Interested in this AI/ML Engineer role at Bloomreach?
Apply Now →Skills & Technologies
About This Role
Bloomreach is building the world’s premier agentic platform for personalization.We’re revolutionizing how businesses connect with their customers, building and deploying AI agents to personalize the *entire* customer journey.
- We're taking autonomous search mainstream, making product discovery more intuitive and conversational for customers, and more profitable for businesses.
- We’re making conversational shopping a reality, connecting every shopper with tailored guidance and product expertise — available on demand, at every touchpoint in their journey.
- We're designing the future of autonomous marketing, taking the work out of workflows, and reclaiming the creative, strategic, and customer\-first work marketers were always meant to do.
And we're building all of that on the intelligence of a single AI engine — Loomi AI — so that personalization isn't only autonomous…it's also consistent.From retail to financial services, hospitality to gaming, businesses use Bloomreach to drive higher growth and lasting loyalty. We power personalization for more than 1,400 global brands, including American Eagle, Sonepar, and Pandora.
We are looking for a forward\-thinking, AI\-savvy Global Proposal Response Manager to revolutionize our RFP and proposal process within our Go\-To\-Market (GTM) Enablement team. If you view RFPs not as an administrative chore, but as a strategic puzzle to be solved using cutting\-edge technology, creative storytelling, and data\-driven insights, you’ll fit right in.
### The Role: What You’ll Do
As our Global Proposal Response Manager, you will own the end\-to\-end proposal lifecycle across our global markets. You will partner closely with international Account Executives, Solution Engineers, Product teams, and Executives to build compelling, winning responses for world\-class brands.
This isn't a traditional, reactive proposal role. Reporting to the Director of GTM Enablement, you will have the opportunity to define what modern, global proposal management looks like at Bloomreach. You will act as both a process architect and a strategic advisor. Crucially, you will look beyond individual submissions to extract data and trends, providing insights to cross\-functional leadership that directly influence our global product and commercial strategies.
### Key Responsibilities
- Global Workflow Architecture: Evaluate, design, and optimize our global GTM proposal processes. Establish scalable frameworks for speed and accuracy—defining clear international hand\-offs, optimizing template libraries, and embedding AI naturally into the team's daily sprint cadence.
- Strategic Qualification: Lead the initial intake process and facilitate rigorous Go/No\-Go assessments to ensure our global sales teams are dedicating resources to high\-value, winnable enterprise opportunities.
- Leadership Insights \& Reporting: Analyze proposal data, win/loss trends, and common product gaps surfaced during the RFP process. Synthesize these findings into actionable, high\-visibility insights for cross\-functional leadership (Product, Marketing, and Sales Executives) to drive strategic decisions.
- End\-to\-End Project Management: Own the global proposal roadmap. Define timelines across multiple time zones, assign deliverables, and keep cross\-functional stakeholders accountable and aligned under tight deadlines.
- AI\-Driven Content Crafting: Serve as the power\-user and global champion for our proposal tech stack. Use AI to generate, refine, and optimize responses, while maintaining Bloomreach’s unique global brand voice.
- Stakeholder Collaboration: Partner directly with global Sales, Product, Legal, and Security teams to extract expert knowledge and translate complex technical capabilities into clear, value\-driven business benefits.
### What We Are Looking For (Requirements)
- Experience: 3–5\+ years of experience managing RFPs, RFIs, and security questionnaires within the Enterprise B2B SaaS space, with a proven track record of handling comprehensive, multi\-product solutions on an international scale.
- Analytical \& Strategic Mindset: Ability to see the "big picture" data within proposals. You know how to track response metrics and communicate complex trends clearly to executive leadership.
- Process Builder Mindset: Proven experience building or transforming workflows from scratch. You know how to balance "speed to submit" with high\-quality output, and you are comfortable piloting new processes globally, gathering feedback, and iterating quickly.
- Tech\-Forward Mindset: Deep comfort (and excitement) working with AI\-driven proposal tools (experience with Von is highly preferred). You view AI as a co\-pilot, not a threat.
- Agile \& Global Adaptability: You thrive in fast\-paced environments, can manage multiple high\-stakes deadlines simultaneously across global regions, and can pivot quickly when priorities shift.
