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About This Role
About SiTime
SiTime Corporation is the precision timing company. Our semiconductor MEMS programmable solutions offer a rich feature set that enables customers to differentiate their products with higher performance, smaller size, lower power and better reliability. With more than 3 billion devices shipped, SiTime is changing the timing industry. For more information, visit www.sitime.com. Job Summary
We are seeking a Head of AI Applications to define and execute the strategy for AI\-powered solutions across SiTime. This role will lead the development, deployment, and scaling of AI applications that drive measurable business outcomes across functions including Sales, Finance, Operations, HR, and Marketing. This is a hands\-on leadership role responsible for building a high\-performing team, delivering production\-grade AI solutions, and driving company\-wide adoption of AI as a trusted, everyday capability. It is not necessary to meet all job requirements to be a qualified candidate for the position. Responsibilities:* Set the AI applications strategy. Define and own the vision and roadmap for AI\-powered solutions, aligned to company and business\-function priorities.
- Build agentic AI across the business. Design and deliver agentic AI solutions for functions across the organization — Sales, Finance, Operations, HR, Marketing, and more — that automate workflows and augment teams.
- Drive AI adoption and enablement. Champion AI across the company: lead change management, training, and enablement so teams actually use AI in their day\-to\-day, and measure and grow adoption over time.
- Own the full\-stack AI solution. Architect end\-to\-end solutions with the right LLMs for each use case, and integrate them with core business systems and data sources (CRM, ERP, ticketing, knowledge bases, etc.).
- Ship production AI. Lead the design, development, evaluation, and deployment of LLM\- and agent\-driven applications — from prototype to scaled production.
- Partner cross\-functionally. Work closely with business leaders, IT, Product, and Engineering to identify high\-impact opportunities and turn them into shipped value.
- Partner with Business Applications. Collaborate closely with the Business Applications team to embed AI directly into core enterprise systems and workflows, ensuring solutions are integrated, supportable, and scalable.
- Own evaluation and quality. Establish rigorous evaluation, testing, and monitoring practices for model performance, accuracy, latency, and cost.
- Manage the model and platform stack. Make build\-vs\-buy decisions across foundation models, fine\-tuning, RAG, agents, and orchestration; balance capability, cost, and reliability.
- Stand up AI governance. Establish SiTime's AI governance framework — policies, review processes, an operating model (e.g., an AI council or center of excellence), and clear standards for how AI is evaluated, approved, and deployed.
- Drive responsible AI. Set standards and guardrails for safety, privacy, fairness, and governance, and ensure compliance with relevant policies and regulations.
- Be the internal expert. Keep the organization current on the fast\-moving AI landscape and advise leadership on where to invest.
Qualifications \& Requirements :* 5\+ years of experience building software or AI solutions, with 4\+ years in AI/ML and 2\+ years leading applied\-AI teams.
- A track record of building agentic AI solutions for business functions (e.g., Sales, Finance, Operations, HR) and shipping them to production at scale — not just prototypes.
- Hands\-on experience with the Microsoft Copilot ecosystem — M365 Copilot, Copilot Studio, and agent builder — to build, customize, and deploy agents across the organization.
- Demonstrated success driving AI adoption and enablement across an organization: change management, training, and measurable uptake.
- Experience architecting full\-stack AI solutions — selecting the right LLMs for each use case and integrating with core business systems (CRM, ERP, data platforms, etc.).
- Deep, current understanding of LLMs and generative AI, including prompting, retrieval\-augmented generation (RAG), fine\-tuning, agents, and evaluation methods.
- Strong engineering fundamentals: you can go deep with your team on architecture, integrations, and trade\-offs.
- Proven ability to hire, lead, and develop high\-performing technical teams.
- Excellent communication and influence skills; you can rally non\-technical teams around AI and explain complex concepts to executives and end users alike.
- A practical grasp of responsible AI — safety, privacy, bias, and governance.
