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About This Role
*OUR MISSION*
At Redwood, we empower our customers with lights\-out automation for their mission\-critical business processes.
*ABOUT US*
Redwood Software is the leader in full stack automation fabric solutions for mission\-critical business processes. With the first SaaS\-based composable automation platform specifically built for ERP, we believe in the transformative power of automation. Our unparalleled solutions empower you to orchestrate, manage and monitor your workflows across any application, service or server — in the cloud or on premises — with confidence and control.
Redwood’s global team of automation experts and customer success engineers provide solutions and world\-class support designed to give you the freedom and time to imagine and define your future. Get out of the weeds and see the forest, with Redwood Software.
*CORE VALUES*
One Team. One Redwood
Make Your Own Weather
Obsess over Customer Success
Work the Problem
Be Curious
Own the Outcome
Respect Each Other
*YOUR IMPACT*
As a Channel Marketing Campaign Manager you will be responsible for planning, executing, and optimizing partner and channel marketing campaigns with a focus on strategic partners such as SAP and AWS, leveraging data\-driven insights to drive engagement, pipeline growth, and revenue impact.
- Execute multi\-touch ABM demand\-generation programs and partner co\-marketing campaigns, including messaging, asset creation, content, paid media, webinars, and events/trade shows
- Build campaign and partner strategies, defining the best approach for each channel motion
- Support partners through enablement, events, and co\-marketing initiatives
- Collaborate with the alliances team to create and deliver partner enablement assets
- Manage partner BDF, events, and co\-marketing programs
- Work cross\-functionally with alliances, BDRs, sales, and marketing teams
- Utilize marketing platforms such as Salesforce, Outreach, Marketo, and 6Sense to support partner initiatives
- *Leverage Salesforce and reporting tools to evaluate campaign performance*
- *Analyze and measure campaign impact on engagement, pipeline, and influenced revenue*
- *Provide ongoing reporting on account engagement and campaign traction*
- *Use data and industry best practices to generate insights and optimize programs for target accounts*
*YOUR EXPERIENCE*
- 4\+ years of experience in channel marketing, integrated campaign management, or a related field
- Digital and email marketing experience, preferably in B2B enterprise software/SaaS
- Experience with CRM, ABM, and marketing automation platforms (e.g., Salesforce, 6Sense, HubSpot)
- Strong project management skills
- Experience managing partner initiatives with SAP preferred but not required
- Proven ability to execute innovative, multi\-channel demand generation programs
- Strong analytical, results\-oriented, and detail\-oriented mindset
- Demonstrated ability to set, track, measure, and communicate progress against goals
- Self\-starter capable of managing time and priorities with limited guidance
- High adaptability, flexibility, and accountability
- Ability to thrive in data\-driven, fast\-paced environments
*If you like growth and working with happy, enthusiastic over\-achievers, you'll enjoy your career with us!*
THE LEGAL BIT
Redwood is an equal opportunity employer. Redwood prohibits unlawful discrimination based on race, colour, religion, sex, gender identity, marital or veteran status, age, national origin, ancestry, citizenship, physical or mental disability, medical condition, genetic information or characteristics (or those of a family member), sexual orientation, pregnancy or any other consideration made unlawful by regional or local laws. We also prohibit discrimination based on a perception that anyone has any of those characteristics or is associated with a person who has or is perceived as having any of those characteristics. All such discrimination is unlawful and will have a zero tolerance policy applied to it.
Redwood will comply with all local data protection laws, including GDPR when it comes to the handling and processing of personal data. Should you wish for us to remove your personal data from our recruitment database, please email us directly at Recruitment@Redwood.com
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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Redwood Software 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 $166,983 based on 13,781 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $131,300.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Redwood Software Inc. AI Hiring
Redwood Software 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 $156,000 across 1,221 positions. About 7% 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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