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About This Role
This role is not open to visa sponsorship or transfer of visa sponsorship including those on H1\-B, F\-1, OPT, STEM\-OPT, or TN visa, nor is it available to work corp\-to\-corp.
This role requires a hybrid schedule and will be based in our South Charlotte, NC or New York, NY office (Tuesday through Thursday) and optional remote days on Mondays and Fridays each week.
We're hiring a Senior Engineer who builds with AI by default. Someone for whom agentic tools are the IDE, not the side project. You'll own the technical direction of our personalization and data platform, built on Go microservices, TypeScript applications, and Python\-based AI agents. Your job is to ship product features, make the platform smarter and faster, and raise the bar for how the team builds software. You're a senior IC with tech\-lead scope: you own technical direction for your domain, influence architecture decisions across the team, and lead by building, not by org chart.
What You'll Do
Ship Product \& Platform Features
- Architect and evolve the personalization and data platform powering our consumer experiences
- Design high\-performance backend services in Go and full\-stack features in React \+ TypeScript, built for scalability, observability, and maintainability
- Build RAG pipelines, semantic search, and LLM\-powered product features, including embedding models, vector stores, reranking, and eval\-driven quality loops
- Drive CI/CD and infrastructure maturity via GitHub Actions, Terraform, and AWS
Build AI Into the Platform
- Design and ship multi\-agent systems using LangGraph, AutoGen, or custom orchestration that operate across the development lifecycle
- Break down our multi\-language codebase into AI\-addressable, agent\-ready modules: clean interfaces, well\-scoped context, documented patterns
- Own the agentic development scaffolding: tool integrations, MCP servers, and shared infrastructure that make every engineer on the team more effective
Multiply the Team's Output
- Use agentic coding tools (Claude Code, Codex, Cursor, or whatever's next) as co\-engineers, not autocomplete, and help the team do the same
- Build shared configurations, custom tool integrations, and workflow hooks that encode team conventions and eliminate repeated decisions
- Spot opportunities to automate engineering toil (test generation, PR summarization, migration scripts, dependency upgrades, documentation) and build the tooling to make it happen
- Track and share measurable productivity gains with engineering leadership
Raise the Engineering Bar
- Run pairing sessions, internal demos, and workshops that build real AI capability across the team
- Define practical AI engineering standards: prompt engineering practices, context window management, human\-in\-the\-loop thresholds, and eval frameworks
- Mentor mid\-level and junior engineers on how to build effectively with AI as a core skill
- Partner with Engineering Leadership to shape the AI adoption roadmap across the portfolio
What You Bring
Must\-Have
- An established AI\-native development practice: agentic tools are part of your daily workflow, with results to show for it. Faster delivery, fewer manual steps, higher output
- 6\+ years of software engineering experience with a track record of technical leadership on complex systems
- Expert in TypeScript (frontend and backend) and production\-grade Go for high\-performance services
- Strong React and modern frontend architecture experience
- Hands\-on experience with LLM APIs (Anthropic Claude preferred): prompt engineering, tool use, structured outputs, streaming, context management
- Solid understanding of RAG architecture: vector stores, chunking strategies, embedding models, reranking, eval loops
- Experience with CI/CD, Terraform, and AWS in a production engineering context
Nice\-to\-Have
- Experience building and shipping agentic systems in production: multi\-step tool\-calling agents, orchestration pipelines (LangGraph, LangChain, AutoGen, CrewAI, or custom)
- Familiarity with agentic coding CLI tools and workflow automation that encodes team patterns at the repo level
- Knowledge of MCP (Model Context Protocol) and experience building or integrating MCP servers
- Experience with code intelligence: AST parsing, static analysis, code graph construction
- Background in developer platform or internal tooling engineering
- Experience with eval frameworks: RAGAS, LangSmith, Braintrust, or custom evaluation infrastructure
- Familiarity with event\-driven architectures, message queuing (Kafka, SQS), and distributed systems patterns
- Experience with data platforms, ETL pipelines, or personalization systems at scale
- Active contributions to open source or technical community leadership
Mindset
- You treat repetitive engineering work as a problem to solve, not a cost of doing business
- Your first instinct when facing toil is: can an agent handle this?
