Interested in this AI/ML Engineer role at Instawork Internal?
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About This Role
Instawork is on a mission to create meaningful economic opportunities for skilled hourly professionals in communities around the globe. Our AI\-powered labor marketplace helps local businesses scale, and enables global technology companies to push the frontiers of robotics and AI. Backed by world\-class investors like Benchmark, Spark Capital, Craft Ventures, Greylock, Y Combinator, and others, we’re looking for exceptional talent to reimagine the way the world works.
We're hiring a Business Operations Associate to support variable pay and quota\-setting across our GTM organization — and, more importantly, to help us automate and rebuild how this work gets done using AI. This is a builder's seat: high ownership, a lot of latitude, and real upside for someone who ships. Traditional RevOps experience is a plus, not a prerequisite — what we're optimizing for is an instinct for turning manual, spreadsheet\-heavy process into agents and workflows.
Who You Are
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### Required
- Comfortable using AI tools daily (Claude, Cursor, ChatGPT, n8n, Replit, or similar), and genuinely excited to get better at directing them to do real work
- A builder's instinct — when you see a manual, repetitive process, your first thought is “how do I automate this,” not just “how do I do this faster”
- 0–3 years of experience in an analytical, operational, or cross\-functional business role (revenue operations, sales operations, finance, business/product operations, or similar) — new grads with strong internship experience welcome
- Willingness to learn SQL well enough to draft queries with AI assistance and sanity\-check the output against source data — you don't need to arrive fluent
- Strong Excel/Sheets skills and comfort with ambiguous, spreadsheet\-heavy problems
- High attention to detail — this role touches people's paychecks, and precision matters
- Clear, professional communication — you'll coordinate directly with sales leaders, Finance, and Legal, and need to hold your own in those conversations
### Nice to Have
- Coursework or project experience with SQL, dbt, or BI tools (Mode, Looker, Tableau)
- Exposure to Salesforce or another CRM — this isn't a CRM\-admin role, and we'd rather teach Salesforce to a strong builder than teach the automation instinct to a CRM specialist
- A personal project, class project, or internship where you built a script, automation, or tool that saved someone time
- Interest in sales compensation, forecasting, or GTM strategy as a career path
What You'll Do
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### AI \& Agentic Automation
- Partner with the RevOps team to turn manual, spreadsheet\-heavy steps of the comp process into small agents, scripts, and workflows — starting with well\-scoped pieces, not the whole cycle.
- Examples of what a strong first project might look like: a checklist\-style agent that flags obvious mismatches between a payout file and source data, a Replit tool that answers reps' basic crediting questions, an n8n workflow that reminds managers when their target approvals are due.
- Tools we use today: Claude, Cursor, ChatGPT, n8n, Replit. No software engineering background required — you'll get support from the team on how to build these. What matters is curiosity and follow\-through on shipping something real.
### Comp Operations Support
- Support monthly and quarterly comp cycles: pull data, run reconciliation checks, help track down discrepancies, and keep documentation current.
- Help run the quarterly target\-setting process — collecting inputs, formatting target packages, tracking approvals, and keeping the timeline on track.
- Assist with new\-hire guarantees, crediting exceptions, and manager comp questions, with guidance from senior RevOps and Finance partners as you build judgment on trickier calls.
### Forecasting \& Pipeline Analytics
- Help maintain weekly and monthly forecast roll\-ups and pipeline dashboards (coverage, stage conversion, velocity, aging).
- Pull together data and first\-pass analysis for the VP of Sales and CFO ahead of QBRs, under the direction of the senior RevOps lead.
### Plan Design \& Process Improvement
- Support the annual comp planning cycle — research, drafting, formatting, and coordinating review cycles with Legal and leadership.
- Bring a fresh eye to how the team's processes work today, and pitch specific, scoped improvements as you learn the function.
For CA\-based applicants:
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The base salary for this position is $90,000 \- $130,000\.
This position is eligible for equity in the form of stock options.
This position is eligible for Instawork benefits, including:
- Medical, dental, and vision plans with coverage beginning on the date of hire
- Flexible paid time off
- At least 8 paid company holidays annually
- Phone stipend
- Commuter stipend
- Supplemental pay on qualified leaves
- Employee health savings accounts (HSA) contribution
- Flexible spending plans
- 401K plan
- Perkspot \- discount program through Lumity
A variety of factors are considered when determining someone's compensation, including a candidate's professional background, experience, and location. Final offer amounts may vary from the amounts listed above.
Our Values
- Empathy, Trust \& Candor
We put ourselves in the shoes of our colleagues and customers and don’t shy away from uncomfortable conversations, instead building trust through honest and direct feedback.
- Bias for Action
We practice high\-velocity decision\-making, clear\-eyed that we often operate with incomplete information. Growing quickly means it’s OK to be wrong, so long as we learn from our mistakes and course correct!
- Always Be Learning
We’re a curious bunch, and with AI transforming our workplace we encourage everyone to learn from each other, compounding our knowledge and experience to help us change an entire industry.
- Act Like an Owner
We work long, hard, and smart, building products that delight our users and drive growth. Your ability to impact Instawork is limited only by your courage and conviction, not your job description.
About Instawork
Founded in 2015, Instawork is the nation’s leading online labor marketplace for food services, hospitality, light industry, and logistics, connecting more than 7M skilled workers with local restaurants, hotels, warehouses, stadiums, and more. Our AI\-powered platform serves thousands of businesses across more than 50 major markets in the United States and Canada. We're not just helping fill shifts, we're supporting local economies—and we're just getting started!
Instawork has been featured by CBS News, The Wall Street Journal, The Washington Post, and the Associated Press. Forbes included us on their Next Billion Dollar Startups list; RetailTech Breakthrough named us Workforce Hiring Solution of the Year for 2025; and Inc. 5000 recognized us as one of the country's top 10% fastest\-growing companies two years in a row. But what matters most is our impact. We're solving real problems for real people, and we’re doing it at scale.
Join our team to help us build something that matters! We’re looking for superstars who want to help us shape the future of work. With hubs in San Francisco, Bangalore, and Chicago, city offices in New York, Phoenix, and Singapore, we're back to working together in\-person five days a week because we believe the best ideas happen when great people collaborate face\-to\-face. We also value diverse perspectives and encourage applications from candidates of all backgrounds.
Ready to make an impact? Learn more at www.instawork.com/about.
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Salary Context
This $90K-$130K 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 Instawork Internal, 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. Entry-level AI roles across all categories have a median of $110,000. This role's midpoint ($110K) sits 49% below the category median. Disclosed range: $90K to $130K.
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.
Instawork Internal AI Hiring
Instawork Internal has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in San Francisco, CA, US. Compensation range: $130K - $130K.
Location Context
AI roles in San Francisco pay a median of $265,000 across 1,335 tracked positions. That's 23% 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 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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