Senior Machine Learning Engineer

NetSPI® pioneered Penetration Testing as a Service (PTaaS) and leads the industry in modern pentesting. Combining world-class security professionals with AI and automation, NetSPI delivers clarity, speed, and scale across 50+ pentest types, attack surface management, and vulnerability prioritization. The NetSPI platform streamlines workflows and accelerates remediation, enabling our experts to focus on deep dive testing that uncovers vulnerabilities others miss. Trusted by the top 10 U.S. banks and Fortune 500 companies worldwide, NetSPI has been driving security innovation since 2001.  

NetSPI is on an exciting growth journey as we disrupt and improve the proactive security market. We are looking for individuals with a collaborative, innovative, and customer-first mindset to join our team. Learn more about our award-winning workplace culture and get to know our A-Team at www.netspi.com/careers.  

We are seeking a Senior Machine Learning Engineer to design, build, and deploy scalable ML and LLM-powered systems in production. This role emphasizes engineering excellence over pure research, with a strong focus on cloud-native architectures, model reliability, and real-world performance. You will work on advanced ML systems, including agentic and LLM-based workflows, that support NetSPI’s security platforms. 

Responsibilities:

• Design, build, and deploy production-grade machine learning and LLM-drive systems 
• Train, validate, fine-tune, and evaluate ML and 

  large language models

  using sound statistical practices 
• Develop end-to-end ML pipelines covering data ingestion, preprocessing, deployment, monitoring, and retraining 
• Build and optimize 

  agentic systems

  , RAG pipelines, and LLM orchestration workflows 
• Optimize models for inference latency, scalability, and cost efficiency in AWS environments 
• Collaborate with product, security, and engineering teams to integrate ML into core platform capabilities 
• Uphold high engineering standards through clean code, testing, documentation, and code reviews 
• Contribute to the evolution of ML platform reliability, observability, and developer experience 

Our General Tech Stack:

• Languages:

  Python (primary), Java, Go 

• ML & AI:

  PyTorch, TensorFlow, scikit-learn, LLM fine-tuning frameworks 

• Infrastructure:

  AWS (ECS, Fargate, Lambda, S3, RDS Aurora, SageMaker) 

• Data & Messaging:

  PostgreSQL, Redis, Kafka 

• MLOps & Platform:

  Docker, Terraform, GitHub Actions, CI/CD, model monitoring 

• APIs & Services:

  FastAPI, Flask, gRPC 

• Orchestration:

   Temporal workflows 

Requirements:

• Fluency in English (written and spoken) 

• 5+ years of professional ML experience

  , including production deployments 
• Strong background in 

  machine learning, statistics, and model evaluation

• Demonstrated experience training, validating, and 

  fine-tuning LLMs

• Hands-on experience with 

  agentic systems and modern NLP architectures

• Strong Python engineering skills and experience with ML frameworks 
• Proven experience building ML systems on 

  AWS-based cloud architectures

• Solid software engineering fundamentals (data structures, algorithms, system design) 
• Proficiency in version control (Git), testing frameworks (pytest/unittest), and CI/CD pipelines 
• Familiarity with cloud platforms (AWS, GCP, Azure) and containerization (Docker, Kubernetes). 
• Minimum 

  Bachelor’s degree

  in a relevant technical field

Preferred:

• Graduate degree in a relevant field such as Computer Science, Data Science, or Applied Mathematics 
• Exposure to MLOps or ML infrastructure tooling (e.g. MLflow, Kubeflow, Airflow, BentoML, SageMaker). 
• Familiarity with feature stores, data versioning, and model monitoring tools. 
• Experience optimizing models for edge or low-latency deployment environments. 
• Contributions to open-source ML or data tooling projects. 

What We Value:

• Pragmatism and an action-oriented mindset, favoring delivery and measurable results over theoretical perfection. 
• Low-ego, highly collaborative mindset with a strong bias toward shared ownership and team success over individual credit. 
• Strong architectural and algorithmic thinking applied to real-world systems. 
• Commitment to writing production-grade, performant, and maintainable code. 
• Passion for continuous learning, experimentation, and improving team standards. 

We are an equal employment opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veteran status or any other characteristic protected by law.