A technical role focused on building and advancing AI-native agentic harnesses, multi-agent workflows, and learning systems for professional services and legal firm platforms.
Compensation
Base salary: $250,000 to $400,000 per year
Responsibilities
Advance our multiplayer agentic harness so humans, agents, firm systems, and clients can collaborate on shared work with the right context, permissions, and accountability.
Design multi-agent workflows using orchestration and actor models so agents can divide work, coordinate with people and other agents, and recover when plans change.
Build shared task and state management so work moves reliably between agents, attorneys, clients, and firm systems without losing context or ownership.
Make agents more effective across software through computer use and deep integrations with the systems a professional services firm depends on.
Turn expert interactions into learning signals by capturing how people delegate, correct, and review work, along with the context behind their decisions.
Investigate and analyze complex patterns of abuse on Google Search, utilizing data-driven insights to develop countermeasures and enhance platform security.
Investigate and analyze complex patterns of abuse on Google Search, utilizing data-driven insights to develop countermeasures and enhance platform security.
Analyze large datasets to identify trends, patterns, and anomalies that may indicate abuse or quality issues within Google Search.
Translate complex technical analyses into insights for both technical and non-technical stakeholders.
Produce intelligence reports on emerging attack vectors, system vulnerabilities, and adversary tactics specifically aiming Large Language Models (LLMs) architectures.
Improve tools and automated systems through data analysis, technical expertise, and present to stakeholders. Analyze and solve complex problems using data and statistical methods.
Requirements
Bachelor's degree in a quantitative field (e.g., Statistics, Econometrics, Computer Science, Mathematics, or Engineering)
Nice to have: Master's degree in a quantitative field
Security Engineer focused on protecting cloud infrastructure, ML systems, and product surfaces in a frontier AI research company.
Compensation
We offer a base salary of $300,000–$400,000 USD and a meaningful equity grant, depending on experience and background, along with competitive benefits.
Responsibilities
Design and implement measures to protect against external threats
Design and implement protective measures around data transfer, external attacks, secret management, identity verification
Architect systems that protect sensitive assets including model weights, customer data, and training datasets
Build secure sandboxed execution environments for AI-generated code and agentic workflows
Develop frameworks for safely handling AI agent interactions with external systems and tools
Lead the technical goal, roadmap, and software architecture for hardware/software co-design, optimizing large-scale ML inference and training systems.
Design, develop, and oversee simulation frameworks, cycle-accurate performance models, and system analysis tools to evaluate architectural trade-offs.
Architect scalable software solutions, kernels, and execution pipelines that bridge frontier ML model architectures with next-generation custom silicon accelerators.
Drive system-level performance optimization across distributed ML infrastructure, balancing compute, memory bandwidth, and inter-chip networking requirements.
Provide technical leadership and mentorship across hardware, compiler, and ML research teams to align on goals and transition co-design innovations into production.
Requirements
Bachelor’s degree in Computer Science, Computer Engineering, Electrical Engineering, or equivalent practical experience
Nice to have: Master’s degree or PhD in Engineering, Computer Science, or related field
Nice to have: technical leadership experience
Nice to have: multi-generational hardware/software co-design
As a Principal ML Engineer on Matchmaking, you’ll own the technical vision and execution for the models, objective functions, and algorithms that make those decisions, serving millions of players across global datacenters.
Compensation
Annual Salary Range $295,250—$345,040 USD
Responsibilities
Own the technical direction and ML strategy for matchmaking
Define and evolve the matchmaking objective function to balance player and developer utility with system efficiency
Build models that predict high-value actions and longer-term outcomes for each player-server match
Design and productionize candidate-server retrieval, ranking, and assignment algorithms
Lead other engineers by example and through technical direction as an individual contributor, mentor engineers
We’re looking for a motivated individual to design, implement, and refine efficient Large Language Models (LLMs) pipelines for scaled distributed training, focusing on reasoning algorithms that go beyond traditional LLMs.
Responsibilities
Implement new reasoning algorithms and models
Evaluate reasoning approaches, including latent space reasoning
Pre-train, fine-tune, and modify the State-of-the-Art LLMs
Optimizing and scaling LLM pipelines
Adjust frameworks and interfaces to accelerate machine learning development
Member of the Technical StaffNorm · New York City, US
Sr/Principal Product Manager, Marketplace Discovery and RecommendationsRoblox · San Mateo, US
AI Search Engineering Analyst, Safety Risk and PlatformGoogle · Mountain View, US
Member of Technical Staff, Security EngineerMirendil · San Francisco, US
AI Engineer - Reinforcement LearningLogical Intelligence · San Francisco, US