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About This Role
If you are looking for a career at a dynamic company with a people\-first mindset and a deep culture of growth and autonomy, ACV is the right place for you! Competitive compensation packages and learning and development opportunities, ACV has what you need to advance to the next level in your career. We will continue to raise the bar every day by investing in our people and technology to help our customers succeed. We hire people who share our passion, bring innovative ideas to the table, and enjoy a collaborative atmosphere.
Who we are:
ACV is a technology company that has revolutionized how dealers buy and sell cars online. We are transforming the automotive industry. ACV Auctions Inc. (ACV), has applied innovation and user\-designed, data driven applications and solutions. We are building the most trusted and efficient digital marketplace with data solutions for sourcing, selling and managing used vehicles with transparency and comprehensive insights that were once unimaginable. We are disruptors of the industry and we want you to join us on our journey. Our network of brands include ACV Auctions, ACV Transportation, ClearCar, MAX Digital and ACV Capital within its Marketplace Products, as well as, True360 and Data Services.
At ACV we focus on the Health, Physical, Financial, Social and Emotional Wellness of our Teammates and, to support this, we offer:
- Multiple medical plans including a high deductible, low cost health plan
- Company\-sponsored (paid) Short\-Term Disability, Long\-Term Disability, and Life Insurance
- Comprehensive optional benefits such as Dental, Vision, Supplemental Life/AD\&D, Legal/ID Protection, and Accident and Critical Illness Insurance
- Generous paid time off options, including uncapped vacation days, the greater of 3 paid sick days or in accordance with the applicable state or local paid sick leave law, 6 paid company holidays, 2 floating holidays, parental leave, bereavement leave, jury duty leave, voting leave, and other forms of paid leave as required by applicable law or regulation
- Employee Stock Purchase Program with additional opportunities to earn stock in the Company
- Retirement planning through the Company’s 401(k)
Who we are looking for:
ACV Auctions is hiring an Principal Architect, Product Security to secure how we build and ship AI, as part of the Product Security team. ACV's marketplace runs on AI that customers stake real money on: computer vision models that grade vehicle condition, pricing models that inform lending decisions, and LLM features across our products. You will define how that surface gets protected, and how our engineers use AI coding assistants and agents without trading away security. You will help engineering deliver secure applications across ACV's marketplace, protecting sensitive dealer, consumer, vehicle, and payment data.
Company\-wide security architect for AI systems and the applications built around them. Defines ACV's target\-state secure architecture, reference architectures, and standards for machine learning and LLM\-powered systems, and guides the highest\-risk AI and application designs across engineering. A P6 architecture\-track role alongside Principal Engineer on the Product Security Career Ladder. Company / multi\-year scope; owns AI and application security architecture and standards; sets direction on the most consequential design decisions.
Focus areas: this role spans AI/ML security and application security. It is a technical architecture role. ACV's AI Governance function owns policy, risk registers, and regulatory alignment; this role owns the technical controls and architecture that make those policies real in production systems.
What you will do:
- Actively and consistently support all efforts to simplify and enhance the customer experience.
- Define ACV's target\-state secure architecture and reference patterns for AI/ML systems: LLM features, retrieval pipelines, agentic workflows, and the computer vision models behind vehicle condition and pricing.
- Protect the integrity of ACV's vision\-based condition and pricing pipeline against adversarial inputs and manipulated or AI\-generated imagery, partnering with fraud and inspection teams on detection and image\-provenance controls.
- Set secure\-by\-default standards adopted across engineering for AI development: prompt injection defense, output handling, tool and agent permissioning, and model and training\-data supply chain security.
- Threat model and review the highest\-risk AI and application designs, applying frameworks such as MITRE ATLAS and the OWASP Top 10 lists for LLM and Agentic Applications.
- Own the security architecture for AI\-assisted engineering: coding assistants, MCP servers, and autonomous agents, with guardrails that preserve developer velocity across an API\-first engineering organization.
- Stand up ACV's AI security testing capability: adversarial testing and red\-teaming of models and LLM features, evaluation harnesses, and runtime guardrails, making deliberate build\-vs\-buy decisions.
- Advise engineering and security leadership on multi\-year AI security strategy, and partner with AI governance to translate policy into enforceable technical controls.
- Scale AI security expertise across engineering, including through ACV's Security Champions program; mentor Staff and Principal engineers; and represent ACV's AI security architecture externally.
- Perform additional duties as assigned.
What you will need:
- Ability to read, write, speak and understand English.
- Bachelor's degree in a related field, or commensurate experience.
- 12\+ years' of security experience, 15\+ years' without degree, including deep application security architecture.
- Hands\-on GenAI/LLM security work required, demonstrated through production experience or a verifiable body of work: AI red\-team engagements, published research or tooling, open\-source contributions, or AI security competition results.
- 2\+ years of production AI security experience preferred.
- Security architecture: owns the enterprise secure\-architecture vision for AI systems and application security.
- AI/ML security depth: LLM application threats (prompt injection, insecure output handling, data leakage through retrieval, excessive agency in agents and tools) and model\-level threats (poisoning, evasion, extraction, malicious pre\-trained models).
- Hands\-on technical fluency: reads and writes code (Python preferred) and has personally used AI security tooling (e.g., Garak, PyRIT, promptfoo, or equivalent) rather than only evaluating vendors.
- Frameworks: working fluency with the OWASP Top 10 for LLM Applications, the OWASP Top 10 for Agentic Applications, MITRE ATLAS, and NIST AI RMF, and the ability to turn them into standards engineers actually follow.
- Standards and patterns: defines reference architectures and paved\-road standards, including for AI\-assisted development.
- Influence: aligns engineering and ML leadership to the target architecture.
- Application security (commensurate with level): OWASP Top 10, secure code review, threat modeling, and SAST/DAST/SCA tooling (e.g., Snyk, Checkmarx, GitHub Advanced Security, Burp Suite).
- Cloud security (commensurate with level): cloud\-native security on AWS, Kubernetes, and infrastructure\-as\-code; familiarity with ML platforms and inference infrastructure (e.g., SageMaker, Bedrock) a plus.
- Comfort working in a fast\-paced, cloud\-native environment with clear written and verbal communication.
- Illustrative credentials (a plus, not required): SABSA or equivalent architecture credentials, CCSP or a cloud security specialty. A demonstrated body of AI security work (research, tooling, red\-team findings, AI CTF results, open\-source contributions) carries more weight than any certification; the AI security credential market is not yet mature.
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Our Values
Trust \& Transparency \| People First \| Positive Experiences \| Calm Persistence \| Never Settling
At ACV, we are committed to an inclusive culture in which every individual is welcomed and empowered to celebrate their true selves. We achieve this by fostering a work environment of acceptance and understanding that is free from discrimination. ACV is committed to being an equal opportunity employer regardless of sex, race, creed, color, religion, marital status, national origin, age, pregnancy, sexual orientation, gender, gender identity, gender expression, genetic information, disability, military status, status as a veteran, or any other protected characteristic. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. If you have a disability or special need that requires reasonable accommodation, please let us know.
For information on our collection and use of your personal information, please see our Privacy Notice.
No immigration or work visa sponsorship provided for this position.
Compensation: The compensation range for this position is listed in the "Job Details" section at the bottom of this posting. Please note that final compensation will be determined based upon the applicant's relevant experience, skill set, location, business needs, market demands, and other factors as permitted by law.
Salary Context
This $200K-$250K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At ACV Auctions, 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($225K) sits 5% above the category median. Disclosed range: $200K to $250K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
ACV Auctions AI Hiring
ACV Auctions has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $250K - $250K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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