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About This Role
Who is Whip Around?
Whip Around is an award-winning, easy-to-use fleet maintenance software company that connects drivers, mechanics, and fleet managers to improve the uptime of vehicles and equipment. Founded in 2016 and headquartered in San Diego, CA, we’re on a mission to keep the world’s fleets moving.
Team culture is at the heart of our success, with personal improvement being just as important as company outcomes. We boast a senior leadership team with experience from numerous successful global businesses, and we continue to double down on our vision to bring simplicity and efficiency to fleet management and compliance — increasingly powered by data and artificial intelligence.
Why this role is important to us:
Reporting to the Director of Operations, the Operations Manager helps improve our internal AI tools and agents while supporting our R&D and Engineering teams with operational measurement, analytics and reporting. Working closely with the Director of Operations and our technical teams, this role helps investigate, implement, validate and continuously improve internal AI solutions, while supporting the data, systems and processes that keep them accurate and reliable.
A key part of the role is supporting Whip Around’s AI enablement efforts across customer-facing teams. Under the direction of the Director of Operations, the Operations Manager will help identify practical opportunities for AI and automation, turn promising experiments into reliable, repeatable solutions, and embed those tools into everyday workflows to save time and improve consistency. We’re looking for someone with hands-on experience helping teams put AI into practical use, improve the quality and reliability of AI-assisted workflows, and demonstrate measurable value from the solutions they implement.
What success looks like:
Operational Delivery & Support
- Support the Director of Operations in keeping workflows, resources and projects on track and aligned to team goals and developing initiatives.
- Track and report on operational KPIs against targets set with the Director, flagging performance gaps and helping put corrective action in place.
- Help manage operational budgets, systems and vendors to ensure we receive appropriate value from our investments.
- Identify new opportunities for AI within assigned areas of responsibility and help design and implement practical solutions..
AI Enablement & Use-Case Delivery
- Support the rollout and adoption of AI and automation tools across the team, providing guides, training and hands-on support people need to use them confidently and effectively.
- Help identify and prioritize practical AI use cases tied to clear outcomes — reducing manual effort, improving consistency and saving time or cost.
- Deliver early, practical “quick wins” in the first few months, then help establish repeatable processes for monitoring evaluating and improving solutions already in use..
- Help ensure live AI tools and agents have access to accurate, current and appropriate information so they remain reliable and trusted by the people using them.
- Maintain clear communication with stakeholders across the business to ensure AI agents and workflows remain aligned with current data, processes and organizational changes.
Process Improvement & Systems
- Map and improve core operational processes, identifying bottlenecks where automation or AI could reduce manual administration and sharing recommendations with the Director.
- Support R&D and Engineering AI initiatives and help teams improve reporting and data quality.
- Document processes, tools and standard operating procedures so that knowledge is captured, repeatable and easy for the team to follow.
- Manage and monitor new system integrations and coordinate new AI agent requests and intake as they arise.
Data, Reporting & Stakeholder Support
- Help develop and maintain meaningful R&D and Engineering metrics and reporting, using data from development and work-management systems to provide visibility into delivery, capacity, workflow health and operational trends.
- Build strong relationships with R&D and engineering to develop a deep understanding of their of their workflows, needs and challenges, and help design AI-assisted reporting that provides useful operational insights while supporting broader company reporting requirements.
- Conduct regular audits of key business data and operational data to ensure accuracy , working with relevant teams to identify and resolve sources of variance.
- Help prepare monthly and quarterly analytics for the Director and senior leadership, communicating findings clearly to both technical and non-technical audiences.
Collaboration & Team Support
- Work closely with the Director of Operations and partner with other Operations Managers to keep work coordinated across teams.
- Bring structure, timelines and follow-through to operational projects so they stay on track.
- Provide documentation, support and training for internal users.
Compliance & Quality
- Follow and help maintain responsible-use, data privacy and quality controls for AI and automation, so the tools we use stay safe and trustworthy.
- Support compliance with applicable data privacy regulations and partner with the Tech team on security where needed.
- Identify and escalate operational risks early and perform other duties as assigned to support projects and departmental needs.
What you’ll bring to the role:
Critical Competencies:
R&D Operations & Insights
- Partner with Engineering and R&D stakeholders to understand how work moves through the development lifecycle and identify opportunities to improve visibility, efficiency, and operational effectiveness.
- Develop and maintain meaningful reporting and metrics around R&D activity, capacity, delivery, workflow health, and team performance.
- Analyze operational and development data to identify trends, bottlenecks, inconsistencies, and opportunities for improvement.
- Translate data and operational findings into clear, useful insights for both technical teams and business stakeholders.
