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About This Role
Marketing Remote
About Us Sense is transforming the way companies hire with AI-powered technology designed to bring unprecedented speed, precision, and personalization to every step of the recruiting process. Our mission is to reimagine how organizations engage with talent—creating faster, smarter, and more human experiences for employers, recruiters, and job seekers alike.
Founded in 2015, Sense is a high-growth HR Tech SaaS company serving over 1,000 customers worldwide. With 250+ team members and backing from Google Ventures, SoftBank, Accel, and Avataar Ventures, we’ve scaled rapidly and continue to lead innovation in talent engagement and automation.
Our diverse and collaborative team is tackling one of the most universal challenges in the workforce—helping people find meaningful work faster. At Sense, you’ll have the opportunity to shape innovative AI solutions that are changing how the world hires, impacting millions of careers globally. Join us in building the future of hiring. Job Description
- Location: Remote (Any state in the Continental US)
- Travel: 10–15% (5–8 industry events and trade shows per year)
- Reports To: Head of Sales
- Experience Level: 1–3 years
- Employment Type: Full-time
About the Role
At Sense, we are transforming how enterprise hiring teams and staffing agencies attract, engage, and retain talent. Our AI-powered Talent Engagement Platform automates critical recruitment workflows from AI chatbots and multi-channel candidate engagement to automated scheduling and voice AI: helping market leaders hire faster and smarter.
We are seeking an energetic, curious, and proactive Junior Marketing Manager to join our fast-growing marketing team. Reporting directly to our Co-Founder and Head of Sales, you will serve as the connective tissue between our AI brand narrative and revenue pipeline. You will dabble across Social Media, Demand Generation, Product Marketing, Sales Enablement, and Field Events, with primary operational ownership over our LinkedIn presence and HubSpot marketing operations.
If you are obsessed with AI (actively using Gemini, Claude, and modern generative tools), love creating high-impact collateral for sales reps, and are excited to represent Sense at top HR tech and talent events, this role offers high visibility and incredible career growth.
Key Responsibilities
- LinkedIn & Executive Thought Leadership: Plan, write, and execute weekly content on our corporate LinkedIn page. Collaborate with the Co-Founder to turn high-level insights into engaging, viral social posts.
- Sales Enablement & Collateral: Partner directly with the Head of Sales and sales reps to design, write, and update buyer collateral: 1-pagers, slide decks, case study summaries, and feature tear-sheets to help win deals.
- Event Management & Travel: Lead end-to-end event execution for 5–8 national conferences and field events annually. Responsibilities include booth logistics, pre-event promotional campaigns, collateral shipping, on-site event support, and post-event lead capture follow-ups in HubSpot.
- HubSpot & Digital Operations: Update website landing pages, create email marketing sequences, manage lead forms, and track campaign performance inside HubSpot.
- Demand Generation & Product Marketing: Assist with multi-channel campaigns, webinar launches, and translating new product releases into buyer-centric messaging.
- AI Tool Integration: Heavily leverage Gemini, Claude, and generative media tools to accelerate draft creation, streamline event copy, research competitors, and automate weekly workflows.
Job Requirement
Qualifications & Requirements
- Location: Must be based in the continental United States and authorized to work without sponsorship.
- 1–3 years of experience in B2B marketing, tech, SaaS, or agency settings.
- Core Tech Stack: Direct experience using HubSpot (email/landing pages) and LinkedIn for organic growth.
- AI Power User: Demonstrated daily reliance on generative AI platforms (Gemini, Claude, ChatGPT) for writing, research, and ideation.
- Travel Flexibility: Ability and willingness to travel 5–8 times per year across the continental US for events, client meetups, or team offsites.
- Dual-Stakeholder Mindset: Strong organizational skills to balance high-level brand building with pipeline-driving collateral and event leads.
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 Sense Talent Labs, Inc., 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. Entry-level AI roles across all categories have a median of $111,000.
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.
Sense Talent Labs, Inc. AI Hiring
Sense Talent Labs, Inc. has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $188,000 across 2,316 positions. About 6% of all AI roles offer remote work.
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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