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About This Role
About SiTime
SiTime is the precision timing company. Timing is the heartbeat of all electronics, ensuring performance, resilience, and scalability. 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. SiTime is changing the timing industry. For more information, visit www.sitime.com.
Job Summary
SiTime is seeking a hands-on Sr. AI Engineer to design, build, deploy, and operate production-grade AI applications and agents across the enterprise. This role will translate high-value business problems into secure, scalable solutions that connect large language models with SiTime data, knowledge, and core systems. The engineer will work across the full solution lifecycle, from rapid prototyping through evaluation, deployment, monitoring, and continuous improvement, with a strong focus on measurable business value, responsible AI, and reusable engineering patterns. It is not necessary to meet all job requirements to be a qualified candidate for the position.
Responsibilities
- Build production AI applications. Design and implement LLM-powered applications and agentic workflows that move from prototype to reliable production use.
- Develop enterprise agents and integrations. Connect AI solutions securely to business systems, data platforms, APIs, knowledge bases, and workflow tools using approved integration patterns.
- Implement retrieval and knowledge solutions. Build retrieval-augmented generation (RAG) pipelines, including ingestion, chunking, indexing, metadata, access controls, retrieval, and grounding.
- Engineer for quality and trust. Create evaluation datasets and automated tests for accuracy, completeness, safety, latency, reliability, and cost; add human review where appropriate.
- Own deployment and operations. Establish CI/CD, observability, prompt and model versioning, incident response, and ongoing performance and cost monitoring for AI services.
- Build reusable platform components. Develop shared services and patterns for model access, orchestration, secure connectors, memory, tool use, auditability, and policy enforcement.
- Apply responsible AI and security controls. Protect confidential information and intellectual property through least privilege access, data handling controls, logging, red-teaming, and adherence to SiTime policies.
- Partner with business and technology teams. Work with product owners, business functions, Business Applications, Data, Security, and IT to define outcomes, prioritize use cases, and deliver solutions that fit existing workflows.
- Measure business value. Instrument adoption and outcome metrics, quantify time, quality, or cycle-time improvements, and iterate based on evidence.
- Advance engineering practices. Conduct design and code reviews, document architectures and runbooks, mentor engineers, and stay current on models, frameworks, and enterprise AI patterns.
Qualifications & Requirements
- Bachelor's degree in Computer Science, Engineering, Data Science, or a related field; a master's degree is preferred.
- 7+ years of software engineering experience, including 3+ years building AI/ML or generative AI solutions.
- Demonstrated experience shipping LLM-based applications or agents to production, not only prototypes.
- Strong Python skills and experience building production services, APIs, and distributed or cloud-native applications.
- Working knowledge of LLM application patterns including prompting, RAG, tool/function calling, structured outputs, agent orchestration, and model evaluation.
- Experience with vector search, embeddings, document processing, data pipelines, and enterprise knowledge retrieval.
- Experience with cloud AI services and modern engineering practices, including containers, CI/CD, infrastructure automation, testing, monitoring, and source control.
- Understanding of identity, authorization, secrets management, data privacy, secure API design, and responsible AI controls.
- Ability to integrate with enterprise platforms such as ERP, CRM, collaboration, ticketing, and data systems.
- Excellent written and verbal communication skills, with the ability to translate business needs into technical designs and explain trade-offs clearly.
- English proficiency is required, including the ability to effectively communicate, collaborate, and perform job responsibilities in a professional business environment.
Preferred Qualifications
- Hands-on experience with the Microsoft AI ecosystem, such as Azure AI Foundry, Azure OpenAI, Microsoft 365 Copilot, Github Copilot, Copilot Studio, Microsoft Graph, and Power Platform.
- Experience with one or more orchestration frameworks and model providers, while maintaining provider-independent architecture where practical.
- Experience building governed enterprise agent marketplaces, reusable agent components, or model gateways.
- Experience with MLOps/LLMOps, automated evaluation, red-teaming, tracing, and AI cost management.
- Experience applying AI to enterprise workflows in Sales, Finance, Operations, HR, Engineering, or Customer Support.
- Semiconductor, electronics, or manufacturing industry experience.
Desired Characteristics & Attributes
- Hands-on builder. Comfortable working across architecture, code, integrations, evaluation, deployment, and operations.
- Business-first mindset. Starts with the problem and expected outcome, then selects the simplest effective technology.
- Pragmatic and accountable. Moves quickly, ships iteratively, and owns reliability and value after launch.
- Security- and quality-minded. Treats data protection, responsible AI, testing, and observability as core engineering requirements.
- Collaborative communicator. Builds trust with technical and non-technical partners and works effectively across functions.
- Curious and adaptable. Keeps pace with a rapidly changing field without chasing technology that lacks a clear use case.
Compensation Range (For HR only)
At SiTime, we believe great work deserves great rewards. We offer a comprehensive and highly competitive compensation package designed to attract top talent.
The annual base salary range for this role is $124,920 - $171,770. The final offer is determined by factors such as location, experience, education, and training.
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. We also offer equity grants, providing a meaningful opportunity to share in the company's future growth and success.
Benefits offered: 401(k) plan; medical, dental, vision, and life insurance; parental leave; legal services; and time-off plans.
Equal Opportunity Employer
SiTime is an Equal Opportunity Employer. We treat each person fairly and do not tolerate discrimination or harassment on the basis of any protected characteristic, 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, 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
Salary Context
This $124K-$171K range is below the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($148K) sits 30% below the category median. Disclosed range: $124K to $171K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
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: $171K - $171K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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