- Master Communicator: Exceptional writing, editing, and storytelling skills. You can turn dense, highly technical enterprise architecture into a compelling, value\-driven business narrative.
- Influence Without Authority: Strong interpersonal skills with a proven ability to corral busy cross\-functional stakeholders (Product, Security, Legal, and Sales) across diverse geographic locations and drive projects to completion.
### Bonus Points
- Hands\-on experience with Conveyer, Loopio or similar RFP tooling.
- APMP Certification (Foundation or Practitioner level): Demonstrates mastery of global industry standards for proposal management and response optimization.
- PMP (Project Management Professional): Proven ability to manage complex, multi\-stakeholder dependencies and drive high\-stakes projects to the finish line under tight deadlines.
- PMI\-ACP (Agile Certified Practitioner): Strong alignment with our agile mindset—comfortable running fast\-paced response "sprints" and adapting quickly to shifting priorities.
- Familiarity with broader sales and enablement tools (e.g., Salesforce, Von, Slack).
More things you'll like about Bloomreach:
---------------------------------------------
### Culture:
- A great deal of freedom and trust. At Bloomreach we don’t clock in and out, and we have neither corporate rules nor long approval processes. This freedom goes hand in hand with responsibility. We are interested in results from day one.
- We have defined our 5 values and the 10 underlying key behaviors that we strongly believe in. We can only succeed if everyone lives these behaviors day to day. We've embedded them in our processes like recruitment, onboarding, feedback, personal development, performance review and internal communication.
- We believe in flexible working hours to accommodate your working style.
- We work virtual\-first with several Bloomreach Hubs available across three continents.
- We organize company events to experience the global spirit of the company and get excited about what's ahead.
- We encourage and support our employees to engage in volunteering activities \- every Bloomreacher can take 5 paid days off to volunteer\*.
- The Bloomreach Glassdoor page elaborates on our stellar 4\.4/5 rating. The Bloomreach Comparably page Culture score is even higher at 4\.9/5
### Personal Development:
- We have a People Development Program \- participating in personal development workshops on various topics run by experts from inside the company. We are continuously developing \& updating competency maps for select functions.
- Our resident communication coach Ivo Večeřa is available to help navigate work\-related communications \& decision\-making challenges.\*
- Our managers are strongly encouraged to participate in the Leader Development Program to develop in the areas we consider essential for any leader. The program includes regular comprehensive feedback, consultations with a coach and follow\-up check\-ins.
- Bloomreachers utilize the $1,500 professional education budget on an annual basis to purchase education products (books, courses, certifications, etc.)\*
### Well\-being:
- The Employee Assistance Program \- with counselors \- is available for non\-work\-related challenges.\*
- Subscription to Calm \- sleep and meditation app.\*
- We organize ‘DisConnect’ days where Bloomreachers globally enjoy one additional day off each quarter, allowing us to unwind together and focus on activities away from the screen with our loved ones.
- We facilitate sports, yoga, and meditation opportunities for each other.
- Extended parental leave up to 26 calendar weeks for Primary Caregivers.\*
### Compensation:
- Restricted Stock Units or Stock Options are granted depending on a team member’s role, seniority, and location.\*
- Everyone gets to participate in the company's success through the company performance bonus.\*
- We offer an employee referral bonus of up to $3,000 paid out immediately after the new hire starts.
- We reward \& celebrate work anniversaries \- Bloomversaries!\*
*(\*Subject to employment type. Interns are exempt from marked benefits, usually for the first 6 months.)*
Excited? Join us and transform the future of commerce experiences!
*If this position doesn't suit you, but you know someone who might be a great fit, share it \- we will be very grateful!*
*Any unsolicited resumes/candidate profiles submitted through our website or to personal email accounts of employees of Bloomreach are considered property of Bloomreach and are not subject to payment of agency fees.*
\#LI\-Remote
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 Bloomreach, 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. Mid-level AI roles across all categories have a median of $165,000.
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.
Bloomreach AI Hiring
Bloomreach has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US.
Location Context
AI roles in Austin pay a median of $215,300 across 523 tracked positions. That's 8% 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.