- You stay current on model releases, agent design patterns, and emerging frameworks because you're genuinely interested, not because someone assigned it
- You balance velocity with rigor: you ship fast, think in systems, and don't create hidden debt
Show Us Your Work
We care about what you've built. In your application, please share GitHub repos, demos, writeups, or anything that shows your approach to AI\-native development. Side projects, open\-source contributions, and technical blog posts all count.
Compensation:
*This range reflects total cash compensation, which may include base salary only or base salary plus target bonus, depending on the role. Where eligible, equity may also be offered separately and not included below. Actual compensation varies based on location, experience, and qualifications.*
- Total Cash Compensation Range: $100,000 – $180,000 per year
Additionally, the following benefits are provided by Red Ventures, subject to eligibility requirements.
- Health Insurance Coverage (medical, dental, and vision)
- Life Insurance
- Short and Long\-Term Disability Insurance
- Flexible Spending Accounts
- Holiday Pay
- 401(k) with match
- Employee Assistance Program
- Paid Parental Bonding Benefit Program
- Flexible Paid Time Off (PTO): We believe time to rest and recharge is essential. That's why we offer a generous and flexible PTO policy. Full\-time employees accrue 20 days of PTO for a full calendar year annually, with an increase to 25 days after five years of service.
Who We Are:
Red Ventures is a global portfolio of high\-growth companies — spanning several U.S. businesses,a joint venture in the health services industry,and strategic investments in Europe. Their businesses include The Points Guy, Lonely Planet, Bankrate, the Allconnect Platform, RV Home Client Growth, RV Growth \& Transformation, Sage Home Loans Corporation, and more. Across the portfolio, Red Ventures businesses deliver seamless digital experiences for consumers, help Fortune 100 clients solve large\-scale digital growth challenges, and create world\-class experiences and opportunities for employees. Learn more at redventures.com and follow @RedVentures on LinkedIn and Instagram.
At Red Ventures, we believe diverse, inclusive teams are better. To help you better understand our core values and beliefs, we encourage you to watch this brief YouTube video: Our Belief Statements. This will give you insight into the principles that guide our work and our commitment to fostering an inclusive environment.
We offer competitive salaries and a comprehensive benefits program for full\-time employees, including medical, dental and vision coverage, paid time off, life insurance, disability coverage, employee assistance program, 401(k) plan and a paid parental leave program.
Red Ventures is an equal opportunity employer that does not discriminate against any employee or applicant because of race, creed, color, religion, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or any other basis protected by law. Employment at Red Ventures is based solely on a person's merit and qualifications.
We are committed to providing equal employment opportunities to qualified individuals with disabilities. This includes providing reasonable accommodation where appropriate. Should you require a reasonable accommodation to apply or participate in the job application or interview process, please contact [email protected].
If you are based in California, we encourage you to read this important information for California residents linked here.
At Red Ventures, we believe in real human connection. That's why we do not hire someone through text, social media, or email only. As part of the hiring process, you should expect live conversations with RV teammates before any offer is made. Also, keep an eye on the sender: we only use official @redventures.com email addresses at the portfolio level or business specific email addresses (e.g., @thepointsguy.com), not ones like "redventurescareer.com." We will never ask candidates to send money, buy equipment, or share financial account info during your journey with us. You can always find our open roles on redventures.com— if you receive a message that seems suspicious, please use redventures.com to verify the opportunity.
For more, the U.S. Federal Trade Commission has published helpful articles to help individuals learn more about protecting themselves from recruiter scams. If you think you've been targeted, feel free to report it to your local authorities. Stay safe out there!
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Questions about this Privacy Notice can be directed to [email protected]. Alternatively, you may raise any questions or concerns to your manager, HR Business Partner, or through the Privacy Team.
Salary Context
This $100K-$180K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Red Ventures, 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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($140K) sits 23% below the category median. Disclosed range: $100K to $180K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Red Ventures AI Hiring
Red Ventures has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $180K - $180K.
Location Context
AI roles in New York pay a median of $211,000 across 2,643 tracked positions. That's 5% 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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