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About This Role
Data quality is the bottleneck of the AI era. Every Fortune 500 is trying to solve it. We're the team they call.
Soda is the data quality layer for Disney, Ralph Lauren, CBRE, HelloFresh, 2K Games, and Nubank. Our open-source engine, Soda Core, is used by companies like Tesla, Slack, Adyen, Walmart, and JPMC. The category is growing fast because AI doesn't work on bad data.
We're hiring one person to own content at Soda, end to end.
Soda's mission is to monitor the world's decisions. We're building the first Data & AI performance monitoring platform that spans data collection to automated decision-making. Soda helps customers find, understand, and fix data quality issues.
The Role The website. Technical blogs, webinars, and product videos. Your own content and following.
One person cannot hand-make all of that. So you'll build the system that can. That isn't a stretch goal or a second phase - we don't think the job exists without it.
You'll join the Growth team reporting to a co-founder.
What you might call yourself. Content engineer, technical content marketer, growth engineer, marketing engineer, GTM engineer, developer advocate, technical writer who got bored, or nothing in particular. We've seen this job posted under every one of those. We don't care what's on your CV. You need to be able to write and you need to be able to build, and almost nobody can do both.
Why this is a real problem and not a content calendar. The expertise is already in the building. Founders who have argued about this category for a decade. Customer engineers who spend their week inside broken pipelines at Fortune 500s. Customers who will tell you things nobody has published anywhere. Almost none of it gets written down, and what does get out is hand-made, one piece at a time, by people who have other jobs. Your job is to get it out of their heads and onto the internet, at a volume that is only possible with a machine behind it.
Taste is the bar. Anti-slop is the whole point. An engine that ships slop faster has negative value, and most of them do. We're hiring for the judgment to look at an accurate, competent, lifeless draft and bin it - then work out what the pipeline did wrong and fix it. The engine has to write in distinct voices: a founder sounds nothing like a customer engineer, and the output should sound like them, not like a model. Your own profile and following is part of how we'll know it works. If it can't make you sound good, it won't make anyone else sound good either.
What this is not. You'll write code every day, but this is not a software engineering job. We're not hiring you to build product, and if you're a backend, data, or platform engineer looking for your next engineering role, this isn't it. It's also not a traditional marketing or DevRel role: no agency to brief and no conference circuit.
Requirements
- You can write, and you can tell good writing from bad: send us something you wrote that a technical audience actually read. The most important line in this ad.
- Serious prompt engineering: multi-step LLM pipelines with structured outputs and evals, not chatting with a model. You know why the naive version produces slop.
- 3+ years building things that go to market: technical content, growth engineering, marketing engineering, GTM engineering, DevRel, marketing ops, RevOps, or a founder background. Nobody agrees what to call this job yet. Titles vary; portfolios don't.
- Enough code to be autonomous: Python or TypeScript, APIs, webhooks, a database. Nothing should be blocked waiting for an engineer. We build agentically here - Claude Code, MCP servers, and agent skills are the daily workflow.
- Enough data fluency to be credible: you're writing for Staff Data Engineers and CDOs. You can hold your own on pipelines, data quality, and warehouses.
- Generalist instinct: you find the constraint and fix it, and you'd rather build the workflow than do the task fifteen more times.
- Independence: you don't need hand-holding in a distributed, async team across 12+ countries.
- Fluent English.
Good to Have* You've grown a technical audience of your own: newsletter, blog, LinkedIn, open source, YouTube.
- Content, growth, or DevRel in data, developer tools, or infrastructure.
- Agentic workflows shipped to real users: MCP servers, Claude skills, tool-using agents.
- You can shoot and cut video yourself.
- Data quality, data management, or data observability industry experience.
Benefits
- Compensation: $120,000-$130,000 / €110,000-€120,000 per year + equity - the same band wherever you live. We don't discount for geography.
- Fully remote (US or EU-based), with offices in Brooklyn if you want one.
- All the tokens you need, across all frontier models.
- Real ownership, real impact, no micromanagement.
- Colleagues in 12+ countries.
Our Values We value freedom with responsibility, transparency, and a growth mindset. We avoid politics, have no tolerance for dishonesty, and value open and direct communication without judgment. We proactively bring up and address potential issues before they become critical, and we overcommunicate frequently. Working at Soda, you will have full autonomy to make impactful decisions.
❤️ Why you will love to join Soda* Be part of a transformation: data quality is having its moment, and we're at the front of it.
- Ownership from day one: if you see a bottleneck, you remove it.
- International culture: colleagues in 12+ countries.
- Work on your own terms: fully remote, flexible hours.
- Paid the same wherever you are: one band for the US and Europe, no geographic discount.
- Your name on the work: you build your own audience on company time, and we want you to.
- Direct line to the founders: how Soda talks about the category gets decided with you in the room.
What you might not love* Rapidly changing priorities: our roadmap can pivot quickly. It's a fast-paced environment with a LOT of work to be done.
- Flexibility required: you will have to learn new things all the time.
- You will kill a lot of your own output: accurate and lifeless still goes in the bin.
- Judged on output, not architecture: an elegant pipeline nobody reads from is a failure.
Hiring Process* 15-minute phone screen - with a founder
- Short Assignment
- Craft deep dive (90 min)
- Culture fit (1 hour)
We answer every application, and every candidate hears back within 3-5 days of each step. If it's not a hell yes, it's a no, and we'll tell you quickly and kindly.
Salary Context
This $120K-$130K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Soda Data, 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 $212,000 based on 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($125K) sits 41% below the category median. Disclosed range: $120K to $130K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Soda Data AI Hiring
Soda Data has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $130K - $130K.
Location Context
AI roles in New York pay a median of $221,000 across 2,878 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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