Interested in this AI/ML Engineer role at Jiffy?
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About This Role
Jiffy.com is seeking Agentic-First Software Engineers (Snr/Staff/Principal) with deep hands-on experience orchestrating AI coding agents to ship production software at pace. Reporting directly to an Engineering Manager and working closely with Product, GM's and other cross functional teams, you will lead the charge on delivering end-to-end features across our e-commerce platform from storefront and checkout to internal tools and platform services while running multiple AI-driven work streams in parallel.
At Jiffy, engineers no longer write code by hand: every line that reaches production is produced, tested, reviewed, and deployed by coding agents (Claude Code, Codex, Cursor) under the direction of a human orchestrator. This role is perfect for an individual contributor who has already internalized the shift from “typing code” to “directing agents,” has strong architectural taste and review instincts, and wants to help define what an AI-native engineering org looks like. You will thrive here if you are a systems thinker, a builder by nature, and someone who gets more done by managing five parallel agents well than by writing a single function perfectly.
At this time, Jiffy is not able to sponsor or take over sponsorship of an employment visa (e.g., H-1B) for this position. Applicants must be legally authorized to work in the United States without employer sponsorship, now or in the future.
Key Responsibilities
- Orchestrate AI coding agents (Claude Code, Codex, Cursor) across 3–6 concurrent work streams spanning features, refactors, bug fixes, and investigations using worktrees, sub-agents, and background tasks to maximize parallel throughput.
- Own features end-to-end: translate product intent into clear specifications, decompose work into agent-executable plans, drive agents through implementation, review every diff critically, run QA, land PRs, and verify production health.
- Design and architect scalable, reliable systems across our stack Rails 8 / Solidus e-commerce, GraphQL APIs, React 19 front end, Kubernetes infrastructure holding the architectural and judgment calls that agents cannot make.
- Raise the quality bar on AI-generated code by catching hallucinated APIs, enforcing secure and idiomatic patterns, and pushing back on output that is technically working but structurally wrong.
- Build and evolve Jiffy’s internal AI tooling: author skills, rules, prompts, MCP integrations, and evaluation harnesses that make the whole engineering org faster. Contribute to our shared .ai/ skills and rules library.
- Operate with production rigor: drive agents to produce meaningful test coverage (RSpec, Vitest, Playwright), monitor deploys, respond to incidents, and fully own the services you ship.
- Partner cross-functionally with Product, Design, Ops, and peer engineering teams to define and ship features; participate in architecture and code reviews; mentor engineers on AI-native workflows and orchestration patterns.
- Continuously identify where remaining manual work (QA, ops runbooks, data investigations, migrations) can be replaced by agents, and automate it.
Requirements
Basic Qualifications
- Bachelor’s degree in Computer Science, Engineering, or a related field.
- 5+ years of professional software engineering experience shipping production systems at scale.
- Hands-on experience using AI coding agents (Claude Code, Codex, Cursor, or equivalent) as a primary development tool. This is mandatory. You must be able to walk through real features, refactors, or investigations delivered through agent orchestration — not occasional autocomplete usage.
- Proven ability to run multiple agents in parallel and manage concurrent work streams without losing quality, context, or accountability.
- Strong software design fundamentals: OO design, API design, data modeling, distributed-system reliability, and performance.
- Proficiency in at least one server-side language (Ruby, Python, Go, Java, or equivalent) and one modern front-end stack (React / TypeScript or equivalent), with the expectation that agents produce the code while you direct, review, and validate it.
- Experience with relational databases (Postgres / MySQL), SQL, and building or consuming REST or GraphQL APIs.
- Elite code-review skills: the ability to read, critique, and correct diffs at speed is the core competency of an AI-first engineer.
- Strong written communication — clear intent, clear PR descriptions, clear prompts. In an AI-first org, writing is the programming interface.
- A high level of integrity, accountability, and ownership, with a commitment to excellence across every deliverable.
- Desire to work in a fast-paced, ambiguous, high-autonomy environment.
Preferred Qualifications
- AI Tooling Builder: Experience authoring or extending AI developer tooling — custom skills, slash commands, MCP servers, sub-agents, evaluation harnesses, or prompt libraries used by a team.
- E-commerce Depth: Experience with Ruby on Rails and Solidus / Spree, or other large-scale e-commerce platforms.
- Modern Front End: Experience with React 19, TypeScript, GraphQL (Apollo), Tailwind, and modern state management (React Query, XState, Jotai).
- Cloud Native Operations: Experience operating services on Kubernetes (Istio, KEDA, Flagger) and modern observability stacks
- Automated QA at Scale: Experience driving agents to maintain and extend test suites with Playwright, Vitest, and RSpec.
- AI-Native Leadership: Track record of mentoring engineers on AI-native workflows, publishing internal patterns, or leading an organization through the shift away from manual coding.
- Measurable Throughput Gains: Documented improvements in features-per-week, cycle time, or review time directly attributable to AI adoption.
- High-Growth Environment: Experience shipping in a fast-paced, agile environment at a high-growth startup or tech company.
Benefits What We Offer
The base compensation for this role is expected to range from $150,000 - $265,000 per year. Actual compensation will be based on a variety of factors, including skills, experience, qualifications, and overall fit for the role.
Total compensation may include base salary, performance-based bonuses, equity, and benefits. Final offers are tailored to each candidate and take into consideration their background and years of experience.
- Comprehensive Benefits: Full medical, dental, and vision coverage, with a portion of premiums paid by Jiffy.
- Retirement Planning: 401(k)
- Wellness Support: Annual wellness benefits to help you stay healthy and balanced.
- Tools for Success: Choice of MacBook or PC laptop, plus equipment for your home office setup.
- Perks & Extras: Annual credit of $200 to use on our website plus more team merch drops than you will know what to do with!
- Professional Development: Annual stipend to support your learning and career growth.
EEO
Jiffy is proud to be an equal opportunity employer. We do not discriminate in hiring or any employment decision based on race, color, religion, national origin, age, sex (including pregnancy, childbirth, or related medical conditions), marital status, ancestry, physical or mental disability, genetic information, veteran status, gender identity or expression, sexual orientation, or other applicable legally protected characteristics. Jiffy considers qualified applicants with criminal histories, consistent with applicable federal, state and local law.
Salary Context
This $150K-$265K range is above the median for AI/ML Engineer roles in our dataset (median: $178K across 2239 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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Jiffy, 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 $212,040 based on 13,780 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. Disclosed range: $150K to $265K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Jiffy AI Hiring
Jiffy has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $265K - $265K.
Location Context
AI roles in New York pay a median of $221,000 across 3,257 tracked positions. That's 4% 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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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