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About This Role
Basic Information
Job Name Principal AI Engineer - USA (B) Country United States State California City Santa Clara Date Published 05-Oct-2026 Job ID 47640 Travel You may occasionally be required to travel for business Additional Locations Detroit - Michigan, Houston - Texas, San Francisco - California, New York - New York, Washington - DC Looking for more details about our benefits?
Description and Requirements
CareerArc Code CA-BS #LI-BS1 Remote: #LI-Remote BMC empowers nearly 80% of the Forbes Global 100 to accelerate business value, faster than humanly possible. Our industry-leading portfolio unlocks human and machine potential to drive business growth, innovation, and sustainable success. BMC does this in a simple and optimized way by connecting people, systems, and data that power the world’s largest organizations so they can seize a competitive advantage. At BMC, we don’t do ordinary. Across BMC, our AI teams are building the next generation of agentic AI, capabilities that help enterprises run their most critical systems faster, more resiliently, and with confidence. Our agentic AI empowers autonomous agents to detect, diagnose, and help resolve complex problems with speed, accountability, and human oversight.
We’re not hiring for roles; we’re hiring for impact. You’ll solve hard problems, build smart solutions, and work with people who challenge and champion you, and see your work make a difference for customers running mission-critical systems.
You will build autonomous and semi-autonomous AI agents that plan, reason, make decisions, and execute tasks, and orchestrate multi-agent systems that integrate with tools, APIs, and enterprise applications. Everything you build must meet enterprise standards: secure, reliable, scalable, observable, and governed.
Here is how, through this role, you will contribute to BMC’s and your own success:
- Design, develop, and maintain enterprise-grade agentic AI applications using modern LLMs and AI frameworks.
- Build autonomous and semi-autonomous AI agents capable of planning, reasoning, decision-making, and task execution.
- Develop multi-agent systems and orchestrate interactions between agents, tools, APIs, and enterprise applications.
- Integrate AI agents with internal and external services through APIs, databases, messaging systems, and workflow engines.
- Build and deploy RAG solutions using vector databases and enterprise knowledge sources.
- Optimize AI workflows for performance, scalability, reliability, and cost efficiency.
- Implement observability, monitoring, guardrails, and governance mechanisms for production AI systems.
- Build and run agent evaluations — define eval sets, test behaviour, iterate on prompts, tools, and workflows.
- Package and deploy solutions in production; apply strong diagnostics across the AI stack.
- Collaborate with product, architects, and stakeholders; contribute to reviews, documentation, and best practices.
- Set applied AI architecture and investment priorities across products.
- Make consequential tradeoffs among models, platforms, build-vs-buy, and risk controls.
- Drive org-wide adoption of AI capabilities with measurable outcomes and managed risk.
- Align product, platform, security, and quality leaders on a coherent AI direction.
To ensure you’re set up for success, you will bring the following skillset & experience:
- Typically, 12+ years of relevant experience
- Bachelor's or Master's in Computer Science, AI, ML, or related field (or equivalent experience).
- Hands-on experience with LLMs (OpenAI, Anthropic, Gemini, Llama, or equivalent)
- Familiarity with agentic frameworks (LangGraph, LangChain, CrewAI, or similar) and agentic SDKs as level requires.
- Understanding of prompt engineering, tool/function calling, planning, reasoning, and workflow orchestration.
- Python; REST APIs, microservices, cloud, containers, CI/CD, and testing — depth scales with level.
- Evals for LLM/RAG/Agent use cases; observability (Langfuse, OpenSearch, OpenTelemetry) as level requires.
- Strong communication and ownership mindset.
- Evidence of setting AI architecture/priorities across multiple products.
- Org/BU adoption with measurable outcomes and explicit risk management.
- Sustained influence on where the organization invests in applied AI.
Whilst these are nice to have, our team can help you develop in the following skills:
- Frontier/foundation models beyond API usage (evaluation, adaptation, fine-tuning).
- Open-weight models and local inference (vLLM, Ollama, Hugging Face).
- Data exploration techniques and tools.
- Agile / Atlassian; advanced IDEs (Cursor, IntelliJ, VSCode).
Our commitment to you!
BMC’s culture is built around its people. We have 6000+ brilliant minds working together across the globe. You won’t be known just by your employee number, but for your true authentic self. BMC lets you be YOU!
If after reading the above, You’re unsure if you meet the qualifications of this role but are deeply excited about BMC and this team, we still encourage you to apply! We want to attract talents from diverse backgrounds and experience to ensure we face the world together with the best ideas!
BMC is committed to equal opportunity employment regardless of race, age, sex, creed, color, religion, citizenship status, sexual orientation, gender, gender expression, gender identity, national origin, disability, marital status, pregnancy, disabled veteran or status as a protected veteran. If you need a reasonable accommodation for any part of the application and hiring process, visit the accommodation request page.
BMC Software maintains a strict policy of not requesting any form of payment in exchange for employment opportunities, upholding a fair and ethical hiring process.
The annual base salary range represents the low and high end of the BMC salary range for this position. Actual salaries depend on a wide range of factors that are considered in making compensation decisions, including but not limited to skill sets; experience and training, licensure, and certifications; and other business and organizational needs.
The range listed is just one component of BMC's employee compensation package. Other rewards may include a variable plan and country specific benefits.
At BMC, it is not typical for an individual to be hired at /near the top of the range. A reasonable estimate of the current range is $197,850 - $329,750
Min salary 197,850 Mid point salary 263,800 Max salary 329,750
Salary Context
This $197K-$329K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $177K across 1606 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 9,253 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At BMC Software, 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 $212,000 based on 13,052 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,382. This role's midpoint ($263K) sits 24% above the category median. Disclosed range: $197K to $329K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $264,000. 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: $229,382; Director: $272,300; VP: $235,830.
BMC Software AI Hiring
BMC Software has 6 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Positions span Santa Clara, CA, US, New York, NY, US. Compensation range: $254K - $329K.
Location Context
Across all AI roles, 6% (530 positions) offer remote work, while 8,653 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 (1,487 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 9,253 open positions tracked in our dataset. By seniority: 107 entry-level, 4,462 mid-level, 3,410 senior, and 1,274 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (530 positions). The remaining 8,653 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $264,000. 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 9,253 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,989), AI Software Engineer (462), AI Product Manager (457). 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 (107) are outnumbered by mid-level (4,462) and senior (3,410) 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 1,274 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (530 positions), with 8,653 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 $264,000, 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 (1,638 postings), Aws (893 postings), Azure (718 postings), Rag (666 postings), Gcp (532 postings), Pytorch (434 postings), Claude (408 postings), Kubernetes (402 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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