Interested in this AI/ML Engineer role at BPM?
Apply Now →About This Role
BPM – where caring and community is in our company DNA; we are always striving to be our best selves; and we’re compelled to ask the questions that lead to innovation. The Assurance Technology Director plays a critical leadership role in modernizing the assurance practice by driving innovation, enhancing audit quality, implementing advanced technology solutions, and improving operational efficiency to support the future growth and success of the firm.
Working with BPM means using your experiences, broadening your skills, and reaching your full potential in work and life—while also making a positive difference for your clients, colleagues, and communities. Our shared entrepreneurial spirit drives us to see and do things differently. Our passion for people makes BPM a place where everyone feels welcome, valued, and part of something bigger. Because People Matter.
What you get:
- Total rewards package: from flexible work arrangements to personalized benefit structures and financial compensation options that give you choice and flexibility
- Well\-being resources: interactive wellness platform and incentives, an employee assistance program and mental health resources, and Colleague Resource Groups (CRGs) that…
- Balance \& flexibility: 14 Firm Holidays including 2 floating, Flex PTO, paid family leave, winter break, summer hours, and remote work options, so you can balance challenging yourself with taking care of yourself
- Professional development opportunities: A collaborative learning culture supported by extensive CPE resources, a dedicated coaching program, and access to live classes, workshops, and seminars through BPM University.
Who is successful at BPM:
- Caring people who put others first
- Self\-starters who embody the BPM entrepreneurial spirit
- Authentic individuals with a diverse point of view
- Lifelong learners with a drive to excel
- Resilient people who rise to the occasion
Responsibilities:
- Lead assurance technology initiatives focused on innovation, automation, process improvement, and operational efficiency.
- Develop and execute strategic plans to modernize audit technology, tools, and workflows.
- Partner with assurance leadership to identify opportunities for digital transformation and continuous improvement.
- Oversee implementation and adoption of audit technologies, including data analytics, AI tools, workflow automation, and audit software platforms.
- Drive standardization and optimization of audit processes across teams and offices.
- Collaborate cross\-functionally with IT, risk advisory, compliance, and firm leadership to align transformation initiatives with business goals.
- Monitor industry trends, regulatory developments, and emerging technologies impacting the audit profession.
- Lead change management efforts, including communication, training, and stakeholder engagement to ensure successful adoption of new processes and tools.
- Develop KPIs and reporting metrics to measure transformation success, audit efficiency, and quality improvements.
- Support talent development by mentoring audit professionals and promoting innovation\-focused learning initiatives.
- Manage technology\-related budgets, project timelines, and vendor relationships.
- Ensure technology initiatives maintain compliance with professional standards, firm policies, and regulatory requirements.
Requirements:
- Bachelor’s degree in Accounting, Finance, Information Systems, Business, or a related field; Master’s degree preferred.
- CPA required; additional certifications such as CIA, CISA, PMP, or Six Sigma are a plus.
- 10\+ years of experience in public accounting, audit, consulting, or transformation leadership roles.
- Strong understanding of audit methodology, risk assessment, internal controls, and regulatory standards.
- Experience leading large\-scale transformation, process improvement, or technology implementation projects.
- Knowledge of audit technologies, data analytics tools, automation platforms, and AI\-driven audit solutions.
- Proven ability to lead cross\-functional teams and influence senior stakeholders.
- Strong project management, organizational, and strategic planning skills.
- Excellent communication and presentation abilities.
- Ability to thrive in a fast\-paced, evolving environment and drive organizational change.
- Experience within a public accounting firm or professional services environment preferred.
This position is not eligible for third\-party or agency submissions. We will not accept unsolicited resumes from search firms or staffing agencies.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At BPM, 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 in Demand for This Role
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 $181,170 based on 12,692 positions with disclosed compensation. Director-level AI roles across all categories have a median of $247,800.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
BPM AI Hiring
BPM has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $170,000 across 1,926 positions. About 15% of all AI roles offer remote work.
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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, 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 $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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