- Experience establishing AI governance frameworks in an enterprise — policies, review and approval processes, and operating models (e.g., an AI council or center of excellence).
- Experience hiring, managing, and developing direct reports; proven ability to build and grow high\-performing AI teams.
- Experience presenting AI strategy, progress, and recommendations to senior leadership and C\-suite stakeholders.
- Hands\-on experience with agentic AI orchestration frameworks (e.g., LangChain, Semantic Kernel, AutoGen, or similar) for designing and deploying multi\-step agent workflows.
- Familiarity with AI governance and compliance frameworks such as the NIST AI Risk Management Framework (AI RMF) or EU AI Act, applied in an enterprise context.
- Demonstrated change management experience — driving adoption of new technology or ways of working across a broad, cross\-functional organization.
- Experience managing vendor and partner relationships for AI tooling, platforms, or services, including contract evaluation and strategic alignment.
Desired Characteristics \& Attributes:* Strategic and hands\-on. Comfortable setting vision at the leadership table and rolling up your sleeves to build alongside your team.
- Influences without authority. Wins buy\-in across functions and seniority levels through credibility, clarity, and trust rather than mandate.
- Business\-first mindset. Starts from the problem and the outcome, not the technology — relentlessly focused on measurable value.
- Bias for action. Moves fast, ships pragmatically, and iterates; favors progress over perfection in a fast\-moving field.
- Comfortable with ambiguity. Thrives amid rapid change and incomplete information, and brings structure to the unknown.
- Change agent and evangelist. Energizes others about AI, builds momentum, and makes adoption feel achievable rather than threatening.
- Strong communicator and translator. Bridges the gap between technical teams and business stakeholders effortlessly.
- Intellectually curious. A continuous learner who stays ahead of an evolving landscape and brings the best ideas back to the organization.
- High integrity and judgment. Champions responsible, trustworthy AI and earns the confidence of leadership and end users alike.
Compensation Range:
At SiTime, we believe great work deserves great rewards. We offer a comprehensive and highly competitive compensation package designed to attract top talent. In addition to base salary, this role is eligible for a quarterly bonus tied to the achievement of innovation goals—reflecting our commitment to recognizing meaningful impact. The annual base salary range for this role is $265,000\.00 – $380,000\.00\. We also offer equity grants, providing a meaningful opportunity to share in the company’s future growth and success. Benefits offered: 401k plan, health and wellness that includes medical, dental, vision, life, parental leave, legal services, and time off plans.
SiTime is an Equal Opportunity Employer. We treat each person fairly and we do not tolerate discrimination or harassment against anyone on the basis of any protected characteristics, including race, color, religion, national or ethnic origin, sex, sexual orientation, gender identity or expression, age, disability, pregnancy, political affiliation, protected veteran status, protected genetic information, or marital status or other characteristics protected by law. SiTime participates in the E\-Verify program.Learn More about SiTime: Review the Get to Know SiTime section of our career page to explore our culture, values, and what makes us unique.* Innovation on Top – Philosophies of Innovation with Rajesh Vashist
- Fabrication Knowledge – An Interview with Rajesh Vashist
- SiTime Corporation – YouTube
\#LI\-SITIME
Salary Context
This $265K-$380K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $181K across 1996 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At SiTime Corporation, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $178,940 based on 11,900 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $160,000. This role's midpoint ($322K) sits 80% above the category median. Disclosed range: $265K to $380K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
SiTime Corporation AI Hiring
SiTime Corporation has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Santa Clara, CA, US. Compensation range: $380K - $380K.
Location Context
Across all AI roles, 16% (613 positions) offer remote work, while 3,187 require on-site attendance. Top AI hiring metros: New York (2,448 roles, $210,000 median); San Francisco (1,990 roles, $253,000 median); Los Angeles (1,686 roles, $189,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (119) are outnumbered by mid-level (1,813) and senior (1,472) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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