Machine Learning Engineer, ServiceNow
Agentic AI Harness & Quality, Moveworks
$139.7-216.5k
+ variable/incentive compensation
Office located in Mountain View, CA
Cloud-based digital workflows for enterprises
Posted today
Cloud-based digital workflows for enterprises
1001+ employees
Posted today
$139.7-216.5k
+ variable/incentive compensation
Office located in Mountain View, CA
1001+ employees
Company mission
To transform IT to revolutionize the enterprise.
Role
Who you are
- Drive to ship product improvements with production-quality, fully unit-tested code and rigorously-evaluated updates to models, prompts, or other tunable system components
- Ability to solve problems end-to-end with machine learning
- Solid grasp of model evaluation fundamentals, especially for text generation, text classification, and non-uniform sampling regimes
- Attention to detail and high standard of data quality for training and especially evaluation datasets
- Readiness to hit the ground running in a Mac development environment, programming in Python and/or Golang
- Knowledge of deep learning architectures and algorithms and leading large language models
- Desire to work at a startup pace in a medium-sized company with a high degree of ownership
- Drive to ship product improvements with production-grade code
- Strong appetite for continuous incremental wins and completing challenging projects fast
- High level of curiosity about engineering outside of immediate discipline and ongoing desire to learn and stay at the cutting edge of NLU & AI
What the job involves
- We are looking for an experienced software engineer with machine learning expertise to join us in expanding Moveworks agentic AI capabilities, enabling increasingly magical user experiences and improving Moveworks agentic and conversational AI capabilities platform-wide
- As a member of the Agentic AI Harness & Quality team, you will have all the tools of modern agentic AI engineering at your disposal, from best-in-class LLMs, multimodal foundation models, and hybrid vector databases to all the infrastructure needed to tune, evaluate, and serve both models and agents in production
- We are an eval-driven, data-centric team, and you will have the assistance of a world-class annotation team to build error-free, inclusive, and privacy-preserving datasets for tuning and evaluation
- You will also go beyond model training to achieve state-of-the-art AI performance in production, in every meaning of the word “performance”: not just accuracy and quality of outputs, but also latency, reliability, and capability of the end-to-end user-facing system as a whole
- Successful machine learning engineers on the team are motivated to design and maintain high-performing compound AI systems, with model training as one tool among many in the toolbox rather than the central responsibility of the job
- Our team indexes on increasing our ability to move fast, solving challenging product and engineering challenges, and pushing the envelope of agent reliability and value provided to customers
- Your work will impact our team’s core objective to build the highest-performing enterprise assistant platform the world has ever seen, in close collaboration with other functions within Moveworks and ServiceNow as a whole
- Apply software engineering, machine learning, and compound AI system engineering to create lasting value for all our customers
- Drive quality in agent behavior, both in terms of increasing utility for end users and decreasing misbehavior in the customer business environment
- Take on exciting and difficult challenges in AI harness engineering, such as agent cognitive architecture, eval benchmark creation, context engineering, generative UI, dynamic agent orchestration, multimodal I/O, multilingual support, conversational memory management, reasoning strategies, abstractive summarization, grounding and verifiability for generated text, deployment safety, and self-learning
- Read, discuss, and build on the latest ML/LLM research and open-source repositories and models
- Research and develop innovative, scalable and dynamic solutions to hard problems
- Use your knowledge of machine learning fundamentals and LLMs to make improvements to the agentic harness and its AI components, evaluate them with small scale experiments and productionize your solutions at scale
- Partner closely with web and chat platform engineering teams to turn harness investments into delightful user experiences that wow our customers
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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