We are hiring an Engineering Manager to own and grow the Data Science group - the R&D team behind our company's security and safety AI models and the data platform that powers them. You will lead a multidisciplinary team of ML engineers, data scientists, and software engineers who ship real-time inference at scale, automated red-teaming of GenAI systems, and the Databricks/Spark data platform underneath. This is a hands-on people-leadership role: you set technical direction, are accountable for delivery and quality, and you build and grow the team.
What your team owns
The group is responsible for a large Python + Rust + PySpark monorepo (dozens of production services and shared libraries) spanning three connected domains:
1. Content-moderation inference at scale
Real-time, multi-tenant detection across text, image, video, and audio - hate speech, CSAM, nudity, child grooming, extremism, PII, prompt injection, age estimation, and more - served through an in-house ActiveServe framework over NVIDIA Triton and a Rust detection monolith, on latency-sensitive, SLA-bound, customer-facing traffic with per-customer custom models.
2. GenAI safety & red-teaming
Automated red-teaming that attacks customers' LLM applications with a research-driven attack taxonomy and measures attack-success rate, alongside the defensive side - LLM-as-judge escalation to cut false positives and the tooling that authors and refines moderation policies - built on a multi-provider LLM foundation (Bedrock, Anthropic, OpenAI, Gemini, xAI Grok) and forming our company's leading edge into agentic-AI safety.
3. Data platform & MLOps
An end-to-end lakehouse and MLOps stack on Databricks - bronze/silver/gold ingestion, PySpark pipelines, and model training, versioning, and promotion through MLflow / Unity Catalog into Triton serving - with the performance-critical hot paths engineered in Rust (PyO3/maturin) for sub-millisecond, high-QPS matching and detection.
What you'll do
Lead and grow the team - mentor ML engineers, data scientists, and software engineers, and own hiring, onboarding, 1:1s, career development, and performance.
Set direction and deliver - set technical direction and standards, turn company and product goals into a prioritized roadmap, and own the quality, reliability, and delivery of the systems above across parallel workstreams.
Champion excellence and partnership - stay hands-on to review designs and unblock the team, drive engineering excellence (testing, observability, CI/CD, on-call, cost/latency), keep the team at the state of the art in ML, LLMs, and GenAI safety, and partner with Product, Platform, and GenAI-safety stakeholders.
Leadership competencies
People-first: builds trust, grows engineers, and creates a healthy, inclusive, high-ownership culture.
Outcome-oriented: drives clarity, sets priorities, and delivers under ambiguity without micromanaging.
Technical credibility: earns the team's respect through sound judgment on architecture and trade-offs.
Systems thinker: balances short-term delivery against long-term platform health, cost, and tech debt.

We are hiring an Engineering Manager to own and grow the Data Science group - the R&D team behind Alice's security and safety AI models and the data platform that powers them. You will lead a multidisciplinary team of ML engineers, data scientists, and software engineers who ship real-time inference at scale, automated red-teaming of GenAI systems, and the Databricks/Spark data platform underneath. This is a hands-on people-leadership role: you set technical direction, are accountable for delivery and quality, and you build and grow the team. What your team owns The group is responsible for a large Python + Rust + PySpark monorepo (dozens of production services and shared libraries) spanning three connected domains: 1. Content-moderation inference at scale Real-time, multi-tenant detection across text, image, video, and audio - hate speech, CSAM, nudity, child grooming, extremism, PII, prompt injection, age estimation, and more - served through an in-house ActiveServe framework over NVIDIA Triton and a Rust detection monolith, on latency-sensitive, SLA-bound, customer-facing traffic with per-customer custom models. 2. GenAI safety & red-teaming Automated red-teaming that attacks customers' LLM applications with a research-driven attack taxonomy and measures attack-success rate, alongside the defensive side - LLM-as-judge escalation to cut false positives and the tooling that authors and refines moderation policies - built on a multi-provider LLM foundation (Bedrock, Anthropic, OpenAI, Gemini, xAI Grok) and forming Alice's leading edge into agentic-AI safety. 3. Data platform & MLOps An end-to-end lakehouse and MLOps stack on Databricks - bronze/silver/gold ingestion, PySpark pipelines, and model training, versioning, and promotion through MLflow / Unity Catalog into Triton serving - with the performance-critical hot paths engineered in Rust (PyO3/maturin) for sub-millisecond, high-QPS matching and detection. What you'll do
* Lead and grow the team - mentor ML engineers, data scientists, and software engineers, and own hiring, onboarding, 1:1s, career development, and performance.
* Set direction and deliver - set technical direction and standards, turn company and product goals into a prioritized roadmap, and own the quality, reliability, and delivery of the systems above across parallel workstreams.
* Champion excellence and partnership - stay hands-on to review designs and unblock the team, drive engineering excellence (testing, observability, CI/CD, on-call, cost/latency), keep the team at the state of the art in ML, LLMs, and GenAI safety, and partner with Product, Platform, and GenAI-safety stakeholders. Leadership competencies
* People-first: builds trust, grows engineers, and creates a healthy, inclusive, high-ownership culture.
* Outcome-oriented: drives clarity, sets priorities, and delivers under ambiguity without micromanaging.
* Technical credibility: earns the team's respect through sound judgment on architecture and trade-offs.
* Systems thinker: balances short-term delivery against long-term platform health, cost, and tech debt.



