Why This Job is Featured on The SaaS Jobs
This Senior AI and ML Engineer role stands out in SaaS because it sits at the intersection of product engineering and applied AI delivery, with a clear mandate to move prototypes into reliable services used across many customers. The emphasis on distributed systems processing large graph datasets and real time event streams reflects the kind of multi tenant, high throughput workloads common in enterprise SaaS, where latency, resilience, and security are product features.
For a long term SaaS career, the role offers repeated exposure to the full lifecycle of AI enabled capabilities, from experimentation through operationalization, monitoring, and iteration in production. Experience designing microservices, working with Kubernetes based infrastructure, and troubleshooting performance at scale translates well across modern SaaS platforms, especially where ML features must meet enterprise expectations around robustness and safe handling of untrusted data.
This position is best suited to engineers who prefer ownership of systems that run continuously and who enjoy partnering across research, product, and platform functions to make new capabilities dependable. It also fits professionals who want to deepen expertise in production ML and backend architecture, rather than focusing primarily on model research or front end product work.
The section above is editorial commentary from The SaaS Jobs, provided to help SaaS professionals understand the role in a broader industry context.
Job Description
About the role:
We’re looking for an experienced Backend engineer to help drive and evolve our product, focusing on high-impact innovation projects as part of our Linea AI team. You will be working on cutting-edge technology and ideas and bringing AI prototypes to production. You will work closely with Labs team to operationalize and scale AI prototypes across many customers.
What you’ll do:
Design and scale distributed systems: Architect, build, and optimize highly scalable and fault-tolerant systems that process large graph datasets at enterprise scale, handling billions of events in real time from tens of thousands of endpoints with sub-second latency.
Solve complex scaling challenges: Tackle real-world performance and reliability problems through deep analysis, profiling, and systematic troubleshooting in high-throughput distributed environments.
Build with modern infrastructure: Develop and evolve a microservices-based architecture using technologies such as Go, Kubernetes, Docker, and Redis, in a continuously improving production stack.
Engineer secure-by-design software: Write hardened, security-first code capable of safely processing untrusted data, resisting real-world attack vectors, and operating reliably across large-scale, internet-facing systems.
Productionize AI systems: Partner with research and product teams to operationalize AI prototypes, turning experimental models into robust, scalable, and production-ready services.
Who you are:
Proven Backend Expertise: 6+ years of experience building and scaling backend systems, with strong proficiency in Python and/or Go, powering production applications used at scale.
Full-Stack Impact: Demonstrated track record of designing and delivering full-stack applications that are actively used in real-world, high-traffic environments.
Generative AI & ML at Scale: Hands-on production experience with large-scale foundational models and transformer-based architectures, including deploying, monitoring, and iterating on GenAI systems.
Agent & Workflow Systems: At least 1 year of experience building AI agents and orchestration workflows using frameworks such as LangChain, LangGraph, Genkit, or similar technologies.
ML Systems & Data Pipelines: Experience delivering machine learning systems at scale, including robust model evaluation, continuous quality improvement pipelines, and data processing using columnar databases and large messaging systems (e.g., Pub/Sub, Kafka).
Ownership & Innovation Mindset: Comfortable working in remote, fast-paced environments, with an entrepreneurial mindset-driven to solve complex, real-world problems through innovation and breakthrough technology.
Good to have:
Security & Product Domain Experience: Background building products in data security, DLP, or application security environments.
Cloud Data & AI Workflows: Experience with GCS and BigQuery, and familiarity with LangGraph or similar frameworks for AI agent orchestration.
LLM Evaluation & Quality Systems: Hands-on experience designing or operating LLM evaluation pipelines, including automated testing, monitoring, and continuous quality improvement.
Joining Cyberhaven is a chance to revolutionize data security. Traditional tools fall short, but we’ve reimagined protection with AI-enabled data lineage that analyzes billions of workflows to understand data, detect risk, and stop threats. Backed by $250M from leading investors like Khosla and Redpoint, our team includes leaders who built industry-defining technologies at CrowdStrike, Palo Alto Networks, Meta, Google, and more. This role lets you shape the future of data security, alongside experts driven to help customers protect their most valuable information.
Cyberhaven is committed to creating a diverse environment and is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, or veteran status.
