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ServiceNow is seeking a Senior Machine Learning Engineer to design, build, ship, and operate model-driven applications at scale. This role sits at the intersection of advanced optimization and AI-native development, where you'll build the engine behind ServiceNow's optimization products—algorithms and pipelines that solve sophisticated scheduling and resource-allocation problems for enterprise customers.
Key Responsibilities:
- Design and build optimization applications in production at scale, including API design, data ingestion, multiprocessing, observability, advanced algorithm development, performance benchmarking, and automated testing.
- Build automated evaluation systems and test frameworks that support confident feature delivery and model behavior validation.
- Specify requirements precisely and direct AI coding agents to implement solutions; decompose work into agent-sized tasks and review output for correctness and maintainability. Contribute to shaping how AI agents are leveraged across teams.
- Own quality, safety, and reliability in production: manage deployments, monitor service availability, implement alerting, and respond to customer issues proactively.
- Work as a forward-deployed engineer, collaborating directly with customers to understand business requirements, accelerate time-to-value, and influence product roadmap based on learnings.
- Partner with product managers, designers, conversation designers, and engineers to define success criteria, align on tradeoffs, and communicate capability and risk clearly.
The role expects both classical ML engineering skills (designing efficient algorithms from scratch) and modern AI-native capabilities (directing AI agents to implement solutions, then verifying correctness). You own the end-to-end correctness, performance, and robustness of what ships, regardless of whether a human or an agent wrote it.
Qualifications:
- 5+ years of related experience with a bachelor's degree in computer science, software engineering, or related technical field. Advanced degrees or certifications (especially in Operations Research) are a bonus.
- Strong software engineering fundamentals: OOP, data structures, algorithms, system design, concurrency, parallelism, APIs, data modeling, and testing.
- Python experience strongly preferred; Java, JavaScript, Kubernetes, or GraphQL experience is a bonus.
- Familiarity with ML/AI tools and platforms: MLflow, Weights & Biases, LoRA, PEFT, SageMaker, Vertex AI, AzureML, Kubeflow, Airflow, Prefect, TorchServe, Triton, vLLM, SHAP, LIME, pandas, Spark, SQL.
- Demonstrated experience with forward-deployed delivery and stakeholder communication; comfort working directly with customers under incomplete information and fixed timelines.
- Proven track record developing reliable and resilient mission-critical systems.
- AI-native approach: strong critical thinking and demonstrated integration of AI into engineering processes, SDLC, workflows, or decision-making.
- Domain expertise in Operations Research, Vehicle Routing Problems, Mathematical Optimization, or Field Service Management is a plus but not required.