About Alice:
Alice is a trust, safety, and security company built for the AI era. We safeguard the communicative technologies people use to create, collaborate, and interact- whether with each other or with machines. In a world where AI has fundamentally changed the nature of risk, Alice provides end-to-end coverage across the entire AI lifecycle. We support frontier model labs, enterprises, and UGC platforms with a comprehensive suite of solutions: from model hardening evaluations and pre-deployment red-teaming to runtime guardrails and ongoing drift detection. Alice is widely considered a global leader in online safety and AI security. We have some of the most forward-thinking and passionate minds in the world working to safeguard over 3 billion users across the largest AI and tech platforms. If you're creative an

As an MLOps Team Leader, you will own our AI infrastructure and backend engineering efforts, leading a team at the intersection of systems design and machine learning to build the engineering systems that turn Deep Learning and Computer Vision research into reliable, scalable production features.

What Youll Be Doing:
Lead MLOps Engineering: Own the roadmap and execution for a team of backend and AI infrastructure/MLOps engineers, setting technical direction, removing blockers, and holding the bar on delivery quality.
Build Production AI Systems: Design and implement production-grade, end-to-end AI solutions, including agentic workflows, that integrate Deep Learning models and Computer Vision algorithms into real features. Own the offline experimentation and validation layer, including POC environments that let the Algo team move from research to production confidently.
Architect Data Platforms: Drive the design and implementation of scalable data platforms and pipelines that power our AI capabilities reliably and with an eye on cost and scale.
Cross-functional Collaboration: Work closely with the Algorithms and Data teams to tackle complex, real-world problems, translating research into shippable, maintainable systems.
Elevate Engineering Standards: Champion Software Engineering and System Design best practices across the group, introducing the right methodologies, tooling, and culture of craft.

We are seeking a highly motivated AI Researcher to join our core data Science team. In this role, you will be at the forefront of tackling complex, high-impact business challenges by leveraging cutting-edge GenAI technologies, advanced AI research, and classical Machine Learning and statistical modeling. You will not just be building models; you will be driving research from ideation to production. We are looking for a true self-learner, someone who thrives in an environment that demands both a strong sense of ownership over your deliverables and deep collaboration. You will work closely with fellow researchers, product managers, and software developers to translate abstract business problems into scalable data -driven solutions. If you are passionate about staying ahead of the AI curve, building agentic workflows, and proactively driving your research initiatives forward within a collaborative team structure, this is the perfect role for you.
What You Will Do: End-to-End Research & Ownership: Lead targeted research projects from initial hypothesis through to production-ready solutions. Take strong ownership of your work's performance and partner with engineering to ensure successful, scalable integration Applied AI Research & Integration: Lead research into LLMs, agentic systems, and modern AI methods, then design and build the workflows that bring them into our ecosystem. LLM Evaluation & Optimization: Contribute to and establish rigorous evaluation frameworks for LLMs to ensure accuracy, safety, and business alignment in practical applications. Cross-Functional Collaboration: Act as a bridge between data, engineering, and product. Work with Developers to ensure seamless integration of ML features into the core product. Continuous Innovation: Act as an internal advocate for emerging AI research and methodologies. Continuously read, experiment, and implement state-of-the-art techniques to advance our research agenda and accelerate research velocity.

