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We are hiring a hands-on AI expert to shape our intelligent platform, organizing complex real estate data into actionable insights and predictive models that power smarter investment decisions.

Responsibilities
AI/ML Architecture: Design and implement scalable AI systems using models such as LLMs (e.g., OpenAI, Anthropic) and computer vision (e.g., YOLO, CLIP).
MCP Integration: Architect and orchestrate model context flows using MCP or similar frameworks to deliver personalized, context-aware experiences
FinTech Platform Integration: Embed intelligent features into our backend and frontend systems, ensuring seamless UX and robust performance
Hands-On Development: Write production-grade code across Python, TypeScript, and Node.js. Own key modules and guide technical decisions
Team Leadership: Mentor a few developers, set engineering standards, and foster a culture of experimentation and excellence
Data Strategy: Collaborate with product and data teams to define training datasets, feedback loops, and model evaluation pipelines.

Alice is a leading platform for Trust & Safety teams worldwide, combining cutting-edge AI with world-class expertise to protect users from the widest spectrum of online harms and keep customers two steps ahead of bad actors.
We are looking for a Senior GenAI Data Scientist with strong hands-on experience in GenAI red teaming, adversarial testing, machine learning, and large-scale data analysis.
You will develop the data science and ML capabilities behind an automated GenAI red-teaming platform. You will analyze and cluster adversarial prompts, identify emerging attack patterns, build models for automated prompt generation and mutation, and develop data-driven methods for prioritizing and evaluating security risks across different AI models.
We are looking for a hands-on technologist with deep expertise in data science: clustering, scaling, machine learning, and statistical analysis.
Key Responsibilities
Build agents that act as data scientists at scale, reasoning, planning, and executing multi-step workflows to generate and analyze adversarial attacks.
Manage and analyze prompt and response data from multiple sources (red-team campaigns, model evaluations, synthetic attacks); clean, curate, normalize, and label it for analysis.
Analyze large volumes of structured and unstructured data to uncover trends, clusters, and anomalies, such as attack families, jailbreak techniques, and previously unseen patterns.
Develop ML models and predictive algorithms to automate red-teaming.
Build evaluation and monitoring pipelines to measure attack success and track model behavior across models and providers.
Use statistical techniques and experiments to validate findings and ensure accuracy and reproducibility.
Take research to production: build reliable, scalable systems optimized for cost and latency, with solid engineering practices (testing, CI/CD, telemetry).

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 a leading platform for Trust & Safety teams worldwide, leveraging cutting‑edge AI and world‑class expertise to protect users from the widest spectrum of online harms. By continuously collecting and analyzing data, our solutions ensure customers stay two steps ahead of bad actors in an ever‑changing threat landscape.
Were seeking an outstanding Backend Engineer with a Generative AI orientation to join our research team. We are building a new AI Platform from scratch to standardize AI development methods, ensure excellence and efficiency, and deliver robust, scalable, and compliant AI capabilities. This platform will handle GenAI models deployment, prompt management, data filtering, logging, and monitoring, centrally, so individuals can focus on their own business-specific logic.
Key Responsibilities
Build and maintain CI/CD pipelines that support fast, reliable integration and deployment of GenAI systems across complex environments, including orchestrating multiple models/agents and MCP clients/servers.
Develop, deploy and integrate AI solutions and agentic frameworks, creating both command‑line tools and interactive notebooks that support our teams research and workflow automation needs.
Build and manage data pipelines and databases (SQL) for large‑scale data handling, automating data collection and curation (e.g., adversarial prompts for model red-teaming) to support AI training and evaluation.
Integrate research into production by implementing the latest generative AI advancements (including white‑box LLM development), integrating different models and agents, and developing evaluation frameworks (e.g., agentic reasoning tests) as part of our automated workflows.
Integration & Orchestration: Integrate dockerized environments (e.g., Harbor format) into internal training frameworks, optimizing reset/statefulness semantics, concurrency, and throughput ceilings
Deploy GenAI models from repositories such as HuggingFace onto our AWS platform, connect them to data pipelines, and automate end‑to‑end processes for prompt generation, labeling, and response evaluation.

We're looking for an exceptional, hands-on leader-engineer to join our core engineering team as a true force multiplier and technological anchor for the company. You'll have the flexibility to drive high-impact initiatives from conception to production, own complex technical and AI domains end-to-end, or guide and level up a focused delivery team, operating with a high degree of autonomy in an ambiguous, fast-moving environment.
What You'll Do:
Drive strategic initiatives: lead complex technical projects, AI feature developments, and core architectural components with an ownership mindset.
Architect for AI & data at scale: design robust, scalable infrastructure and pipelines capable of handling massive volumes of cloud infrastructure data.
Bridge product, AI & tech: collaborate closely with Product Managers and data specialists to translate business roadmaps into high-performance technical systems.
Promote engineering excellence: establish development guidelines, lead design and architecture reviews, and raise the bar for code quality across teams.
Mentor & guide: act as a technical mentor to fellow engineers through hands-on collaboration, deep code reviews, and architectural consultation.

