Why This Job is Featured on The SaaS Jobs
This Machine Learning Engineer role sits at a core SaaS problem: turning high-volume operational telemetry into productized intelligence. Building agentic systems over heterogeneous log data aligns with where many SaaS platforms are heading—embedding LLM-driven reasoning directly into workflows that support reliability, security, and observability. The emphasis on context engineering, memory management, and evaluation suggests work that is closer to “AI as a feature” than isolated research.
From a SaaS career perspective, the role offers exposure to the full loop that matters in subscription software: data sourcing and quality, model behavior under real-world constraints, and the operational discipline required to keep AI capabilities reliable in production. Experience with golden datasets, interpretability, and continuous improvement maps well to adjacent SaaS domains like ML platform engineering, LLMOps, and product analytics, where measurable outcomes and maintainability tend to drive roadmap decisions.
This position is best suited to an engineer who prefers ownership across experimentation and deployment, and who is comfortable translating ambiguous product needs into testable systems. It will also fit someone motivated by cross-functional delivery—working with product, data, and infrastructure—rather than optimizing models in isolation.
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
The proliferation of machine log data has the potential to give organizations unprecedented real-time visibility into their infrastructure and operations. With this opportunity comes tremendous technical challenges around ingesting, managing, and understanding high-volume streams of heterogeneous data
As a Machine Learning Engineer, you’ll build the intelligence behind the next generation of agentic AI systems and related AI systems that reason over massive, heterogeneous log data. You’ll combine machine learning, prompt engineering, and rigorous evaluation to create autonomous AI agents that help organizations understand and act on their data in real time.
You’ll be part of a small, high-impact team shaping how AI agents understand complex machine data. This is an opportunity to work on cutting-edge LLM infrastructure and contribute to defining best practices in context engineering and AI observability.
Responsibilities
- Design, implement, and optimize agentic AI components, including context engineering, memory management, and prompts.
- Develop and maintain golden datasets by defining sourcing strategies, working with data vendors, and ensuring quality and representativeness at scale.
- Prototype and evaluate novel prompting strategies and reasoning chains for model reliability and interpretability.
- Collaborate cross-functionally with product, data, and infrastructure teams to deliver end-to-end AI-powered insights.
- Operate autonomously in a fast-paced, ambiguous environment - defining scope, setting milestones, and driving outcomes.
- Ensure reliability, performance, and observability of deployed agents through rigorous testing and continuous improvement.
- Maintain a strong bias for action—delivering incremental, well-tested improvements that directly enhance customer experience.
Required Qualifications
- B.Tech, M.Tech, or Ph.D. in Computer Science, Data Science, or a related field.
- 2-5 years of hands-on industry experience with demonstrable ownership and delivery.
- Strong understanding of machine learning fundamentals, data pipelines, and model evaluation.
- Proficiency in Python and ML/data libraries (NumPy, pandas, scikit-learn); familiarity with JVM languages is a plus.
- Working knowledge of LLM core concepts, prompt design, and agentic design patterns.
- Strong communication skills and a passion for shaping emerging AI paradigms.
Desired Qualifications
- Prior experience building and deploying AI agents or LLM applications in production.
- Familiarity with modern agentic AI frameworks (e.g., LangGraph, LangChain, CrewAI).
- Experience with ML infrastructure and tooling (PyTorch, MLflow, Airflow, Docker, AWS).
- Exposure to LLM Ops - infrastructure optimization, observability, latency, and cost monitoring.
About Us
Sumo Logic, Inc. helps make the digital world secure, fast, and reliable by unifying critical security and operational data through its Intelligent Operations Platform. Built to address the increasing complexity of modern cybersecurity and cloud operations challenges, we empower digital teams to move from reaction to readiness—combining agentic AI-powered SIEM and log analytics into a single platform to detect, investigate, and resolve modern challenges. Customers around the world rely on Sumo Logic for trusted insights to protect against security threats, ensure reliability, and gain powerful insights into their digital environments. For more information, visit www.sumologic.com.
Sumo Logic Privacy Policy. Employees will be responsible for complying with applicable federal privacy laws and regulations, as well as organizational policies related to data protection.
The expected annual base salary range for this position is $148,000 - $173,200. Compensation may vary based on a variety of factors, which include (but aren’t limited to) role level, skills and competencies, qualifications, knowledge, location, and experience. In addition to base pay, certain roles are eligible to participate in our bonus or commission plans, as well as our benefits offerings and equity awards.
Must be authorized to work in the United States at the time of hire and for the duration of employment. At this time, we are not able to offer new non-immigrant visa sponsorship for this position.