Position Intro:
Earnix is the first AI company purpose-built for insurance decisioning, providing the industry with the intelligence, governance, and decisioning agility to drive profitable growth, improve resilience, and operate with speed and precision in a rapidly changing risk environment. A trusted provider of production-grade AI and decisioning technology, Earnix builds on more than 25 years of experience in artificial intelligence risk, pricing, rating, analytics, and decisioning. Earnix brings vertical AI into the workflows and decisions that shape insurance performance across insurers pricing, underwriting, claims, customer engagement, retention, and other insurers high-value insurance moments. Earnixs AIOS enables insurers to orchestrate intelligence across the insurance decision lifecycle and drive governed, Real-Time decisions that deliver measurable business outcomes. Earnix is headquartered in Boston, MA and has been innovating for insurers and banks since 2001 with customers in over 35 countries across six continents and offices in the Americas, Europe, Asia Pacific, and Israel.

We are seeking an experienced and highly motivated Team Lead to lead our Data Infrastructure team. This role is responsible for managing a team of engineers while driving the design, scalability, reliability, and operational excellence of the companys core data platform.
The ideal candidate combines strong technical expertise in distributed systems and data technologies with proven leadership capabilities, strategic thinking, and the ability to lead complex initiatives end-to-end - from initial design and planning through implementation, deployment, and production ownership.
Key Responsibilities:
Lead, mentor, and develop a team of Data Infrastructure engineers, fostering a culture of ownership, collaboration, and continuous improvement
Define and execute the technical roadmap for the organizations data infrastructure platform
Oversee the architecture, scalability, reliability, and performance of large-scale data systems and pipelines
Define and drive data quality standards, monitoring, and validation processes to ensure trust and reliability across the data platform
Partner with engineering and product teams to enable scalable data foundations for analytics, machine learning, and AI-driven initiatives
Lead cross-functional technical initiatives end-to-end, including requirements gathering, architecture design, implementation, deployment, and post-production ownership
Partner closely with cross-functional teams including Data Engineering, Analytics, Backend Engineering, DevOps, and Product Management
Drive operational excellence through monitoring, observability, automation, and incident management best practices
Ensure high availability and stability of critical production systems
Lead technical design discussions, architecture reviews, and engineering planning processes
Establish and promote engineering standards, development best practices, and quality assurance processes
Manage project prioritization, resource allocation, and timely delivery of strategic initiatives
Support recruitment, onboarding, performance management, and career development of team members
Evaluate emerging technologies and recommend improvements to the data platform ecosystem

Required Senior ML Data Engineer
About the team:
The AI Engineering group builds modern infrastructure and solutions that improve how algorithms are developed.
We are a small, independent team of experienced engineers with a mix of skills in algorithms, software, and infrastructure. We work in a DevOps style and build cross-team solutions that support research and development of advanced perception algorithms.
Our flagship project is a unified AV dataset used to train and evaluate next-generation models. We take large volumes of multi-camera video, object labels, HD maps, and sensor data from across the organization, and turn it into a curated, high-quality training set - at scale.
We are looking for someone who brings ML and computer-vision depth to the team - someone who can help shape the intelligence layer that decides what data is worth training on.
What will your job look like:
Work collaboratively with shared ownership. Your focus area will be the curation and ML side of our data pipeline, but you will contribute across the full stack alongside the rest of the team.
Build and improve the curation pipeline - from vision-model embeddings and scene detection, through VLM-based scene analysis, to scoring, deduplication, and sampling that produces a balanced and diverse dataset.
Run and optimize GPU inference at scale (embedding extraction, VLM inference) across thousands of driving sessions using workflow orchestration.
Develop scoring and sampling strategies that ensure rare but important scenarios (night driving, adverse weather, hazardous situations) are well-represented in the final dataset.
Work with algorithm teams to understand what data gaps hurt model performance and translate those into curation criteria.
Build validation and diagnostics that measure dataset quality - not just pipeline health, but whether the data is actually good for training.
Contribute to the core dataset SDK, converter, and 3D-geometry tooling (camera projection, calibration, coordinate transforms).

