Interested in this AI/ML Engineer role at EvolutionIQ?
Apply Now →Skills & Technologies
About This Role
About Us: EvolutionIQ's mission is to improve the lives of injured and disabled workers and enable them to return to the workforce, saving billions of dollars in avoidable costs and lost productivity to the US and global economies and make insurance more affordable for everyone. We are currently experiencing massive growth and to accomplish our goals, we are hiring world\-class talent who want to help build and scale internally, and transform the insurance space. Our team is our \#1 priority, and we have been named one of Inc.'s Best Workplaces 3 years in a row!
The Adventure: We're the leading AI Guidance Platform in the insurance industry today, working with some of the largest insurance carriers in the US and expanding globally. We are seeking a highly skilled and motivated Senior Analytics Engineer to play a pivotal role in building and scaling our next\-generation analytical data platform. This role will be instrumental in shaping our analytics strategy, designing robust data models, and ensuring the availability, accuracy, and performance of our business intelligence data assets. As a key member of our team, you will collaborate closely with data scientists, analysts, and other engineers to deliver data\-driven insights that drive critical business decisions.
#### Responsibilities:
Platform Design and Development:
- Lead the design, development, and implementation of a scalable, reliable, and efficient analytical data platform.
- Influence decisions on technology and tool selection for data ingestion, storage, transformation, and analysis for the analytics platform.
- Contribute to the overall data architecture and strategy, ensuring alignment with business needs and best practices.
Data Pipeline Engineering:
- Build and maintain robust ETL/ELT pipelines to ingest, transform, and load data from various sources into the data warehouse (e.g., cloud storage, databases, APIs).
- Develop and optimize data models for analytical use cases, ensuring data quality, consistency, and accessibility.
Data Quality and Governance:
- Establish and enforce data quality standards and processes to ensure data accuracy and integrity.
- Implement data governance policies and procedures to manage data access, security, and compliance for analytics use cases
- Proactively identify and address data quality issues, working with stakeholders to resolve root causes.
Collaboration and Communication:
- Partner with data scientists, analysts, and other engineers to understand their data needs and provide solutions.
- Effectively communicate technical concepts and designs to both technical and non\-technical audiences.
- Mentor and guide data engineers, fostering a culture of learning and collaboration.
Innovation and Continuous Improvement:
- Stay up\-to\-date on the latest trends and technologies in data engineering and analytics.
- Identify opportunities to improve existing data processes and tools.
- Proactively propose and implement innovative solutions to address data challenges.
#### Required Qualifications \& Skills:
- Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
- 5\+ years of experience in data engineering, with a focus on building analytical data platforms.
- Demonstrated experience with cloud data warehousing solutions (e.g. BigQuery, Snowflake, Redshift).
- Strong proficiency in SQL and experience with data modeling techniques (e.g., star schema, dimensional modeling).
- Experience building and maintaining ETL/ELT pipelines using tools like Dagster, Apache Airflow, dbt, or similar.
- Experience with programming languages such as Python, Java, or Scala.
- Experience with data governance and data quality tools and processes is highly desirable.
- Excellent problem\-solving and analytical skills, along with passion for data and a commitment to data quality.
Work\-life, Culture \& Perks:
- Compensation: The base salary range is $200\-225K, with flexibility depending on a candidate's background and experience. An annual bonus plan and company equity plan (RSUs) are also included in our compensation package.
- Well\-Being: Medical, dental, vision, short \& long\-term disability, life insurance and AD\&D, and 401k matching. Additional family, wellness, and pet benefits.
- Home \& Family: Paid time off and sick leave, 100% paid parental leave (16 weeks for primary caregivers and 12 weeks for secondary caregivers). We offer a flexible schedule for new parents returning to work.
- Office Life: Catered lunches, happy hours, pet\-friendly spaces, and monthly technology stipend.
- Growth \& Training: $1,000/year for each employee for professional development, as well opportunities for tuition reimbursement.
- Sponsorship: We are open to sponsoring candidates currently in the U.S. who need to transfer their active visa. Please check with our Recruiting team if your visa is applicable for transfer.
*EvolutionIQ appreciates your interest in our company as a place of employment. EvolutionIQ is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.*
Salary Context
This $200K-$225K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At EvolutionIQ, 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. Disclosed range: $200K to $225K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
EvolutionIQ AI Hiring
EvolutionIQ has 4 open AI roles right now. They're hiring across AI Software Engineer, AI/ML Engineer. Based in New York, NY, US. Compensation range: $225K - $330K.
Location Context
AI roles in New York pay a median of $220,000 across 1,650 tracked positions.
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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 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.