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About This Role
Company Description We Are Bosch.
At Bosch, we shape the future by inventing high-quality technologies and services that spark enthusiasm and enrich people’s lives. Our areas of activity are every bit as diverse as our outstanding Bosch teams around the world. Their creativity is the key to innovation through connected living, mobility, or industry.
Let’s grow together, enjoy more, and inspire each other. Work #LikeABosch
- Reinvent yourself: At Bosch, you will evolve.
- Discover new directions: At Bosch, you will find your place.
- Balance your life: At Bosch, your job matches your lifestyle.
- Celebrate success: At Bosch, we celebrate you.
- Be yourself: At Bosch, we value values.
- Shape tomorrow: At Bosch, you change lives.
At Bosch, “Invented for Life” is what drives us and our vision of global future mobility. Bosch has an outstanding track record on pioneering key technologies worldwide which shapes the way we move. The next big milestone in our sight is to make autonomous vehicles a reality, bringing its benefits to today's roads.
Job Description
In this role, you will operate as a Lead AI Systems Engineer, setting technical direction across multiple teams, influencing architecture and engineering standards, and elevating technical capabilities across the organization.
We are seeking a Systems Engineer with strong leadership skills to build and enhance our AI-focused systems engineering and testing frameworks. In this role, you will guide the design, implementation, and evaluation of our Advanced Driver Assistance Systems (ADAS) and Autonomous Driving (AD) technologies. You will serve as the technical lead for a cross-functional team, integrating modern software development practices to help realize safe, reliable self-driving vehicles and empower our global R&D teams.
Responsibilities:
- Architect and scale intelligent failure triaging and automated Root Cause Analysis (RCA) frameworks capable of processing petabyte-scale simulation and real-world vehicle logs.
- Apply modern AI/ML methods (e.g., LLMs/VLMs, time-series anomaly detection, statistical pattern recognition, and causal inference) to automatically isolate failure modes across perception, planning, control, and vehicle platform layers.
- Integrate automated triaging into continuous integration, regression testing, and cloud replay pipelines (SiL/HiL/ViL) to streamline software release cycles and raise validation standards across engineering groups.
- Establish best practices for log data structures, automated analytics workflows, vector search/embeddings for failure mode retrieval, and ML pipeline reliability.
- Partner directly with autonomy software leads, validation engineers, and cloud infrastructure teams to align toolsets and integrate automated diagnostics into day-to-day R&D workflows.
Qualifications
- Master’s degree or Ph.D. in Computer Science, Computer Engineering, Robotics, Electrical Engineering, or a related technical field (or equivalent practical experience).
- 5+ years of progressive engineering experience in software development, data analytics infrastructure, or automated testing for autonomous vehicles, robotics, or complex distributed systems.
- 2+ years of experience leading technical architecture, setting engineering strategy, or guiding technical direction across multiple engineering teams.
- Expert proficiency in Python and C++, with demonstrated experience building production-grade data pipelines, distributed execution frameworks, or testing infrastructure.
- Hands-on experience working with big data structures, robotics logging formats (e.g., ROS/ROS2, MCAP, Protobuf, CAN logs), and cloud data storage.
Preferred Qualifications:
- Demonstrated experience applying LLMs/VLMs, sequence modeling, pattern recognition, or vector embeddings to log classification, failure mode retrieval, or automated root cause analysis.
- In-depth understanding of autonomous driving software architectures (perception, sensor fusion, localization, motion planning, vehicle control).
- Experience operating within SiL, HiL, or cloud-based replay and simulation testing setups at scale.
- Experience with distributed frameworks and data tools such as PySpark, Ray, Databricks/Snowflake, Vector DBs, Apache Airflow, Kubernetes, and AWS/GCP services.
Additional Information
We offer a competitive base salary for this position with a range in US-California of -$185,000 - $215,000 along with an annual corporate bonus, and a long-term incentive bonus designed to reward sustained impact and contribution over time. Within the salary range, the individual pay is determined based on several factors, including, but not limited to, work experience and job knowledge, complexity of the role, job location, etc.
Your well-being matters at Bosch! We offer a a benefits package designed to empower you in every area of your life. This includes premium health coverage, a 401(k) with generous matching, resources for financial planning and goal setting, ample paid time off, parental leave, and comprehensive life and disability protection. Your Recruiter can share more details for this position during the interview process.
Learn more about our full benefits offerings by visiting: https://www.myboschbenefits.com/public/welcome.
Equal Opportunity Employer, including disability / veterans.
- Bosch adheres to Federal, State, and Local laws regarding drug-testing. Employment is contingent upon the successful completion of a drug screen and background check. Candidates who have been offered the position must pass both screenings before their start date.
#LI-JM1
Salary Context
This $185K-$215K range is above 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 Bosch, 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 ($200K) sits 6% below the category median. Disclosed range: $185K to $215K.
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.
Bosch AI Hiring
Bosch has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Sunnyvale, CA, US. Compensation range: $215K - $215K.
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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