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Vacancy No VN433
Status Active
Location Remote US
Location Country United States
Location Region
Location City
Description Who are we? ATG is leading the transformation of the multi-billion dollar global auction industry. The company has grown rapidly over the last 7 years, to over $170m of revenue and over $80m of profits today. The company IPO’d in 2021 and has had a successful run both during the Covid period and after. ATG is early in our journey to establish a hugely influential global internet company that is revolutionizing one of the last industries in the world yet to move significantly online.
What do we do?
ATG connects thousands of auction houses, listing millions of items worth over $15 billion annually (and growing!), to buyers in over 170 countries around the world. These buyers generate over 100 million bids a year for our sellers, helping them achieve maximum value for the goods they sell. We help auctioneers sell everything from medieval swords to combine harvesters, paintings and decorative art through to classic cars, furniture, and Caterpillar forklifts. We do this through 8 marketplaces that leverage our online auction technologies. We make auctioneers more efficient, we make small companies big by bringing them access to millions of bidders they could never find on their own, and we bring them technology they could never afford alone. We bring bidders choice of unique and specialized items, convenience, and trust. And we do it all in a shared success model that accelerates growth of the circular economy and sustainable buying. With ATG, our buyers and sellers win but so too does the planet! We have ethical ambition and it’s something our employees value and is a key part of who we are….as well as being a high growth marketplace company with excellent competitive positioning!
Where are we now?
ATG achieved “first mover advantage” in each of the sectors we serve. As such, we have benefited from incredible network effects meaning that as we brought the most sellers, we attracted more buyers and as we brought more buyers, we then organically attracted more sellers. This symbiotic relationship has been pivotal to our profitable growth.
ATG established a vision that has 3 big strategic phases for how we will lead the transformation of the auction industry.
Phase 1 was assembling the critical mass of marketplaces and technologies that would serve as the foundation on which we would build. It enabled us to capitalize on the network effects and set ourselves apart from competitors.
We are now in Phase 2. Here, we are building out eCommerce basics, following the well-trodden path of online marketplace development-improved UI and UX, implementing best practice conversion funnel actions, improving trust, adding payment and shipping options, and much more. The goal is: Maximize Quality Bidders. On the seller side, we are focused on creating a one-stop-shop for auctioneers where they can get access to auction marketplaces where they find the best bidders, interconnectivity to enable them to rapidly reach the bidders on ANY of our marketplaces, digital marketing services, shipping services to improve their efficiency and customer experiences and payments to complete the fulfilment.
Phase 3 entails transformative ways to use these services to bring even more value to auctioneers and bidders alike. We can go into that if you progress through the interview process!
Who are we looking for?
As our Applied AI Lead, you’ll be the person who makes AI real at ATG — not as a strategy deck, but as working tools people use every day. This is a senior individual contributor role: you’ll build alongside the team, and you’ll set the standards, governance, and direction for how the organization builds and adopts AI.
Roughly half your time is hands-on delivery. You’ll size opportunities, design future-state flows, document requirements, build solutions using AI-assisted development tools, and deploy them across Operations, Finance, and HR. This matters to us: you can’t set credible standards for AI-built systems if you aren’t living under them yourself.
The other half is scope only you will carry. You’ll define how these systems are governed — who can build what, how tools are reviewed, who owns them after launch, and how we retire what no longer earns its place. You’ll partner with Legal and IT on the security and data-privacy questions that AI-built tools raise. You’ll lead adoption across the organization, including running internal build events that get colleagues outside this team creating their own solutions. And you’ll own the implementation of major AI platforms, most immediately our AI-powered customer support system: a significant cross-functional program spanning integration, change management, and rollout.
This role delivers against ATG’s Hands-Off Keyboard (HOK) mandate: our commitment to taking manual work out of the business.
You won’t have direct reports, but you will set the bar for how ATG builds with AI, and you’ll mentor the Automation Associate on this team.
What we offer you!
We’re large enough to have the resources and backing of a public company, yet still early enough in our journey that your impact will be seen, felt, and appreciated. Our culture is intentional, and our employee engagement scores consistently reflect a strong sense of belonging and shared purpose.
Here, your personal and professional growth goes hand in hand with ATG’s evolution. As we build toward a global brand that reshapes a legacy industry, you’ll be developing a track record of success in scaling high-value client experiences. Every employee is also a shareholder, so as we win together, you’ll share in that success.
Key Responsibilities What are the key responsibilities?
Hands-On Delivery Build and deploy automations yourself. You’ll size opportunities, design the future-state flow, document requirements, and ship working solutions using AI-assisted development tools — keeping a steady cadence of delivered projects alongside the program work below.
Maintenance & Support Keep the portfolio running. You’ll carry your share of the support load for deployed tools and set how the team handles it. You’ll also make the call on when a tool should be rebuilt, absorbed into a core system, or retired.
AI Support Platform Implementation Own the end-to-end implementation of ATG’s AI-powered customer support system: integration planning, cross-functional coordination, change management, and rollout across our marketplace brands. This is a major program and one of the single largest opportunities in front of us.
Governance & Standards Define how AI-built systems are governed at ATG including what gets built and by whom, how tools are reviewed and documented, who owns them after launch, and how they’re maintained or retired.
Legal, IT & Security Partnership Work with Legal and IT on how AI tools handle company and customer data, what’s permitted, and what safeguards are required. You’ll translate between the technical reality and the risk questions leadership needs answered.
Organizational Enablement Lead AI adoption beyond this team across all ATG employees. Provide training, internal hackathons and build events, office hours, and practical support that turns curious colleagues into capable builders.
Measurement & Reporting Own the portfolio view of what we’re delivering: usage, adoption, and savings realized against savings projected. Report it credibly to senior leadership, including where projects underperform and what we’re doing about it.
Key Requirements Who are you?
You’ve built and shipped internal tools or automations that provide value and that people use today.
You’ve led a significant cross-functional implementation or platform rollout end to end: stakeholder alignment, change management, go-live.
Fluent with AI-assisted development and low-code tools, and current on how fast the space is moving. Pragmatic about what these tools do well and clear-eyed about where they don’t belong.
You’ve handled governance, security, or data-privacy questions inside a real organization, and you can hold a productive conversation with Legal and IT rather than an adversarial one.
Strong commercial instincts: you can size a prize, defend an estimate, and tell leadership when something isn’t worth doing.
Credible with senior stakeholders and comfortable influencing without authority. You’ll lead a lot of people who don’t report to you.
An appetite for teaching and raising other people’s capability. A meaningful part of this job is making colleagues better at building.
Bonus: experience in marketplaces, e-commerce, or a multi-brand operating environment; exposure to customer support platforms or contact center technology.
Auction Technology Group is committed to fair and equitable compensation practices.
The pay range for this role is $135,000 to $150,000. Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to skill set, depth of experience, certifications, and specific work location. This may be different in other locations due to differences in the cost of labor.
The total compensation package for this position may also include annual performance bonus, stock, benefits and/or other applicable incentive compensation plans.
Employment Type Permanent
Duration
Business Name
Function Name Operations
Salary Context
This $135K-$150K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Auction Technology Group, 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 in Demand for This Role
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 11,866 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($142K) sits 33% below the category median. Disclosed range: $135K to $150K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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: $272,250; VP: $235,000.
Auction Technology Group AI Hiring
Auction Technology Group has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $150K - $150K.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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