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About This Role
About AirSlate airSlate is a global SaaS technology company that develops no-code workflow automation, electronic signature, and document management solutions. Our award-winning products - Sign Now, pdfFiller, DocHub, altaFlow, Instapage, and US Legal Forms
- serve over hundreds of millions of users and more than one million customers worldwide, helping organizations of every size digitize processes, improve efficiency, and transform how they work.
We’re in an exciting phase of growth and transformation, with teammates in more than 20 countries across three continents and main hubs in the United States, Poland, Ukraine and Philippines .
At airSlate, we’re building value for customers and a culture where growth and innovation go hand in hand. We’re looking for people eager to shape products, scale a company, and thrive in a fast-moving environment.
Who We Are airSlate built a $1800M+ business serving dozens of millions of users across pdfFiller, SignNow, DocHub, AltaFlow and Instapage, on the back of a proprietary long-tail marketing engine. AI is now disrupting the core of that business and, at the same time, opening up a much bigger opportunity: solving the underlying business outcome end to end — draft, sign, track, invoice, collect — not just the document step.
*The opportunity* We're running the company as two deliberately separate efforts. Our legacy portfolio — pdfFiller, DocHub, SignNow, AltaFlow — which we're optimizing for defensible 3-5 year revenue to fund what's next. AI Labs: a structurally separate, AI-native business we're building from scratch, targeting $20-50M of new topline in 2-3 years.
AI Labs is already proving the model works. BizDraft — AI-drafted agreements for SMBs — has shipped and is our learning vehicle for running a ~90% AI-driven product end to end. Ordinum, an AI compliance agent for regulated SMBs, is in development. BizPulse, which reconstructs a small business's financial and operational reality straight from its inbox, is in a controlled experiment. Alongside the products, AI Labs has rebuilt the pdfFiller editor from scratch (5x faster, 10x smaller) and stood up internal AI systems — Discovery Engine, document-based segmentation, Synthetic Users, Zero Reporting — that are starting to change how the rest of the company works.
We need a product leader to take AI Labs from "proving the model" to a real business.
*The role* You'll own product for AI Labs: strategy, roadmap, and definition across our AI-native product line (BizDraft, Ordinum, BizPulse, and whatever comes next). You lead a small group of Product Owners, and will work in tight lockstep with AI Labs' engineering leadership, who own the build team.
A small go-to-market team — one product marketer and one marketer — supports AI Labs today, and you'll work closely with them on market analysis, idea generation, triage and prioritization, positioning, and launch.
What You'll Do
- Own product strategy and roadmap for AI Labs, targeting $20-50M in new AI-native topline over 2-3 years
- Scale BizDraft post-launch, drive Ordinum and BizPulse through to go/no-go for launch
- Identify, size and prioritize 2 to 5 more high-potential projects, based on established customer needs and a potential competitive advantage for airSlate
- Hire and lead a very small team of AI-native, 0-to-1 Product Owners, each owning the build out of a specific product against the prioritized ideas, and developing it based on customer and market feedback
- Develop a working understanding of our existing products (pdfFiller, SignNow, DocHub, AltaFlow) and their customers, to surface needs and use cases AI Labs can act on — some of which may be better rolled into an existing product as a feature rather than launched as a standalone AI Labs product
- Define product requirements and priorities with the precision AI-driven execution demands
- Partner closely with AI Labs' engineering lead on sequencing and trade-offs, without owning the engineering team directly
- Decide which internal AI systems incubated in Labs (Discovery Engine, segmentation, Synthetic Users, Zero Reporting, etc.) are ready to hand off to a functional owner — Marketing, CIO, Data — once proven, and which stay in Labs
- Partner with the AI Labs go-to-market team on positioning, GTM planning, and launch execution for each product as it matures
- Represent AI Labs product progress to the executive team and the board
Who You Are
- Experienced in building 0-to-1 inside a larger company
- A builder-operator who has shipped AI-native products end to end — or at the very least, transformative standalone features or modules on top of existing products, with proven adoption success
- Deeply fluent in AI-assisted build workflows (Claude Code, OpenAI's Codex and the like) and what they change about team shape, ratios, and the product definition bar
- Comfortable driving product outcomes through precision and influence rather than headcount — you won't have an engineering team reporting to you
- Commercially minded — comfortable partnering closely with marketing and PMM on positioning and GTM
- Low ego and collaborative — able to earn credibility with engineering leadership and peers rather than assume it from the org chart
- SaaS, SMB, and document/workflow space experience a plus
*Why this and not somewhere else* Ownership of product for the most ambitious green-field initiative in the company, with real budget and a mandate to help build a $20-50M+ business from scratch — backed by a company with 100M+ users and proven distribution.
What We Offer Flexible working environment
- Our teams operate across the globe. We value in‑person collaboration in our hubs, but we also embrace remote and hybrid working. You can work from one of our offices in the United States, Poland, Romania or Ukraine, or remotely from many countries.
Competitive compensation and stock options
- We offer salaries that reflect local market conditions and experience, plus a performance-based bonus system and stock options so you share in the company’s growth.
Professional growth and learning
- We invest in your development through courses, conferences, and access to learning resources. We encourage career growth and internal mobility, supporting teammates who want to explore new opportunities within airSlate.
Health and well‑being
- We provide comprehensive benefits tailored to each country, including health coverage, wellness programmes and access to fitness options. We also dedicate quarterly company-wide Mental Health Days, when everyone takes time off to rest and recharge.
Family‑friendly culture
- Family life is part of who we are, and we embrace it in many forms. From flexibility for parents to our airSlate Junior Club for kids, to company-wide family days and our pet-friendly approach, we’re committed to making work and life easier to balance.
Giving back
- We support charitable initiatives around the world through the airSlate Care programme. Our current focus includes humanitarian aid in Ukraine and other regions, matching employee donations and supporting causes chosen by our teams.
Open communication
- We encourage transparent dialogue at all levels. From team discussions to company-wide Q&A sessions with our CEO, we make sure everyone has the chance to be heard and to influence how we grow.
It is airSlate's policy to comply with all applicable equal employment opportunity laws by making all employment decisions without unlawful regard or consideration of any individual’s race, religion, ethnicity, color, sex, sexual orientation, gender identity or expressions, transgender status, sexual and other reproductive health decisions, marital status, age, national origin, genetic information, ancestry, citizenship, physical or mental disability, veteran or family status or any other basis protected by applicable national, federal, state, provincial or local law. airSlate's policy prohibits unlawful discrimination based on any of these impermissible bases, as well as any bases or grounds protected by applicable law in each jurisdiction. If you need assistance or an accommodation during the application process because of a disability, it is available upon request. airSlate is pleased to provide such assistance and no applicant will be penalized as a result of such a request. Pursuant to relevant law, where applicable, airSlate will consider for employment qualified applicants with arrest and conviction records.
Read our Recruitment Privacy Notice to Learn how we process your personal information. We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At airSlate, 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 $211,950 based on 13,048 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,300.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
airSlate AI Hiring
airSlate has 2 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager. Positions span Boston, MA, Remote, US. Compensation range: $240K - $240K.
Location Context
AI roles in Boston pay a median of $215,000 across 360 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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