizations operating at serious scale. AI is not a feature here - it's the system.
We're hiring a Senior AI Engineer to design, fine-tune, and operate AI agents and large-scale models in production. This role exists because off-the-shelf models aren't enough for the problems we're solving.
If you enjoy pushing models until they break - and then fixing them - keep reading.
What you'll do:
Design and operate AI agents that reason, act, and collaborate with humans
Fine-tune and adapt large language models for:
Behavior analysis
Reasoning over long, messy timelines
High-precision enterprise workflows
Build agent orchestration systems (tool use, memory, planning, feedback loops)
Run large-scale inference and training pipelines in production
Work on model evaluation, drift detection, and continuous improvement
Optimize for latency, cost, and reliability at real enterprise scale
Partner closely with DevOps, security, and backend engineers - no research silos
Ship models that are auditable, explainable, and safe in sensitive environments

Were seeking a talented and impact-driven Senior Data Analyst, Product to join our ML team. In this role, you will shape the future of the digital health products that help people understand and improve their heart health. By applying your technical skills, analytical mindset, and product intuition to rich real-world health and behavioral data, you will help define the experiences we build for our users and the value we deliver to employers and health plans.
You will collaborate with a wide range of cross-functional partners across Product, Engineering, Data Science, Data Engineering, Clinical, Marketing, and Customer Success. You will use data and analysis to identify and solve our biggest product challenges, influence product strategy and investment decisions, and translate insights into digital solutions that improve the well-being of the people we serve.
What you'll do:
Product leadership: Use data to shape product development, quantify new opportunities, and identify upcoming challenges. Help your partner teams prioritize what to build, set goals, and understand their product's ecosystem, ensuring what we build brings real value to users, clients, and us.
Analytics: Guide teams with data and insights. Develop hypotheses and test them using a varied toolkit of rigorous analytical approaches, from experimentation to causal inference and statistical modeling.
Communication and influence: Don't just present data, tell data-driven stories. Influence partners with clear insights and recommendations, and become a trusted strategic partner through structure and clarity.
Your responsibilities:
Work with large and complex datasets, applying statistical analysis and visualization methods to extract meaningful patterns and trends.
Identify and measure the success of product efforts through goal setting, forecasting, and monitoring of key product and engagement metrics.
Design, execute, and analyze A/B tests, including tests of ML-driven features, accounting for cause/effect and treatment/prevention relationships.
Monitor and analyze the performance of our ML models in production, and generate insights and research leads for model improvements and new data-driven solutions.
Define, understand, and test opportunities and levers to improve the product, and drive roadmaps through your insights and recommendations.
Partner with Product, Engineering, Data Science, and healthcare professionals to inform and execute product strategy.
Integrate AI tools into your workflows to drive measurable gains in efficiency and quality.
Stay current with the latest research and best practices in healthcare analytics and AI.

Required Data Scientist
Tel Aviv
Full-Time
About Us:
Were building the first AI-native autonomous revenue engine for D2C brands, a new category of AI infrastructure replacing the entire revenue stack.
If you want to build category-defining infrastructure with a team that ships weekly, lets talk.
About the Role:
As a Data Scientist, youll be at the heart of the intelligence behind our product. Youll develop predictive models, analyze complex behavioral datasets, and collaborate across engineering and product to turn insights into real-time actions for our clients. This is a hands-on role with plenty of ownership and room to grow.
Responsibilities
Model Development:
Design scalable ML models that predict customer churn, ideal purchase timing, product recommendations, and more.
Data Exploration & Feature Engineering:
Structure and clean large datasets, develop features, and surface key behavioral patterns.
Collaborate with Engineering:
Work closely with developers to implement models and ensure performance at scale.
Business Insight Generation:
Turn data into actionable insights that drive product features and client decisions.

