We are seeking a Senior Security Engineer with a specialty in Detection and Incident Response to join our Security Engineering team. This role sits at the intersection of security operations and software engineering — you won't just investigate incidents, you'll build the systems that detect, contain, and prevent them. You will design and ship high-precision detections across cloud services and enterprise SaaS, develop automation that shortens response timelines, and mature the telemetry pipelines that make it all possible. Your ability to write production-quality code is just as important as your ability to triage an alert.

You will structure investigations, analyze root causes, and clearly communicate the significance of security incidents, their impact, and recommended remediation steps — but you'll also turn those findings into durable engineering improvements: better detections, tighter schemas, and smarter automation.

You will:

• Engineer, test, and deploy detection logic across cloud and enterprise environments, treating detections as software with version control, peer review, and measurable performance.
• Build and maintain incident response automation, runbooks, and tooling that reduce containment timelines without sacrificing developer velocity.
• Mature telemetry pipelines through improved schema design, normalization, enrichment, and quality checks that reduce false positives and increase signal fidelity.
• Perform digital incident investigations to identify and contain potential security breaches.
• Conduct digital forensics and malware analysis to understand attack vectors and adversary methodologies.
• Integrate alerting with messaging and ticketing systems to enable fast, traceable response workflows.
• Partner cross-functionally with IT, security, and engineering teams to harden identity and access patterns, close logging and forensics gaps, and implement maintainable guardrails that scale with the organization.
• Utilize threat intelligence platforms to improve hunting, detection, and response workflows.
• Clearly explain the significance and impact of incidents, providing actionable recommendations to both technical and non-technical stakeholders.

Ideally, you'd have:

• • 5+ years of experience in Detection Engineering, Incident Response, or Security Operations, with a strong emphasis on building and shipping security tooling and automation.
  • Proficiency in at least one programming language (e.g., Python, Go) and comfort writing production-grade code — not just scripts.
  • Hands-on experience designing or improving detection pipelines, SIEM content, and alerting workflows in cloud-native environments.
  • Practical experience with SIEM, EDR, and SOAR tools, with a preference for candidates who have built integrations or extended these platforms programmatically.
  • Strong understanding of modern cyber threats, common attack techniques, and adversary TTPs.
  • Familiarity with digital forensics tools and malware analysis techniques.
  • Experience with cloud-native environments (e.g., AWS, GCP, Azure) and the security telemetry those environments generate.
  • Exposure to threat intelligence platforms and integrating intel into detection and investigation workflows.
  • Strong communication skills, with the ability to translate complex security findings into clear business impact.
  • Relevant security certifications (e.g., GCIH, GCFA, GCIA, CISSP, GDSA) are a plus.

Software is eating the world, but AI is eating software. We live in unprecedented times – AI has the potential to exponentially augment human intelligence. Every person will have a personal tutor, coach, assistant, personal shopper, travel guide, and therapist throughout life. As the world adjusts to this new reality, leading platform companies are scrambling to build LLMs at billion scale, while large enterprises figure out how to add it to their products. To make them safe, aligned and actually useful, these models need human evaluation and reinforcement learning through human feedback (RLHF) during pre-training, fine-tuning, and production evaluations. This is the main innovation that’s enabled ChatGPT to get such a large headstart among competition.

At Scale, our products include the Generative AI Data Engine, SGP, Donovan, and others that power the most advanced LLMs and generative models in the world through world-class RLHF, human data generation, model evaluation, safety, and alignment. The data we are producing is some of the most important work for how humanity will interact with AI.

At the foundation of these products is the Platform Engineering team. As a Staff Software Engineer, you will define and drive both the architectural roadmap and implementation of core platforms and software systems. You will be responsible for providing high-level vision and driving adoption across the engineering org for orchestration, data abstraction, data pipelines, identity & access management, and underlying cloud infrastructure.  You’ll also get widespread exposure to the forefront of the AI race as Scale sees it in enterprises, startups, governments, and large tech companies.

