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About This Role
HYPR, the Identity Assurance Company, helps organizations create trust in the identity lifecycle. We are a community of bold thinkers and energetic doers who imagined a brand new approach to identity security and, at the same time, are eliminating a pervasive and persistent 60-year old problem – the password. Since being founded in 2014, the company has grown to be the premium provider of authentication security to marquee brands around the globe and are quickly becoming the recognized leader in Identity Assurance. We are looking for exceptional people with the ambition and the will to positively change the world. If that sounds like you, join us on our mission to create a passwordless world and secure the identity lifecycle.
Your Role and Impact
You have proven yourself in fast-paced environments, recently and feverishly building AI-powered workflows, agent prototypes, GTM Automation, and other 0->1 initiatives. You are "AI-pilled" and very much of the mindset that marketing teams everywhere must evolve rapidly and that entire functions will increasingly be AI-generated and supported with cross-team collaborative input where impactful.
Now you are ready for greater ownership and beyond. You're ready to take our marketing organization to the next level and prove that you can take command of the team and our mission.
This role is the opportunity to lead HYPR as we continue to scale our Identity Assurance platform along with new product launches in the AI space for governance and management of agents.
This hybrid role is based out of HYPR's New York City headquarters located conveniently in midtown Manhattan. You are the type of person who comfortably wants to be in the office 3-4 days a week, spend time in person with others on the marketing team, engineering leadership and our CEO. Clocking out at 5pm isn't in your DNA. This role reports directly to the President & COO, working alongside a small but mighty team of field marketing, visual/branding designer, PR, generalist, and BDRs.
Key Responsibilities
- Design, build, and maintain AI-powered workflows and lightweight tools that improve GTM efficiency, using LLM APIs, no-code/low-code automation platforms, and agent-builder tools.
- Prototype and ship internal AI applications — research/enrichment agents, outreach personalization tools, content or reporting assistants — that GTM teams can actually adopt.
- Build and optimize AI-assisted workflows for sales outreach, including sequencing, personalization at scale, lead enrichment, and prioritization, in partnership with Sales on our Outreach engagement stack.
- Own and improve automations within HubSpot, including lifecycle workflows, lead routing/scoring, campaign operations, and data hygiene.
- Build reusable systems — templates, modules, and workflows — that scale across campaigns and teams, not one-off fixes.
- Support reporting and attribution by automating data pulls, dashboards, and recurring analysis that currently require manual work.
- Work directly with Sales, Marketing, and RevOps to identify high-friction, high-repetition processes worth automating, and document what you build clearly enough that others can use, maintain, and extend it.
- Act as an internal resource for "can AI do this?" questions across the GTM org.
Tools You'll Work With
- Existing GTM stack: HubSpot (marketing automation, lifecycle workflows, landing pages) and Outreach (sales engagement, sequencing, prospect workflows).
- AI & automation tooling: LLM platforms (Claude Code, or similar) for building; automation/orchestration platforms like n8n, Zapier, or Make; sales intelligence and enrichment tools like Clay, Apollo, or ZoomInfo.
- Reporting & data: Excel/Sheets minimum for dashboards and ad hoc analysis; SQL and basic scripting (Python or JavaScript) are a plus
Requirements
- 4-10 years of experience in marketing with experience across functions such as demand gen, digital marketing, enablement, BDR management and/or supporting functions such as Outreach sequencing, lead scoring rebuild, and campaign ops, and more.
- Demonstrated builder instinct — you can point to specific things you've built (an automation, an internal tool, a workflow, a script) and explain the problem it solved.
- Genuine, hands-on fluency with AI tools — using them to build and automate, not just to draft copy or summarize documents.
- Comfortable with ambiguity — you can take a vague "this is slow and annoying" problem and turn it into a scoped, shippable fix.
- Strong documentation instincts and clear communication with non-technical stakeholders.
- Experience with SalesForce, HubSpot, Outreach, or similar marketing/sales engagement platforms is highly desirable.
- Basic scripting comfort (Python or JavaScript) is a plus for connecting APIs and debugging integrations.
- Experience in cybersecurity, identity, or enterprise B2B SaaS GTM is a plus, as is familiarity with agent-builder frameworks or MCP-style tool integrations.
The Benefits of Working with HYPR
- Competitive Salary & Equity offer with significant upside as we scale
- Great Medical/Dental/Vision coverage, as well as FSA/HSA, 401k available and more
- Generous equipment budget and customized workstation. You will have what you need to be successful
- Long-term stability within a high-growth sector within identity access automation
- A mission driven culture. At HYPR you will never be spinning your wheels on tasks that won't make an impact. Everything we develop is cutting edge and vital to the success and security of our clients
Note:
This job is hybrid with 3-4 days onsite at our NY Corporate HQ.
HYPR is an Equal Opportunity Employer.
Base Salary: $140,000 to $185,000 + Variable
Our salary ranges are based on paying competitively for our size and industry, and are one part of our total compensation package that also includes equity, benefits, and other opportunities at HYPR. Individual pay decisions are based on a number of factors, including location, qualifications for the role, experience level, skillset, and balancing internal equity relative to other HYPRions. We expect the majority of the candidates who are offered roles at HYPR to fall healthily throughout the range based on these factors.
As the premium provider of authentication security, HYPR is committed to preventing identity fraud by safely and accurately verifying the identities of our employees. Accordingly, all new employees are required to participate in an automated identity verification process, which includes document verification, location detection, and facial recognition, on their first day of employment with the Company. To learn more about this identity verification process, please contact [email protected].
HYPR is an Equal Employment Opportunity employer and will not discriminate against an applicant or employee based on race, religion, color, national origin, gender (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender identity, gender expression, age, status as a protected veteran, status as an individual with a disability, or any other applicable legally protected characteristic under federal, state or local law.
Applicants with disabilities may be entitled to reasonable accommodation under the Americans with Disabilities Act and certain state and/or local laws. A reasonable accommodation is a change in the way things are normally done which will ensure an equal employment opportunity without imposing undue hardship on HYPR. Please inform [email protected] if you need assistance completing HYPR's application process.
Salary Context
This $140K-$185K range is below the median for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At HYPR, 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 11,857 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,275. This role's midpoint ($162K) sits 23% below the category median. Disclosed range: $140K to $185K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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: $272,275; VP: $235,000.
HYPR AI Hiring
HYPR has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $185K - $185K.
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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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