Staff Machine Learning Engineer, ServiceNow
Agentic AI Systems, Moveworks
Office located in 1400 Terra Bella Avenue, 94043 Mountain View, CA
Cloud-based digital workflows for enterprises
Job no longer available
Cloud-based digital workflows for enterprises
1001+ employees
Job no longer available
Office located in 1400 Terra Bella Avenue, 94043 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 NLU (natural language understanding) and agentic AI capabilities, enabling increasingly magical user experiences and improving Moveworks generative and conversational AI capabilities platform-wide
- As a member of the NLU team, you will have all the tools of modern NLP and NLG at your disposal, from best-in-class LLMs, multimodal foundation models, and hybrid vector databases to all the infrastructure needed to fine-tune, evaluate, and serve your own models in production
- We are a 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 model training 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 just as motivated to design and evolve great compound AI systems and their components as they are to train great models
- Our team indexes on increasing our ability to move fast, solving challenging product and engineering challenges, and pushing the envelope of value provided to customers
- Your work will impact our team’s core objectives to understand every enterprise issue and build the most reliable copilot the world has ever seen, in deep collaboration with other functions within Moveworks
- Apply software engineering, machine learning, and compound AI system engineering to create lasting value for all our customers
- Take on exciting and difficult challenges in conversational agent domains, such as agent cognitive architecture iteration, multimodal agents, multilingual agents, conversational memory management, reasoning strategies (eg Tree of Thoughts / Graph of Thoughts), fine-tuning LLMs for tool use and enterprise reasoning (including preference alignment with RLHF/RLAIF/DPO), agent evaluation, active learning of exemplars for few-shot text classification, abstractive summarization, and grounding & verifiability for generated text
- Push the envelope of Moveworks commitments to responsible AI, expanding our infrastructure for ensuring models work equally well for all people, red-teaming models to ensure they behave safely and as intended, and keeping our ML at the cutting edge of data privacy and security
- Use your knowledge of machine learning fundamentals and LLMs to design new algorithms and architectures, evaluate them with small scale experiments and productionize your solutions at scale
- Research and develop innovative, scalable and dynamic solutions to hard problems
- Use the latest advances in machine learning and LLMs to enhance our products and create delightful user experiences
- Spend time weekly reading, discussing, and potentially building models out of the latest ML research and open-source code
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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