Required Founding Engineer
Tel Aviv
Full-Time
About Us:
Were building the first AI-native autonomous revenue engine for D2C brands, a new category of AI infrastructure replacing the entire revenue stack.
If you want to build category-defining infrastructure with a team that ships weekly, lets talk.
About the Role:
This is a broad, hands-on engineering role.
Youll work directly with the founders and own the most challenging parts of the platform end to end.
The focus is backend and infrastructure, but youll work across the full stack whenever a problem calls for it.
Youll help shape the technical direction, and how engineering works as the team grows.
Responsibilities:
Own the Challenging Problems - Take the most complex parts of the platform end to end, from architecture through production.
Shape Architectural Decisions - Help define service boundaries, data models, and technical direction as the platform scales.
Build the Core - Design and ship the systems at the heart of the platform.
Scale the Data Pipeline - Build the ingestion, processing, and orchestration layers that move data through the platform reliably.
Own Infrastructure - Shape deployment, observability, and reliability across cloud infrastructure and Kubernetes.
Build End to End - Work across the full stack when the problem calls for it, including the surfaces your services power.

Required Founding ML Engineer
Tel Aviv
Full-Time
About Us:
Were building the first AI-native autonomous revenue engine for D2C brands, a new category of AI infrastructure replacing the entire revenue stack.
If you want to build category-defining infrastructure with a team that ships weekly, lets talk.
About the Role:
Youll build the platform our data science team works on.
That means the pipelines, tooling, and infrastructure that let data scientists train, deploy, and monitor models without needing an engineer at every step - and working alongside them to get their models onto the platform when they do.
The work is predictive ML: churn, purchase timing, and recommendations, running for enterprise clients at scale.
Youll help shape how ML is built and operated here as the team grows.
Responsibilities:
Build the ML Platform - Design the pipelines, tooling, and infrastructure the data science team builds on.
Enable Self-Service - Make it possible for data scientists to train, deploy, and monitor models without an engineer in the loop.
Implement Models on the Platform - Work alongside data scientists to take their models from notebook to production.
Own Features at Scale - Build the feature pipelines that keep training and inference consistent.
Keep Models Healthy - Build the monitoring and retraining that keep predictions accurate over time.
Shape How We Build ML - Help define the tooling, standards, and practices for ML as the team grows.

We are seeking an experienced sharp and dynamic analytical mind to join our Product Analytics team as a Strategic product analyst. This position is ideally suited for those who are naturally passionate about learning, gaining knowledge, and analyzing anything they do and see with experience in utilizing data to make a significant impact.
The role requires a combination of analytical, technical, problem-solving, communication, and project management skills, and it involves working closely with various stakeholders and customers - including Product Managers, Customer Success Managers, Operations teams, Data Engineers, UI/UX Designers, SW developers, and AI experts at all levels.
Responsibilities
As a Strategic product analyst at our companys Analytics group, you will play a critical role in expanding our data-driven vision and building analytics capabilities that create value for both our customers and internal teams. Your work will include:
Building scalable customer-facing analytics and data-sharing products, transforming complex data and customer needs into reliable, intuitive, and standardized solutions.
Partnering with Customer Success and Product teams to understand how customers use our products, identify opportunities to improve adoption and demonstrate value, and translate site needs into scalable analytical solutions.
Leading strategic analyses across products, workflows, and clinical domains, connecting data from multiple parts of our companys ecosystem to uncover insights and support business and product decisions.
Designing product KPIs and measurement frameworks, monitoring trends, and working with key stakeholders to translate findings into actionable insights.
Conducting decision-support analyses for Product, Customer Success, development, and operational initiatives.
Designing and improving scalable data models and analytics infrastructure that support customer-facing products, internal analytics, and repeatable analytical capabilities.
Continuously expanding our analytics knowledge base, improving data quality and reliability, and documenting learnings to strengthen the teams collective expertise.
The role is all about collaborating with stakeholders to craft their questions into tangible metrics and captivating visuals, empowering them to uncover insights and make informed decisions through the power of data. Plus, you get to dive deep into learning opportunities and document your discoveries, enriching our collective intelligence and fueling continuous improvement.

