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About This Role
Basic Information
Job Name
Sr Data Scientist \- USA
Country
United States
State
NA
Date Published
07\-Jul\-2026
Job ID
47234
Travel
up to 25%
This role can be based remotely in United States
Looking for more details about our benefits?
Description and Requirements
CareerArc Code
CA\-MM
\#LI\-MM1
Hybrid: \#LI\-Hybrid
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.
We are looking for a Senior AI Data Architect / Solution Engineer to design the architecture that delivers trusted data at scale — and to build the AI products that put it directly in the hands of the business. This is a hands\-on, senior role for someone who can set the technical blueprint, build the agents on top of it, and act as the bridge between business needs and technical reality.
You will work closely with the Director and Enterprise Architect, serve as technical lead and advisor to the team, and partner with the Senior AI Data Engineer to take designs from blueprint to production. Success is measured by the trust, scale, and adoption of the data and AI products that ship — and by the business outcomes they unlock.
Here is how, through this role, you will contribute to BMC's and your own success:
- Architecture for Scale (Primary Focus) — Design the reference architecture and delivery patterns that take governed data from the semantic layer to every downstream consumer — dashboards, applications, and AI agents — reliably and at scale, with the build\-vs\-buy calls and standards that let the platform grow without painful rework.
- AI Data Products \& Agents — Architect and build the Data Agent, the natural\-language interface that lets business users query governed data without writing SQL; design agentic workflows with orchestration, tool/function calling, retrieval, evaluation, and guardrails.
- Semantic Foundation — Co\-design the semantic layer (HoneyDew / DBT) — the metrics, dimensions, and governed business meaning that power both analytics and AI — ensuring agents query governed definitions, not raw tables.
- Technical Leadership — Serve as technical lead and trusted advisor; set patterns, run design reviews, and mentor engineers, partnering with the Data Engineer to take designs to production.
- Business Translation — Translate ambiguous business problems into clear technical requirements; own stakeholder relationships and move fluently between executive framing and engineering detail.
- Ownership \& Execution — Take features and subsystems end\-to\-end, proactively identifying gaps and driving work forward even when requirements are incomplete or evolving.
To ensure you're set up for success, you will bring the following skillset \& experience:
- 8\+ years across data architecture and hands\-on engineering, including production systems that held up under real enterprise use.
- Strong proficiency in Python and SQL, with deep, hands\-on experience designing and building on Snowflake and DBT.
- Proven experience designing and building LLM / agentic solutions — RAG, function / tool calling, orchestration (LangChain / LlamaIndex / LangGraph), prompt engineering, and evaluation / observability.
- Deep understanding of the semantic layer as the architectural bridge between source data and business meaning — and why it is foundational to trustworthy AI.
- Demonstrated ability to translate complex technical concepts for non\-technical stakeholders and influence architecture decisions across teams.
- Strong communication and the ability to lead through execution in a fast\-moving environment.
Broad technical exposure (depth not required in all areas):
- Cloud\-native architecture across AWS, Azure, or GCP.
- Vector databases, embeddings, and retrieval evaluation.
- CI/CD and modern engineering practices for data and AI systems.
- Containerization and distributed systems (Docker, Kubernetes, microservices).
Whilst these are nice to have, our team can help you develop in the following skills:
- HoneyDew or other universal/headless semantic layers (HoneyDew, Cube, AtScale, dbt Semantic Layer).
- Snowflake Cortex / Snowflake Intelligence, or production text\-to\-SQL systems.
- Prior technical\-lead or principal\-engineer role with mentorship responsibility.
- Background bridging pre\-sales / solution engineering and hands\-on delivery.
\#LI\-Remote
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.
Min salary
132,975
Max salary
221,625
Min Salary \- NEW
132,975
Max Salary \- NEW
221,625
Salary Context
This $132K-$221K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($177K) sits 19% below the category median. Disclosed range: $132K to $221K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
BMC Software AI Hiring
BMC Software has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $221K - $221K.
Location Context
AI roles in New York pay a median of $220,000 across 1,045 tracked positions.
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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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