Interested in this AI/ML Engineer role at TruStage?
Apply Now →About This Role
At TruStage, we’re on a mission to make a brighter financial future accessible to everyone. We put people first, and work hand in hand with employees and customers to create a diverse and inclusive environment. Passionate about building insurance and financial services solutions, we push the boundaries of what’s possible. We need you to help us shape what’s next. You’ll be encouraged to share your experiences, ideas and skills to help others take control of their financial future.
Join a team that has received numerous awards for being a top place to work: TruStage awards and recognition
Job Responsibilities:
Rapid POC \& MVP Delivery (Agentic AI)
- Embedded into Data Strategy team, lead data engineering, platform, and business teams to identify high value Agentic AI use cases (e.g., Data Product Build, data quality automation, metadata management, governance assistance).
- Design and deliver rapid POCs and MVPs embedded in real TruStage data environments.
- Evaluate agent performance, reliability, controls, and human in the loop patterns.
Forward Deployment \& Embedded Engagement
- Act as a forward deployed resource, embedding with teams to co define problems, refine use cases, and adapt solutions in context.
- Translate ambiguous business and operational needs into practical AI driven data solutions.
- Ensure solutions fit TruStage’s operating model, risk posture, and regulatory expectations.
Architecture, Standards \& Methodology
- Create architect design patterns, standards and methodology that will be followed by data management teams to scale Agentic/AI work.
- Closely collaborate with Enterprise AI architect for setting best practices, standards and governance process for MCP/AA/API based integration patterns.
- Document learnings from POCs and MVPs into:
+ Reference architectures for Agentic AI;
+ Design patterns and guardrails;
+ Deployment and operating standards.
- Define processes and methodologies for developing, deploying, and governing Agentic AI in data domains in accordance and partnership with AI Governance team as needed
- Establish criteria for scalability, security, observability, and cost management.
Scale Enablement \& Adoption
- Partner with central platform, data governance, and engineering leaders to industrialize validated patterns.
- Enable teams with clear playbooks, templates, and examples to scale Agentic AI safely and consistently.
- Influence roadmap priorities based on field learnings and adoption signals.
Feedback Loop to Strategy
- Provide continuous feedback to D\&A leadership on:
+ What works vs. what doesn’t in real deployment;
+ Capability gaps and tooling needs;
+ Change management and operating model implications.
- Help TruStage evolve from experimentation to AI\-enabled data operations at scale.
Platform \& Ecosystem Strategy
- Influence enterprise AI/data platform capabilities, AI tool selection, and integration standards.
- In collaboration with AI COE, evaluate emerging Agentic AI frameworks, orchestration platforms, vector technologies, and LLM tooling for enterprise fit.
- Emerging Technologies \& Trends: Assess vendor capabilities, strategic partnerships, and technology maturity to accelerate delivery while minimizing lock\-in risk.
Organizational Enablement \& Capability Building
- Mentor architects, engineers, and data teams on Agentic AI patterns and architectural best practices for data management.
- Build internal communities of practice and reusable knowledge assets.
- Reference Architecture Ownership – AI Data Foundations.
- Establish and maintain enterprise reference architectures for multi\-agent systems, orchestration patterns, memory/context management, and integration with enterprise data platforms.
The above statement of duties is not intended to be all inclusive and other duties will be assigned from time to time.
Job Requirements:
- Bachelor’s degree in information technology, computer science, or related field, or equivalent combination of education and/or related professional work experience.
- 10\+ years of strong background in data architecture, data engineering, and cloud platforms.
- 4 years of hands\-on experience with AI/ML, LLMs, automation, or orchestration technologies.
- Proven ability to move from concept to prototype to production.
- Comfort working in ambiguous, fast\-moving environments.
- Strong communication skills across technical and executive audiences.
- Deep appreciation for risk, governance, and trust in data and AI.
- Systems integration expertise.
- Cross\-functional execution (engineering, product, security, operations, and business teams).
- Infrastructure and DataOps fluency.
- Change management and adoption.
- Graph technologies – ontologies, knowledge graphs, semantics, context graphs.
- Prefer prior experience in the insurance or finance industry
At this time, we're not considering applicants that need any type of immigration sponsorship (additional work authorization or permanent work authorization) now or in the future to work in the United States. This includes, but IS
NOT LIMITED TO: F1\-OPT, F1\-CPT, H\-1B, TN, L\-1, J\-1, etc
\#LI\-SW
If you’re ready to help make a difference, apply today. A resume is required to apply. TruStage may process applicant information using an Artificial Intelligence (AI) tool. This tool automatically generates a screening score based on how well applicant information matches the requirements and qualifications for the position. TruStage recruiters use the screening score as a guide to further evaluate candidates; the score is one component of an application review and does not automatically determine whether a candidate moves forward. Candidates may choose to opt out of this process.
Compensation may vary based on the job level, your geographic work location, position incentive plan and exemption status.
Base Salary Range:
$158,000\.00 \- $237,000\.00
At TruStage, we believe a sound, inclusive benefits program is of vital importance, along with a flexible workplace that allows for work\-life balance, career growth and retirement assistance. In addition to your base pay, your position may be eligible for an annual incentive (bonus) plan. Additional benefits available to eligible employees include medical, dental, vision, employee assistance program, life insurance, disability plans, parental leave, paid time off, 401k, and tuition reimbursement, just to name a few. Beyond pay and benefits, we also recognize that flexibility, including working in a place you prefer, is essential to caring for our employees. We will continue to strive to offer flexibility and invest in technology and other tools that will make hybrid working normal rather than an exception, so that when “life happens,” you can focus on what’s most important.
Accommodation request
TruStage is a place where everyone can bring their best self and thrive. If you need application or interview process accommodations, please contact the accessibility department.
Salary Context
This $158K-$237K range is above 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 TruStage, 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 $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 ($197K) sits 10% below the category median. Disclosed range: $158K to $237K.
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.
TruStage AI Hiring
TruStage has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $237K - $237K.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% 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 3,708 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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