ML Software Engineer
Join Fortinet, a cybersecurity pioneer with over two decades of excellence, as we continue to shape the future of cybersecurity and redefine the intersection of networking and security. At Fortinet, our mission is to safeguard people, devices, and data everywhere.
We are looking for a strong Backend Software Engineer to bridge the gap between our Machine Learning research team and our enterprise production systems. You will act as the technical backbone for our ML Scientists - by advising, designing and implementing the production facing features. If you are a backend expert who wants to solve complex system architecture challenges and dive into the world of ML platforms & Agentic LLM pipelines, this is the role for you - An exciting role collaborating with ML science team, data/infra team and DevOps to drive real customer impact.
As a ML Engineer, you will:
Lead ML delivery: transforming research output (code, models, ideas) into robust, scalable, low-latency microservices in production
Help architect e2e solutions to real customer pains ranging from ingestion, integration, ETLs, DB design up to low-latency services
Design, build, and maintain automated workflows for ML models, including auto-trains, benchmarking, testing, performance gating, and production deployment.
Tackle complex backend challenges: optimizing API response times, managing database connectivity and concurrency at scale, balancing accuracy’s drive for complex questions with the business needs of fast responsiveness by making hard technical trade-offs between customer gains and business costs.
Design and optimize data pipelines and ETL processes, connecting our Snowflake data warehouse to our training environments.
Work within our existing ML infrastructure (Kubeflow, MLflow, KServe) to ensure smooth model lifecycles and performance monitoring.
Collaborate closely with ML Scientists, guiding them on software engineering best practices without slowing down their research.
Monitor and optimize production models for performance, cost efficiency, availability, and observability.
Must-Haves (The Core Requirements):
6+ years of backend software engineering experience designing, building, and maintaining large-scale, high-throughput production systems
Strong coding skills, Ability to write clean, maintainable code, OOP familiarity, package design, microservices etc.
Note: Work is in python, but strong engineers with deep Java/C# backgrounds who have some Python experience and are willing to transition fully are highly encouraged to apply.
Solid Database design & SQL skills, Deep understanding of SQL, experience working with relational and/or bigdata (columnar) databases, ORMs, and efficient query design.
API & Performant Design Proven experience - building robust systems, you understand how to handle concurrency, ETL tradeoffs, building fault-tolerant best effort data flows
Nice-to-Haves (Strong Advantages):
Previous experience in productionizing Machine Learning models or working closely with Data Science teams.
Familiarity with data warehouses (e.g., Snowflake) and pipeline orchestration.
Familiarity with containerized environments (Docker/Kubernetes) and model serving frameworks (KServe).
Why Join Us:
At Fortinet, we embrace diversity and inclusivity. We encourage applications from diverse backgrounds and identities. Explore our welcoming work environment designed for a rewarding career journey with an attractive Total Rewards package to support you with your overall health and financial well-being. Join us in bringing solutions that make a meaningful and lasting impact to our 660,000+ customers around the globe.
We will only notify shortlisted candidates.
Fortinet will not entertain any unsolicited resumes, please refrain from sending them to any Fortinet employees or Fortinet email aliases. Should any Agency submit any resumes to Fortinet, these resumes if considered, will be assumed to have been given by the Agency free of any related fees/charges.
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As a ML Engineer, you will:
Lead ML delivery: transforming research output (code, models, ideas) into robust, scalable, low-latencymicroservices in production
Help architect e2e solutions to real customer pains ranging from ingestion, integration, ETLs, DB design up to low-latency services
Design, build, and maintain automated workflows for ML models, including auto-trains, benchmarking, testing, performance gating, and production deployment.
Tackle complex backend challenges: optimizing API response times, managing database connectivity and concurrency at scale, balancing accuracy’s drive for complex questions with the business needs of fast responsiveness by making hard technical trade-offs between customer gains and business costs.
Design and optimize data pipelines and ETL processes, connecting our Snowflake data warehouse to our training environments.
Work within our existing ML infrastructure (Kubeflow, MLflow, KServe) to ensure smooth model lifecycles and performance monitoring.
Collaborate closely with ML Scientists, guiding them on software engineering best practices without slowing down their research.
Monitor and optimize production models for performance, cost efficiency, availability, and observability.
Must-Haves (The Core Requirements):
6+ years of backend software engineering experience designing, building, and maintaining large-scale, high-throughput production systems
Strong coding skills, Ability to write clean, maintainable code, OOP familiarity, package design, microservices etc.
Note: Work is in python, but strong engineers with deep Java/C# backgrounds who have some Python experience and are willing to transition fully are highly encouraged to apply.
Solid Database design & SQL skills, Deep understanding of SQL, experience working with relational and/or bigdata (columnar) databases, ORMs, and efficient query design.
API & Performant Design Proven experience - building robust systems, you understand how to handle concurrency, ETL tradeoffs, building fault-tolerant best effort data flows
Nice-to-Haves (Strong Advantages):
Previous experience in productionizing Machine Learning models or working closely with Data Science teams.
Familiarity with data warehouses (e.g., Snowflake) and pipeline orchestration.
Familiarity with containerized environments (Docker/Kubernetes) and model serving frameworks (KServe).