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About This Role
About BrightPlan
BrightPlan is a leader in Total Financial Wellness. We provide a comprehensive global solution that addresses all aspects of employees’ financial health at every stage of life, empowering HR teams to enhance the employee experience and better attract, retain, and engage talent.
We are a remote\-first SaaS company building practical AI\-powered solutions that improve real financial outcomes for users. Our team values accountability, curiosity, thoughtful execution, and continuous learning.
We are expanding our engineering team to build the next generation of AI\-powered financial wellness tools. This role focuses on building and shipping practical AI capabilities within a production SaaS platform rather than on research\-oriented model development.
The Role
We are looking for a Senior Applied AI Engineer to help design, build, and scale the systems that power AI across the BrightPlan platform. This role will play an important part in shaping the evolution of AI systems within BrightPlan’s product architecture.
This role focuses on developing reliable, scalable infrastructure and frameworks for LLM\-powered applications, enabling the broader engineering team to deliver reliable, high\-quality AI features. You will work closely with the CTO to help shape technical decisions around AI architecture, model usage, and system evolution.
This role is ideal for an engineer who enjoys owning complex technical problems and building the systems that make production AI reliable and scalable.
This is an opportunity to have significant technical impact in a real\-world fintech environment.
What You’ll Do
- Design and implement scalable AI services and supporting infrastructure that enable AI\-powered product capabilities across the platform
- Develop solutions for evaluating and improving LLM output quality
- Build frameworks and tooling that support AI feature development across the engineering team
- Develop and maintain systems that support model tuning, evaluation, and optimization
- Contribute directly to deployment and operational reliability of AI services in production environments
- Optimize AI services for latency, cost efficiency, and reliability
- Partner with the CTO to evaluate emerging AI capabilities and recommend technical approaches for model usage, architecture, and system design
- Provide technical guidance to engineers building AI\-powered features and help establish effective development patterns and engineering best practices
- Contribute to technical architecture discussions related to AI systems and platform capabilities
What Success Looks Like
- Contribute meaningfully to establishing reliable systems supporting AI\-powered product features within your first 90 days
- Improve the performance, consistency, and reliability of AI\-generated outputs
- Enable faster experimentation and iteration for AI product development
- Help establish development patterns and technical practices that enable other engineers to build AI\-powered features more effectively
- Demonstrate strong technical leadership and ownership of AI system design and implementation
Requirements Qualifications
*When applying, please include a brief description of one AI\-powered feature, service, or system you helped build or support in production.*
Required
- 5\+ years of software engineering experience, including strong backend or systems development experience
- Strong Python development experience
- Experience designing and building backend services or distributed systems
- Experience integrating LLM APIs, generative AI services, or AI\-enabled capabilities into production systems
- Experience evaluating or improving AI model performance in production environments
- Strong problem\-solving and systems design skills
- Ability to collaborate effectively in a cross\-functional team
Nice to Have
- Experience with FastAPI or similar frameworks
- Experience with model fine\-tuning, retrieval\-augmented generation (RAG), prompt evaluation, or LLM evaluation methods
- Experience building internal tooling or infrastructure supporting AI applications
- Experience working with vector databases or AI data pipelines
- Experience working in fintech or B2B SaaS environments
Benefits Why Join BrightPlan
- Build meaningful technology that helps people improve their financial lives
- Work on practical AI applications deployed in a live fintech product
- Collaborate in a remote\-first environment with experienced product and engineering leaders
- Competitive compensation, benefits, and equity
Compensation
The estimated annual base salary range for this role is $160,000 to $195,000, depending on qualifications, experience, and location.
This role is also eligible for:
- Annual performance bonus
- Equity participation
- Comprehensive benefits package
*Actual compensation will be determined based on relevant qualifications, skills, experience, and geographic location.*
*BrightPlan is proud to be an equal opportunity employer and to consider qualified applicants without regard to race, color, religion, sex, national origin, ancestry, age, genetic information, sexual orientation, gender identity, marital or family status, veteran status, medical condition or disability.*
*To work at BrightPlan, you must live and work in the United States and be eligible for employment by any employer in the U.S. You must have a dedicated workspace and reliable internet service. At*
*BrightPlan, base salary is determined by job\-related experience, education/training, related job skills, residence location, as well as market indicators. Pursuant to state and local pay disclosure requirements, the base pay range for this role is listed annually above.*
*This position is eligible for BrightPlan's standard benefits offering, including medical/dental/vision, 401(k) with company contribution, annual performance bonus, life insurance, paid time off, and other benefits in accordance with applicable plan documents. This benefits information is based on BrightPlan's good faith estimate as of the date of publication and may be modified in the future.*
BrightPlan is not accepting agency referrals; only direct applicants will be considered
Salary Context
This $160K-$195K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At BRIGHTPLAN, 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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. Disclosed range: $160K to $195K.
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.
BRIGHTPLAN AI Hiring
BRIGHTPLAN has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in US. Compensation range: $155K - $195K.
Location Context
AI roles in Austin pay a median of $215,300 across 523 tracked positions. That's 8% above the national 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 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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