We are seeking a highly motivated AI Researcher to join our core Data Science team. In this role, you will be at the forefront of tackling complex, high-impact business challenges by leveraging cutting-edge GenAI technologies, advanced AI research, and classical machine learning and statistical modeling.
You will not just be building models; you will be driving research from ideation to production. We are looking for a true self-learner, someone who thrives in an environment that demands both a strong sense of ownership over your deliverables and deep collaboration. You will work closely with fellow researchers, product managers, and software developers to translate abstract business problems into scalable data-driven solutions.
If you are passionate about staying ahead of the AI curve, building agentic workflows, and proactively driving your research initiatives forward within a collaborative team structure, this is the perfect role for you.
What You Will Do:
End-to-End Research & Ownership: Lead targeted research projects from initial hypothesis through to production-ready solutions. Take strong ownership of your work's performance and partner with engineering to ensure successful, scalable integration
Applied AI Research & Integration: Lead research into LLMs, agentic systems, and modern AI methods, then design and build the workflows that bring them into our ecosystem.
LLM Evaluation & Optimization: Contribute to and establish rigorous evaluation frameworks for LLMs to ensure accuracy, safety, and business alignment in practical applications.
Cross-Functional Collaboration: Act as a bridge between data, engineering, and product. Work with Developers to ensure seamless integration of ML features into the core product.
Continuous Innovation: Act as an internal advocate for emerging AI research and methodologies. Continuously read, experiment, and implement state-of-the-art techniques to advance our research agenda and accelerate research velocity.

Were looking for a Director of Engineering Security to join our organization and lead multiple engineering teams building the next generation of secuirty products.
This is a hands-on engineering leadership role for someone who is a builder at heart, an experienced software engineering lead who loves to code, understands the full product and technical lifecycle, and knows how to scale both people and technology.
Youll work closely with the VP of AI and leaders across Product, Engineering, and other functions to turn ambitious ideas into high-quality products, while building and mentoring strong engineering teams.
What Youll Do:
Lead and manage multiple engineering teams, including managing and mentoring Team Leads.
Set the technical direction and execution standards across the teams.
Stay close to the technology and remain hands-on when needed- this is not a purely managerial role.
Drive the full engineering lifecycle, from architecture and design through development, deployment, and production.
Build and scale SaaS products in a fast-moving environment.
Partner closely with Product and other departments to translate business needs into technical execution.
Make pragmatic tradeoffs between long-term architecture and short-term delivery.
Encourage a culture of ownership, speed, experimentation, and continuous improvement.
Motivate engineers by connecting their work to the broader product and business goals.
Collaborate closely with managers and stakeholders across the organization.
Resolve conflicts effectively and build strong, collaborative relationships across teams.
Help identify technical risks and long-term implications while maintaining a strong focus on delivering results now.

Required Data Engineering Manager
Data, Ramat Gan, Israel (Hybrid)
Description
We are one of the most popular and downloaded apps in the world. Working with us provides a unique opportunity to influence hundreds of millions of our users and to be part of the journey that makes us a super-app. Our mission is to make peoples lives easier by enabling meaningful connections, from precious moments with family and friends, through managing business relationships to pursuing their passions.
As an Engineering Manager in the data department, youll build and scale our data platform and data apps that powers our business insights. Youll design and implement robust pipelines to process billions of daily records, leveraging cutting-edge cloud technologies to transform data into actionable intelligence.
If youre passionate about data engineering and driving business growth through insights, wed love to hear from you!
Responsibilities:
Lead and grow Data Engineering and Machine Learning teams in a high-scale environment (tens of billions of events per day).
Own the design and evolution of a self-service data platform enabling internal teams to easily build, ship, and consume data products.
Architect and scale batch and streaming pipelines powering core business and ML use cases.
Drive production ML systems end-to-end (recommendation, ranking, prediction) with direct business KPI impact.
Ensure reliability, scalability, and observability of large-scale data and ML systems in production.

We are looking for a Software Engineer to help design and develop the core services and applications that drive our data capabilities. In this role, you will build high-quality, scalable software; contribute to the architecture of our next-generation data systems; and collaborate with research and product teams to turn cutting-edge ideas into production-ready features.
Responsibilities:
Design, develop, and maintain scalable backend services and components that power our data workflows, analytics, and product features.
Build high-quality internal tools and applications that improve engineering workflows and enable data-driven development across the organization.
Contribute to the architecture and evolution of our new data platform with a strong focus on clean design, testability, maintainability, and performance.
Collaborate closely with security researchers, analysts, and product managers to translate innovative cybersecurity concepts into reliable, production-ready software.
Apply engineering best practices across the stack - including code quality, testing, observability, versioning, and documentation - to ensure system robustness as we scale.