We're looking for a technical support engineer with 2-3 years of experience to own the support queue for our enterprise customers, most of them in the US. You'll dig into the logs and the data to find out why something broke, and help run customer data migrations and the monitoring that catches problems before customers do.
What Youll Do:
Own the Support Queue: Take tickets from first response through resolution. You are responsible for meeting our response times, keeping every open ticket moving, and closing standard issues on your own.
Investigate Before You Escalate: Reproduce the problem, read the logs and the data, and isolate the cause. When something needs engineering, it reaches them with findings and evidence rather than a forwarded complaint.
Communicate with Customers: Keep customers informed in writing, manage expectations honestly, and stay credible even when the answer is not ready yet.
Run Customer Data Migrations: Map and validate customer data as it moves into, and catch the records that don't fit before the customer does.
Write the Help Center: Turn what you solve into articles on common actions and troubleshooting, so the answer exists the next time someone asks.
Build Customer Enablement: Create and maintain a library of short workflow recordings, and run group training sessions, webinars and office hours as the role grows.
Monitor Proactively: Build and run monitors over our observability stack, triage the alerts they raise, and reach out to customers before they notice the problem themselves.

Were looking for a a senior backend engineer with 6+ years of experience, playing a key role in building impactful, user-centric products from the ground up. As part of the companys early engineering team, youll help shape both the product and the engineering culture, own backend architecture, lead end-to-end feature development, and drive technical excellence while collaborating closely with product, design, and infrastructure in a fast-growing environment.
What Youll Do:
Define and evolve the backend architecture, infrastructure and development best practices for a fast-growing team.
Own features end-to-end - from concept and design discussions to implementation, deployment and observability.
Solve complex challenges in distributed systems, event-driven architectures and real-time data pipelines.
Continuously improve developer workflows - identify friction, integrate new tools, automate repetitive tasks and champion engineering excellence.
Lead and mentor a small team of engineers: drive planning, break down work, ensure quality delivery and help hire exceptional talent.
Collaborate with experienced peers - engineers, PMs and designers - whove built large-scale systems at top-tier companies.
Here are some of the technologies we work with: Golang, Python, Protobuf, React, Typescript, Kafka, Postgresql, Redis, Kubernetes, Terraform and AWS.

We are seeking an experienced Solutions Data Engineer who possess both technical depth and strong interpersonal skills to partner with internal and external teams to develop scalable, flexible, and cutting-edge solutions. Solutions Engineers collaborate with operations and business development to help craft solutions to meet customer business problems.
A Solutions Engineer works to balance various aspects of the project, from safety to design. Additionally, a Solutions Engineer researches advanced technology regarding best practices in the field and seek to find cost-effective solutions.
Job Description:
Were looking for a Solutions Engineer with deep experience in Big Data technologies, real-time data pipelines, and scalable infrastructure-someone whos been delivering critical systems under pressure, and knows what it takes to bring complex data architectures to life. This isnt just about checking boxes on tech stacks-its about solving real-world data problems, collaborating with smart people, and building robust, future-proof solutions.
In this role, youll partner closely with engineering, product, and customers to design and deliver high-impact systems that move, transform, and serve data at scale. Youll help customers architect pipelines that are not only performant and cost-efficient but also easy to operate and evolve.
We want someone whos comfortable switching hats between low-level debugging, high-level architecture, and communicating clearly with stakeholders of all technical levels.
Key Responsibilities:
Build distributed data pipelines using technologies like Kafka, Spark (batch & streaming), Python, Trino, Airflow, and S3-compatible data lakes-designed for scale, modularity, and seamless integration across real-time and batch workloads.
Design, deploy, and troubleshoot hybrid cloud/on-prem environments using Terraform, Docker, Kubernetes, and CI/CD automation tools.
Implement event-driven and serverless workflows with precise control over latency, throughput, and fault tolerance trade-offs.
Create technical guides, architecture docs, and demo pipelines to support onboarding, evangelize best practices, and accelerate adoption across engineering, product, and customer-facing teams.
Integrate data validation, observability tools, and governance directly into the pipeline lifecycle.
Own end-to-end platform lifecycle: ingestion → transformation → storage (Parquet/ORC on S3) → compute layer (Trino/Spark).
Benchmark and tune storage backends (S3/NFS/SMB) and compute layers for throughput, latency, and scalability using production datasets.
Work cross-functionally with R&D to push performance limits across interactive, streaming, and ML-ready analytics workloads.
Operate and debug object store-backed data lake infrastructure, enabling schema-on-read access, high-throughput ingestion, advanced searching strategies, and performance tuning for large-scale workloads.