Senior Machine Learning Engineer, ServiceNow
Cloud-based digital workflows for enterprises
Be an early applicant
Cloud-based digital workflows for enterprises
1001+ employees
Be an early applicant
1001+ employees
Company mission
To transform IT to revolutionize the enterprise.
Role
Who you are
- 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
- Software engineering fundamentals. Strong command of OOP, data structures, algorithms, system design, concurrency, parallelism, APIs, data modeling, and testing
- Experience with key Technologies: Experience with Python is strongly preferred; experience in Java, JavaScript, Kubernetes, or GraphQL is a bonus
- Python, MLflow, Weights & Biases, LoRA, PEFT, SageMaker, Vertex AI, AzureML, Kubeflow, Airflow, Prefect, TorchServe, Triton, vLLM, SHAP, LIME, pandas, Spark, SQL
- Forward deployed delivery and stakeholder communication. Comfort working directly with customers under incomplete information and fixed timelines, judgment about when a fast local solution is right and when to hold out for the durable one, and credibility communicating capability, limitation, and risk to non-engineers
- Experience with mission critical systems. Demonstrated experience developing reliable and resilient systems
- AI Native Approach. Strong critical thinking and a track record of integrating AI into engineering processes, SDLC, workflows, or decision making
- Domain expertise. Familiarity with Operations Research, Vehicle Routing Problems, Mathematical Optimization, Field Service Management is a plus but not required
What the job involves
- The Machine Learning Developer designs, builds, ships, and operates applications whose core behavior is model-driven rather than explicitly authored. The Developer builds the engine behind ServiceNow's optimization products — the algorithms and pipelines that turn sophisticated scheduling and resource-allocation problems into fast, reliable customer outcomes. The work is evolving — where "AI-driven" once meant applying ML techniques to optimization problems, it now also means using AI agents as part of the build process itself
- This role expects both: someone who can design an efficient algorithm from scratch, and someone who can specify a problem precisely enough to direct an AI agent to implement part of it, then verify the result is correct. The developer owns the correctness, performance, and robustness of what ships, regardless of whether a human or an agent wrote it
- Build Optimization applications. Design, build and operate applications built around advanced optimization models in production at scale. This includes API design, data ingestion, multiprocessing, observability, advanced algorithm development, performance benchmarking, and automated testing
- Build automated evaluation and test model behaviour. Design and operate the evaluation system that supports development and delivering features with confidence
- Specify precisely and direct AI coding agents. Convert requirements into testable specifications with explicit scope, constraints, and acceptance criteria; decompose work into agent-sized tasks; and review agent output for correctness and maintainability. Contribute to teams AI-harness helping shape the way agents are leveraged
- Own quality, safety, and reliability in production. Manage deployments, monitor service availability, implement alerting and respond to customer issues before they even know they have a problem
- Deliver as a forward deployed engineer. Work closely with customers when the work calls for it. Understanding business requirements and product capabilities to help customers get to value faster and use learnings to influence product roadmap
- Collaborate across product, design, and engineering. Partner with product managers, designers, conversation designers, and engineers to define success criteria, align on tradeoffs, and communicate capability and risk clearly
Our take
ServiceNow provides cloud-based solutions that define, structure, manage, and automate services for enterprise operations, transforming old, manual ways of working into modern digital workflows. The company was founded in 2004 with a vision to build a cloud-based platform that would enable regular people to route work effectively through the enterprise.
Much as Salesforce enables a company to manage its external clients by keeping a record of all contacts and interactions, ServiceNow promises an internal system to meet the needs of employees. It has succeeded in claiming half the market, setting itself apart from legacy IT service-management software players like BMC Software, Hewlett Packard Enterprise, Cherwell Software and CA Technologies.
The company's success comes from two product traits: simplicity and customisability. ServiceNow's IT tools don't require the IT department to set them up. Once running, they offer a single collection centre for requests, data points and checklists, all of which can in turn be analysed by algorithms to predict needs, flag concerns and measure efficiency.
The company has more than 7,700 customers, including 85% of the Fortune 500 list of the United States' largest corporations. ServiceNow now looks to increase its revenue threefold in the upcoming years and has entered into a 5-year partnership plan with Visa, beginning with the launch of a Visa-integrated dispute resolution solution for issuers. The company is prioritising organic growth over mergers and acquisitions, with a target revenue of $15B, something which its Visa partnership will no doubt aid in achieving.
Freddie
Company Specialist at Welcome to the Jungle
Insights
16% employee growth in 12 months
Company
Funding (last 2 of 6 rounds)
Mar 2012
$11m
GROWTH EQUITY VC
Feb 2012
$17.9m
GROWTH EQUITY VC
Total funding: $83.8m
Company benefits
- Generous family leave
- Matched donations
- Annual learning stipends
- Flexible PTO
- Competitive retirement plan
- Paid volunteer time
Company HQ
Santa Clara, CA
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