We're looking for an AI researcher to take an integral part in research into how these models behave, how they should be evaluated, and how they can be deployed and used more efficiently across real engineering workloads.
This is a deeply technical research role with direct product impact.
You'll study frontier and open-source models, develop evaluation frameworks, investigate inference and model optimization techniques, explore local deployment strategies, and help answer questions such as:
Which model should handle a particular task?
How much capability do we lose when we quantize it?
Can a smaller local model replace a frontier API for a specific workload?
How should models be routed across latency, cost, privacy, and quality constraints?
How do we measure whether one model is actually better than another for agentic software engineering?
Your work will directly shape model strategy and the intelligence behind our AI infrastructure products.

we are seeking to hire a Technical Fraud Threat Analyst to join the Fraud team. In this role, you will research, evaluate, and analyze fraud detection mechanisms, abuse vectors, and platform vulnerabilities across a wide range of online services.
You will generate strategic insights from diverse intelligence sources, conduct OSINT investigations, and identify emerging fraud tactics and behavioral vulnerabilities. In addition to research and analysis, you will leverage technical tools and infrastructure to support investigations, simulate real-world abuse scenarios, and enable scalable fraud research. Your work will culminate in actionable intelligence and mitigation recommendations for clients.
Key Responsibilities:
Conduct investigations and identify fraud methods across web and mobile platforms (social media, forums, apps, and the dark web).
Design and execute fraud research methodologies, including technical validation of abuse scenarios.
Build, maintain, and operate research environments and technical tooling that support fraud investigations and red team activities.
Analyze large datasets, telemetry, and technical artifacts to uncover patterns and deliver actionable insights.
Develop or leverage automation to improve research workflows and increase operational efficiency.
Monitor third-party websites and channels for malicious activities and fraud risks.
Prepare detailed reports with findings, insights, and actionable recommendations.

we are hiring Intermediate Malware Researchers to analyze and review malware applications. Candidates should have a background in cybersecurity and software development.
The role includes training in malware analysis and detection and provides exposure to various malware threats and techniques. Researchers will combine technical and intelligence findings to reach accurate verdicts and assist in the bulk removal of malicious applications. The position requires adaptability and proactive identification of emerging malware trends.
We are seeking candidates who are willing to learn, can perform effectively under pressure, and maintain high professional standards.

we are seeking to hire a Fraud Threat Analyst to join the Fraud team. In this role, you will be responsible for researching, evaluating, and analyzing fraud detection mechanisms and vulnerabilities across various platforms and solutions.
You will generate strategic insights from diverse intelligence sources, conduct OSINT investigations to identify fraud risks, and monitor online platforms to detect malicious activities. You will also focus on uncovering fraud tactics, behavioral vulnerabilities, and process bypasses while delivering actionable mitigation recommendations to clients.
Key Responsibilities:
Conduct investigations and identify fraud methods across web and mobile platforms (social media, forums, apps, and the darknet).
Analyze large datasets to uncover patterns and deliver actionable insights.
Monitor third-party websites and channels for malicious activities and fraud risks.
Prepare detailed reports with findings, insights, and actionable recommendations.

we are seeking an experienced Researcher to join our team dedicated to Android malware research. This role presents an exciting opportunity to conduct comprehensive malware analysis, detect emerging threats, and contribute to our understanding of the Android threat landscape.
The ideal candidate demonstrates a positive, proactive attitude and excels as a reliable team player with strong technical skills in Android malware analysis.
Key Responsibilities:
Conduct in-depth Android malware research and reverse engineering
Detect and document emerging Android malware trends and attack vectors
Analyze Android applications for malicious behavior
Write detection rules and develop automation processes for Android malware identification
Collaborate with team members and share knowledge across departments
Perform static and dynamic analysis of Android malware samples
Document findings and contribute to threat intelligence reports.

we are looking for a Senior Software Engineer - Endpoint.
What You'll Do:
Own the installation and enrollment mechanics, the CLI flows, MDM scripts, and platform-specific packaging (Windows msi/exe, macOS pkg, Linux deb/rpm) that put TokenShift on developer machines at fleet scale.
Instrument and monitor endpoint behavior through the telemetry pipeline, using signals to improve coverage and catch regressions.
Build and maintain a robust lab enabling automatic research and development of new capabilities and platforms by AI agents.
Build and maintain agent harnesses, the per-agent adapters that hook into Claude Code, Cursor, Copilot CLI, Codex, and future agents to route network and shell commands through the TokenShift proxy.
Develop and extend compression rules and strategies for new command types (git, build tools, infra CLIs, search, and more), ensuring token reduction never comes at the cost of semantic accuracy.
Maintain correctness and resilience of the proxy layer, compression must be reversible on demand, hooks must never block a developer's command, and failures must be silent and transparent.
Collaborate with the research team to translate newly supported agent architectures into production-grade harnesses.

We're looking for a Senior Product Manager to own TokenShift, PointFive's product for finding and cutting wasted AI token spend. This is a chance to define product strategy for a brand-new category, working hands-on with R&D on how AI agents and LLM models actually run, get monitored, and get paid for in production. You'll take TokenShift from early adoption to a core pillar of PointFive's platform, alongside our flagship DeepWaste product.
What You'll Do:
Own the product strategy and roadmap for TokenShift, from discovery through GA and beyond.
Translate a deep technical understanding of how AI agents and LLM workloads run, inference calls, endpoints, agent harnesses and orchestration, into requirements R&D can build against.
Partner with R&D, design, and data science to scope, prioritize, and ship features that surface and reduce AI token waste.
Work directly with enterprise customers and prospects to understand how they deploy AI agents in production and where their cost and visibility gaps are.
Define and track success metrics, adoption, cost savings, detection accuracy, and use them to drive roadmap decisions.
Collaborate with go-to-market teams on positioning, messaging, and launch plans for new capabilities.
Stay current on the landscape: coding agents and harnesses, LLM gateways and routers, AI observability, and endpoint management.
Represent the voice of the customer internally, balancing technical depth with business impact.