Impact and Responsibilities:

• Architectural Vision: You will drive the design and implementation of foundational systems, acting as a bridge between high-level business goals and technical goals.
• Cross-Functional Leadership: You will collaborate with cross-functional teams to define and drive adoption of the next generation of features for our AI data infrastructure.
• Technical Ownership: You are responsible for proactively identifying and driving opportunities for organizational growth, driving improvements in programming practices, and upgrading the tools that define our development lifecycle.
• Technical Mentorship: You will serve as a subject matter expert, presenting technical information to stakeholders and providing the guidance to elevate the engineering culture across the company.

Ideally you’d have:

• 8+ years of full-time engineering experience, post-graduation with specialities in back-end systems.
• Extensive experience in software development and a deep understanding of distributed systems and public cloud platforms (AWS preferred).
• Demonstrated a track record of independent ownership and leadership across successful multi-team engineering projects
• Possess excellent communication and collaboration skills, and the ability to translate complex technical concepts to non-technical stakeholders.
• Experience working fluently with standard containerization & deployment technologies like Kubernetes, Terraform, Docker, etc.
• Experience with orchestration platforms, such as Temporal and AWS Step Functions.
• Experience with NoSQL document databases (MongoDB) and structured databases (Postgres).
• Strong knowledge of software engineering best practices and CI/CD tooling (CircleCI, ArgoCD).

Nice to haves:

• Experience with data warehouses (Snowflake, Firebolt) and data pipeline/ETL tools (Dagster, dbt).
• Experience scaling products at hyper-growth startups.
• Excitement to work with AI technologies.

Software is eating the world, but AI is eating software. We live in unprecedented times – AI has the potential to exponentially augment human intelligence. Every person will have a personal tutor, coach, assistant, personal shopper, travel guide, and therapist throughout life. As the world adjusts to this new reality, leading platform companies are scrambling to build LLMs at billion scale, while large enterprises figure out how to add it to their products. To make them safe, aligned and actually useful, these models need human evaluation and reinforcement learning through human feedback (RLHF) during pre-training, fine-tuning, and production evaluations. This is the main innovation that’s enabled ChatGPT to get such a large headstart among competition.

At Scale, our products include the Generative AI Data Engine, SGP, Donovan, and others that power the most advanced LLMs and generative models in the world through world-class RLHF, human data generation, model evaluation, safety, and alignment. The data we are producing is some of the most important work for how humanity will interact with AI.

At the foundation of these products is the Platform Engineering team. In this role, you will lead the design and development of core data storage, streaming, caching, and indexing platforms and underlying systems.  You’ll also get widespread exposure to the forefront of the AI race as Scale sees it in enterprises, startups, governments, and large tech companies.

You will:

• Drive the architecture, design, implementation, and reliability of our foundational data platforms and systems, working closely with stakeholders and internal customers to understand and refine requirements.
• Collaborate with cross-functional teams to define, design, and deliver new features.
• Proactively identify opportunities for, and driving improvements to, current programming practices, including process enhancements and tool upgrades.
• Present technical information to teams and stakeholders, providing guidance and insight on development processes and technologies.
• Provide technical leadership, including: upholding and upleveling engineering standards across the organization, mentoring junior engineers. 

Ideally you’d have:

• 8+ years of full-time engineering experience, post-graduation with specialties in back-end systems, specifically related to building large-scale data storage, streaming, and warehousing systems.
• Extensive experience in various database technologies (MongoDB, Postgres), streaming/processing solutions (Kinesis, Flink, Spark), indexing/caching (ElasticSearch, Redis), and various data query engines (Trino, Presto, Snowflake, etc.).
• Show a track record of mentoring and leading teams in successful projects.
• Possess excellent communication and collaboration skills, and the ability to translate complex technical concepts to non-technical stakeholders.
• Experience working fluently with standard containerization & deployment technologies like Kubernetes and various public cloud offerings.
• Extensive experience in software development and a deep understanding of distributed systems, cloud platforms and data systems.
• Experience driving cross functional collaboration and communication at an organizational or broader level. 

Nice to haves:

• Strong knowledge of software engineering best practices and CI/CD tooling (CircleCI).
• Experience with performance tuning and cost optimizations of cloud based data platforms. 
• Experience defining a data lifecycle strategy and designing/implementing tooling for data privacy (i.e. GDPR) needs.  
• Experience scaling products at hyper-growth startups.
• Excitement to work with AI technologies.