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About This Role
Make Your Mark::
As a Senior AI Developer on BlackLine’s Corporate AI team, you will play a pivotal role in bringing AI to every corner of the company. You’ll design and deliver enterprise\-grade AI agents that transform data into actionable insights, streamline internal workflows, and help teams across the business make better, faster decisions. In this highly visible role, you’ll partner directly with business leaders and be trusted to shape, build, and launch AI solutions end to end.
You'll Get To::
- Design, build, and ship AI agents and applications using tools such as Vertex AI, Anthropic, and cloud\-based AI services, focusing on internal/enterprise use cases.
- Translate business problems into AI solutions by working directly with stakeholders to clarify needs, define requirements, and propose impactful use cases.
- Own the full development lifecycle—from requirements and design, to coding, testing, deployment, and iterative enhancement.
- Launch short\-term AI agents and features within a quarter, consistently meeting release dates and timelines.
- Integrate AI agents with enterprise systems, APIs, and internal tools, creating seamless, end\-to\-end workflows that fit into how teams already work.
- Monitor and support AI solutions in production, investigating issues, improving performance, and refining user experience based on feedback and metrics.
- Contribute to team\-wide standards and best practices for AI development, including coding patterns, observability, and governance.
- Collaborate closely with a small global AI team (US and India) and contractors, sharing context, reviewing code, and jointly owning deliverables.
- Provide knowledge transfer and occasional mentoring, helping others understand how to effectively leverage AI tools and patterns.
What You'll Bring::
- 3\+ years of Senior\-level software development experience, including at least 2\+ years focused on AI development in an enterprise environment.
- Strong programming skills in one or more languages used for AI and cloud development (e.g., Python, Java, or similar).
- Hands\-on experience building applications or agents with generative AI platforms, such as:
+ Google Cloud Vertex AI
+ Anthropic models and associated SDKs/ADKs
+ Other major cloud AI APIs and services
+ Agentic frameworks like LangChain or LlamaIndex to build, chain, and deploy LLM\-powered applications.
- Proven experience delivering production\-grade AI solutions, not just prototypes—covering reliability, maintainability, and scalability.
- Experience implementing RAG architectures using vector databases such as Vertex AI Vector Search, Pinecone, or Weaviate.
- Advanced prompt engineering skills and a deep understanding of how to design, test, and optimize prompts for specific outcomes within agents.
- Experience developing software that adheres to enterprise security and compliance standards
- Demonstrated ability to clarify ambiguous requirements, think analytically, and design practical solutions to operational and functional problems.
- Excellent communication skills, with a track record of partnering directly with business leads to gather requirements, present options, and align on outcomes.
- A history of operating as a senior individual contributor, setting objectives and delivering results that materially impact a team, function, or division.
- Comfort working in a small, distributed team, pulling your own weight while collaborating effectively across time zones.
We’re Even More Excited If You Have::
- You’ve worked on a Corporate/Enterprise AI team, building internal tools or agents rather than only external product features.
- You have experience leading or significantly contributing to high\-visibility AI projects with executive\-level stakeholders.
- You’re familiar with enterprise data, security, and governance considerations when building AI solutions (e.g., permissions, privacy, data minimization).
- You’ve successfully collaborated with global teams and contractors (e.g., US and India) in a fast\-paced environment.
Thrive at BlackLine Because You Are Joining::
- A technology\-based company with a sense of adventure and a vision for the future. Every door at BlackLine is open. Just bring your brains, your problem\-solving skills, and be part of a winning team at the world's most trusted name in Finance Automation!
- A culture that is kind, open, and accepting. It's a place where people can embrace what makes them unique, and the mix of cultural backgrounds and varying interests cultivates diverse thought and perspectives.
- A culture where BlackLiner's continued growth and learning is empowered. BlackLine offers a wide variety of professional development seminars and inclusive affinity groups to celebrate and support our diversity.
BlackLine is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to sex, gender identity or expression, race, ethnicity, age, religious creed, national origin, physical or mental disability, ancestry, color, marital status, sexual orientation, military or veteran status, status as a victim of domestic violence, sexual assault or stalking, medical condition, genetic information, or any other protected class or category recognized by applicable equal employment opportunity or other similar laws
BlackLine recognizes that the ways we work and the workplace itself has shifted. We innovate in a workplace that optimizes a combination of virtual and in\-person interactions to maximize collaboration and nurture our culture. Candidates who live within a reasonable commute to one of our offices will work in the office at least 3 days a week.
Salary Range:: USD $130,000\.00/Yr. \- USD $163,000\.00/Yr.
Salary Context
This $130K-$163K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 2088 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 4,021 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At BlackLine, 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 $180,000 based on 12,397 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($146K) sits 19% below the category median. Disclosed range: $130K to $163K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($290,000) and AI Safety ($274,200). By seniority level: Entry: $97,760; Mid: $163,400; Senior: $227,400; Director: $244,800; VP: $250,000.
BlackLine AI Hiring
BlackLine has 5 open AI roles right now. They're hiring across AI Product Manager, AI Software Engineer, AI/ML Engineer, MLOps Engineer. Positions span New York, NY, US, Pleasanton, CA, US. Compensation range: $163K - $322K.
Location Context
Across all AI roles, 15% (608 positions) offer remote work, while 3,392 require on-site attendance. Top AI hiring metros: New York (2,585 roles, $210,300 median); San Francisco (2,102 roles, $253,000 median); Los Angeles (1,764 roles, $190,500 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 4,021 open positions tracked in our dataset. By seniority: 118 entry-level, 1,906 mid-level, 1,555 senior, and 442 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (608 positions). The remaining 3,392 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($290,000 median, 39 roles); AI Safety ($274,200 median, 52 roles); Research Engineer ($260,000 median, 421 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 4,021 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,818), Data Scientist (312), AI Software Engineer (280). 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 (118) are outnumbered by mid-level (1,906) and senior (1,555) 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 442 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (608 positions), with 3,392 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 $200,000. Top-quartile roles start at $253,000, 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 $290,000 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 (2,069 postings), Aws (1,260 postings), Azure (946 postings), Rag (893 postings), Gcp (783 postings), Pytorch (624 postings), Prompt Engineering (619 postings), Claude (570 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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