ML Engineer - Linguistics & Speech at ServiceNow
4h ago⚠️ Before applying, please carefully review all the job requirements
- Location:
- United States (AMS - North America and Canada Region)
- Work Arrangement:
- Remote
- Experience:
- Experience in ML
- Job Type:
- Full-time
- Company:
- ServiceNow
- Salary:
- $161,300 - $274,200 base pay per year (plus equity, incentive compensation, and benefits)
- Posted:
- 2026-10-16
- Work Arrangement:
- Flexible or Remote
Job Overview
- ServiceNow is recruiting for the role of SEO TITLE:. The role is remote and is classified as full-time. The salary mentioned in the listing is $161,300 - $274,200 base pay per year (plus equity, incentive compensation, and benefits). The requirements point towards Python, Artificial Intelligence and LLMs. The experience level listed is 4+ years of industry or applied research experience (Master's or PhD required). For general context: Artificial intelligence is increasingly part of everyday software products. Large language models power many of today's AI assistants. Read the details below carefully and use the application link on this page if the role matches your profile.
Key Responsibilities
- ★ Design, build, and deploy core Voice Platform applications, including Speech Recognition, Speech Generation, and Conversation Management systems.
- ★ Manage end-to-end multi-language support, maintaining locale-specific normalization rules, pronunciation dictionaries, and language instructions.
- ★ Analyze complex acoustic and linguistic failures in live production calls, trace root causes across components, and drive engineering fixes.
- ★ Partner with engineers and product managers to map language problem spaces, translate customer needs into technical specifications, and write feature code.
- ★ Formulate quantitative experiments and evaluations to inform technology choices, model selection, and vendor decisions for STT and TTS engines.
- ★ Build automated tooling and scripts using Python and AI developer tools to analyze large-scale acoustic and linguistic dataset issues.
- ★ Mentor junior team members and foster cross-disciplinary knowledge sharing across Voice AI engineering and linguistics.
Required Skills and Qualifications
- ★ Master's or PhD degree in Linguistics, Cognitive Science, or a closely related field.
- ★ Specialization in applied or interdisciplinary subfields such as phonetics, acoustics, psycholinguistics, or cognitive science.
- ★ Minimum of 4 years of experience conducting quantitative research projects and large-scale experiment design in an industry or applied research setting.
- ★ Hands-on engineering experience designing and building Voice AI applications.
- ★ Practical development proficiency in Python or equivalent programming languages.
- ★ In-depth understanding of multi-language structures, phonology, and grammar (fluency in languages other than English is advantageous).
- ★ Demonstrated capability to explain complex linguistic analysis to engineers, product managers, and enterprise clients.
- ★ Critical thinking regarding AI adoption, with hands-on experience using AI development tools such as Claude Code, Cursor, or Windsurf.
What These Requirements Mean in Practice
- ★ Python: Writing readable scripts and backend or data code using Python's standard library and common packages.
- ★ Artificial Intelligence: A general understanding of how AI systems learn from data, where they are useful, and where they can be unreliable.
- ★ LLMs: Understanding how large language models generate text from prompts, and why their output still needs to be checked.
- ★ AI tools: Using AI assistants to speed up everyday work while still reviewing the result yourself.
- ★ Teamwork: working through shared code, reviews and task boards without blocking other people.
Skills to Brush Up Before Applying
- ★ Python: core data structures, functions and modules, virtual environments and pip
- ★ Artificial Intelligence: supervised versus unsupervised learning, common real-world use cases, limitations and bias
- ★ LLMs: prompting basics, context limits and hallucinations, using an LLM API
- ★ AI tools: writing clear prompts, reviewing generated code, knowing when not to trust the output
Who May Be a Good Fit for This Role?
- This role may be suitable for candidates who match the skills and experience described in the requirements. Those who are comfortable with Python, Artificial Intelligence and LLMs and enjoy building practical projects may find the requirements a good match. A remote arrangement may suit people who work well independently and communicate clearly in writing. This is editorial guidance only, and the employer makes the final decision on suitability.
Preferred Qualifications
- ★ Demonstrated experience evaluating speech recognition (STT) and speech synthesis (TTS) systems.
- ★ Hands-on experience developing software applications using Large Language Models (LLMs) and prompt engineering strategies.
What The Company Offers
- ★ Annual base salary guideline of $161,300 to $274,200 (varying by qualifications, competencies, and geographic location).
- ★ Equity compensation eligibility (where applicable) plus variable incentive compensation.
- ★ Comprehensive health plans with Flexible Spending Accounts (FSA).
- ★ 401(k) retirement plan with company matching contributions.
- ★ Employee Stock Purchase Plan (ESPP) and corporate donation matching.
- ★ Flexible time away policy and paid family leave programs. This role may be suitable for computational linguists, speech scientists, and Machine Learning Engineers who hold an advanced degree (Master's or PhD) in Linguistics or Cognitive Science. Candidates with over four years of applied research or industry experience in phonetics, acoustics, and Voice AI application development will find this position well aligned with their background. It is particularly well-suited for Python developers who enjoy combining linguistic principles with software engineering to diagnose live call failures, evaluate speech-to-text models, and build multi-language voice platforms within a flexible or remote work environment. Applicants may want to highlight advanced research accomplishments, doctoral dissertations, or industry projects involving speech-to-text (STT), text-to-speech (TTS), phonetics, or computational linguistics in their CV. Demonstrating practical Python development experience and detailing any work with speech evaluation metrics, prompt engineering, or LLM-backed applications will illustrate technical versatility. In your application materials, clearly articulate your experience conducting quantitative research and diagnosing speech model failures in production environments. Furthermore, highlighting familiarity with modern AI coding assistants like Cursor, Windsurf, or Claude Code can help reflect alignment with ServiceNow's engineering workflows.
Application Tips
- Applicants may want to highlight relevant experience with Python, Artificial Intelligence and LLMs in their CV. If you have used AI tools or built small projects with language models, briefly describe what you built and how you checked the results. A short, role-specific summary at the top of your CV usually reads better than a generic profile. These are general tips, not requirements stated by the employer.
Prepare for Your Interview
- These suggested questions can help you prepare for an interview by focusing on the skills, responsibilities, and technical areas relevant to this role. They are for preparation only and are not confirmed employer questions.
- Q: How do decorators and generators work in Python?
- Q: How would you explain the difference between traditional programming and machine learning to a non-technical person?
- Q: How would you design a prompt and validate the output when using an LLM inside an application?
- Q: Which AI tools do you use in your development workflow, and how do you verify their output before relying on it?
- Q: How do lists, tuples, sets, and dictionaries differ in Python, and when would you use each?
- Q: Where could AI add value to a typical web application, and what risks should you plan for?
- Q: How would you explain what a large language model is, and where can its answers be unreliable?
- Q: How do you stay organised and communicate clearly when working remotely?
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