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About This Role
Company Description
Afficiency is a rapidly growing Insurtech company whose mission is to provide life insurance to everyone via both our proprietary platform and in collaboration with our carrier and distribution partners. Located in NYC, we design and deliver life insurance products that can be purchased in an entirely digital environment for both the agents and their customers. We are looking for new team members to join us on our journey to shake up the life insurance industry. We need individuals who bring passion, curiosity, and a desire for excellence.
Job Description
Afficiency builds the technology that powers modern life insurance — from the moment someone starts an application to the moment they get approved. Every product we ship has dozens of small UX and workflow decisions baked into it, and most of them were made under deadline pressure, not with the benefit of hindsight. We're hiring someone whose job is to go find those moments, figure out what's actually broken or clunky, and then use AI tools to go fix it — not just write it up and hand it off.
About Afficiency
Afficiency is an insurtech company building the digital infrastructure behind term life insurance — application journeys, underwriting automation, and the agent and consumer\-facing tools that make buying life insurance faster and less painful. Our products span multiple carriers and channels, which means a lot of surface area, a lot of user journeys, and a lot of opportunity for a sharp, hands\-on operator to make things measurably better.
About the Role
This is a hybrid analyst/builder role. You'll spend part of your time in our products and UX flows like a detective — reading session recordings, walking through application journeys yourself, digging into support tickets and drop\-off points — to find where users get confused, stuck, or frustrated. Then, instead of stopping at a slide deck of recommendations, you'll use AI tools (Claude and others) to prototype and ship the fix yourself, or work directly with the team that can. A year in, success looks like a track record of real, shipped improvements you identified, scoped, and drove to completion — not a backlog of tickets someone else implemented.
What You'll Do
- Own the ongoing review of Afficiency's product and UX/UI journeys — application flows, agent tools, and consumer\-facing experiences — to identify friction, confusion, and drop\-off points.
- Dig into real usage data: session recordings, support tickets, underwriting outcomes, and conversion funnels, to find where the product isn't working the way it should.
- Turn findings into concrete, prioritized recommendations grounded in business impact, not just "this feels off."
- Use AI tools such as Claude to prototype, build, or directly implement fixes — copy changes, flow adjustments, small tooling, automation, or reports — rather than only handing off findings.
- Partner with product, engineering, and underwriting stakeholders to scope and ship changes, escalating and collaborating when something needs deeper technical work.
- Build a repeatable habit of measuring whether a change actually worked, and iterating when it didn't.
- Bring plain business judgment to the table: does this make sense, would a real customer understand this, is this worth fixing now or later.
Qualifications
What We're Looking For
Required
- Candidates not currently living in the NY tri\-state area will not be considered
- About 2\+ years of business, analyst, operations, or product\-adjacent work experience — a non\-technical background is completely fine.
- A genuinely analytical mind: you notice when something doesn't add up, and you dig until you understand why.
- Strong common sense and business judgment about how a product or process should work, and an eye for where it doesn't.
- A self\-starter attitude — you don't wait to be told what to look at or handed a fully scoped project.
- Real ownership instinct: when you find a problem, your instinct is "let me go fix this," not "let me go tell someone."
- Comfort learning and using AI tools (like Claude) as a working tool to move faster on analysis and execution — no coding background required, just curiosity and a willingness to figure it out.
- Clear written and verbal communication — you can explain what's broken and why it matters to both technical and non\-technical people.
Preferred (nice to have)
- Experience with insurance, fintech, or another regulated/complex\-product industry.
- Prior exposure to product analytics tools (e.g. session replay tools, funnel/behavioral analytics) or SQL.
Hands\-on experience already using AI tools to build something — a workflow, an automation, a prototype — even informally.
Additional Information Compensation \& Location
$70,000–$105,000 base salary, commensurate with experience. This role is based in New York, NY on a hybrid schedule.
Benefits
- Robust health, dental, and vision benefits
- 401(k) with matching
- Company\-paid WFH setup
- Hybrid work environment
- The chance to build something meaningful with a supportive, collaborative team
Afficiency is an Equal Opportunity Employer. All your information will be kept confidential according to EEO guidelines.
Salary Context
This $70K-$105K range is in the lower quartile 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 Afficiency, 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. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($87K) sits 59% below the category median. Disclosed range: $70K to $105K.
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.
Afficiency AI Hiring
Afficiency has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $105K - $105K.
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
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