Interested in this AI/ML Engineer role at Lineage?
Apply Now →Skills & Technologies
About This Role
Job Description:
The AI Engineer - Finance is an embedded AI partner within Lineage's Finance organization, the professional who sits at the intersection of the AI platform and finance and accounting problems. Reporting to the Chief Accounting Officer and working directly with the broader finance team, this role works with the finance team to identify high-value AI finance use cases, builds first solutions alongside finance teams where needed, and teaches those teams to build, run, and improve their own, so capability stays in Finance. These AI solutions will make our finance professionals more productive and make our processes more accurate, efficient, and well controlled. Success is measured by team adoption and self-sufficiency, not solely by solutions delivered. The role has a dotted-line relationship to a Senior Forward Deployed Engineer in Lineage's enterprise AI Center of Excellence (CoE) and builds/teaches in accordance with the standards, platforms, and governance established by the AI CoE.
This is a REMOTE role.
We are unable to sponsor work authorization, now or in the future, for this role.Primary Responsibilities:
- Partner and discover: Embed with Finance and Accounting stakeholders (e.g., controllership, financial reporting, FP&A, tax, audit, and treasury) to deeply understand workflows, pain points, and opportunities for AI-driven improvement. + Grow and maintain a broad and deep knowledge of finance and accounting processes, Lineage's supply chain business, and the ways AI can be applied to them.
- Teach and enable: Enable and upskill Finance team members to adopt AI tools effectively and identify new opportunities for AI-driven improvement. + Work with CAO and other finance leaders for ideas including, but not limited to, workshops, holding office hours, reusable guides and templates, champion network, hosting a hack-a-thon, etc. Coach teams to build and own their own solutions, and move from "building for" to "building with" over time.
- Build and deliver: Collaborate with stakeholders to quantify use-case value and , prioritize the engagement pipeline, and measure post-deployment impact. This includes identifying "quick wins" and situations where a short-term solution will provide benefit while a longer-term solution maybe developed in the future. Additionally, work with teams in existing systems, including those that leverage AI, to further leverage existing functionality and supplement with additional prompts, skills, and/or agents, as needed.
- Identify places where AI solutions require a broader skillset and then sScope, architect, and deliver end-to-end AI solutions, from data ingestion, prompt engineering, skill and agent build through deployment, monitoring, and iteration.
- Govern and control: Provide software engineering and AI solutions excellence including: + Leverage the CoE's core AI platform, working closely with Platform Engineers to extend tooling and surface platform gaps. + Follow and apply the AI CoE's standards for architecture, development, security, data governance, model evaluation, and responsible AI; partner with the dotted-line Senior Forward Deployed Engineer on design and code reviews, technical guidance, and alignment with enterprise AI priorities. + Ship flagship finance use cases (e.g., month-end close, account reconciliations, journal entry support, variance analysis, and reporting) that establish repeatable delivery patterns. + Design solutions that support internal controls, auditability, and SOX compliance, including appropriate human review, documentation, and data access controls.
- Measure and scale: Help the finance team quantify value, potential cost savings, and track adoption/self-sufficiency metrics.
- Other Technical Objectives: + Partner with Solutions Architects and Data & Integration Engineers to ensure solutions integrate cleanly with enterprise systems and finance data sources (e.g., ERP, consolidation, and reporting systems). + Document solution architectures, runbooks, and lessons learned to accelerate future engagements and enable developer enablement across the enterprise. + Consistently deliver high-quality software and processes in usable increments.
Education and Work Experience:
- Bachelor's degree in computer science, software engineering, or a related field, or equivalent practical experience.
- 4+ years of experience in software engineering, applied ML/AI, or solutions engineering, with direct client- or stakeholder-facing delivery.
- Experience with enterprise AI platforms such as Claude Enterprise, OpenAI (ChatGPT Enterprise), or other similar tools is a plus.
- Proficiency in Python and experience with modern AI/ML tooling (LLM APIs, agent frameworks, vector databases, RAG pipelines).
- Demonstrated ability to take a project from an ambiguous business need to a production-grade solution with minimal oversight.
- Experience working in fast-paced, early-stage, or consulting-style environments where scope and priorities shift frequently.
- Familiarity with cloud platforms (AWS, Azure, or GCP) and modern data infrastructure.
- Track record of shipping AI/ML use cases in enterprise environments (financial services, healthcare, manufacturing, logistics, or similar) is a plus.
- Experience building with agentic AI frameworks (e.g., LangChain, CrewAI, AutoGen) and evaluating model outputs systematically is a plus.
- Previous exposure to finance and accounting systems (e.g., ERP, general ledger, consolidation, close management, and reporting tools) is a plus.
- Understanding of accounting principles (U.S. GAAP), the financial close process, and internal controls (SOX) is a plus.
Required Skills and Abilities:
- Demonstrate excellent verbal and written communication skills, including the ability to present to business leaders and translate technical concepts.
- Manage stakeholder relationships and expectations across multiple engagements.
- Possess a willingness to be coached and proactively seek feedback.
- Manage multiple assignments / projects simultaneously.
- Learn and apply modern technologies and business concepts.
- Work within established governance frameworks and standards, and handle sensitive financial data with discretion.
#LI-Remote
Why Lineage?
This is an excellent position to begin your career path within Lineage! Success in this role enables greater responsibilities and promotions! A career at Lineage starts with learning about our business and how each team member plays a part each and every day to satisfy our customers' requirements. Beyond that, you'll help us grow and learn on our journey to be the very best employer in our industry. We'll ask you for your opinion and ensure we do our part to keep you developing and engaged as we grow our business. Working at Lineage is energizing and enjoyable. We value respect and care about our team members.
Lineage is an Equal Employment Opportunity Employer and is committed to compliance with all federal, state, and local laws that prohibit workplace discrimination and unlawful harassment and retaliation. Lineage will not discriminate against any applicant on the basis of race, color, age, national origin, religion, physical or mental disability or any other protected status under federal, state and local law.
Benefits
Lineage provides safe, stable, reliable work environments, medical, dental, and basic life and disability insurance benefits, 401k retirement plan, paid time off, annual bonus eligibility, and a minimum of 7 holidays throughout the calendar year.
Organic
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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Lineage, 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 $212,040 based on 13,780 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Lineage AI Hiring
Lineage has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Novi, MI, US.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 postings), Kubernetes (559 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.