- Help improve the quality and consistency of development and ticketing data so that reporting and AI-generated insights are reliable.
- Support process improvement initiatives across R&D without adding unnecessary administrative burden to engineering teams.
- Comfortable working with structured business and operational data using SQL, spreadsheets, reporting platforms, or BI tools.
AI Enablement & Agent Development
- Hands-on experience using generative AI tools to solve business or operational problems.
- Experience building, configuring, training, testing, and refining AI agents or AI-assisted workflows.
- Strong working knowledge of Claude and the ability to build and iteratively improve Claude-based projects, agents, workflows, or other AI solutions.
- Ability to evaluate AI-generated outputs for accuracy, usefulness, consistency, and appropriate use of source context.
- Experience improving AI performance through prompt design, instructions, context management, source-data refinement, testing, and feedback.
- Understanding of hallucination risk and practical approaches for improving the reliability of AI-generated outputs.
- Experience designing AI workflows that incorporate human review or approval where appropriate.
- Ability to identify practical opportunities where AI can reduce manual work, improve reporting, surface useful insights, or support better decision-making.
- Comfortable experimenting with new AI capabilities and determining whether they provide meaningful business value.
Engineering & R&D Collaboration
- Experience working closely with software engineering, product, technical, or R&D teams.
- Ability to translate between technical and non-technical stakeholders and turn business needs into clear requirements, workflows, reporting, or process improvements.
- Strong stakeholder-management skills, including the ability to influence, coordinate, and move work forward without direct authority.
- Comfortable asking technical teams for information, clarification, or assistance while respecting competing priorities and engineering ownership.
- Able to independently investigate problems, gather available context, and clearly define what assistance is needed before escalating to Engineering.
- Comfortable operating as a collaborative peer within a technical environment rather than as a people manager.
Professional Skills
- Well organized and dependable, with the ability to manage multiple priorities and bring structure to ambiguous problems.
- Excellent interpersonal and written communication skills, with the ability to build productive working relationships across technical and business teams.
- Able to communicate technical concepts and analytical findings clearly to both technical and non-technical audiences.
- High level of integrity, attention to detail, and accuracy.
- Curious and comfortable learning unfamiliar systems, tools, and business processes.
- Comfortable working in a fast-moving environment with a bias toward practical, measurable results.
- Collaborative, resourceful, and comfortable working independently while knowing when to involve subject-matter experts.
Preferred and Differentiated Competencies:
- Experience with Atlassian products such as Jira and Confluence.
- Experience with Atlassian Rovo, Rovo agents, or similar AI-enabled work-management platforms.
- Experience with engineering analytics, developer productivity, or R&D reporting platforms such as Jellyfish or similar tools.
- Experience developing metrics or reporting around software delivery, engineering capacity, cycle time, throughput, planning, or development workflows.
- Experience with Claude and other major generative AI platforms.
- Experience with AI agent builders, workflow automation platforms, or low-code/no-code automation tools.
- SQL experience and familiarity working with relational or structured datasets.
- Experience with BI and visualization platforms such as Power BI, Tableau, Looker, or similar tools.
- Familiarity with APIs, integrations, or data pipelines sufficient to collaborate effectively with technical teams; hands-on software development experience is not required.
- Experience with process improvement, operational analytics, program coordination, or project management.
- Experience working in SaaS, software development, technology, or other fast-moving product environments.
- Familiarity with Agile development practices and software-development lifecycle concepts.
- Experience with AI governance, data governance, security, privacy, or compliance practices is a plus.
Requirements* Bachelor’s degree in Business, Operations, Analytics, Information Systems, Technology, or a related field, or equivalent practical experience.
- 3+ years of experience in business operations, R&D or engineering operations, analytics, business systems, program management, AI enablement, or a related field.
- Hands-on experience using generative AI tools to create or improve business workflows, reporting, analysis, or internal processes.
- Experience working cross-functionally with technical teams and translating operational needs into practical solutions.
- Experience working with operational data and using it to produce meaningful reporting, recommendations, or process improvements..
Location & Work Arrangement
- Location: Remote, with preference in US Central or Eastern time zones
- Hours: Full-time. Occasional early or late calls to overlap with New Zealand colleagues.
- Travel: Minimal.
Benefits
- Compensation: $100K - $115K USD, depending upon experience and location
- Unlimited PTO: Use your paid time off when you need it
- Subsidized Healthcare: A variety of subsidized medical plans to fit your needs
- Retirement Support: 401(k) matching to help you invest in your future.
- Family Friendly: Paid parental leave and caregiver support
- Growth & Development: Professional development plans and resources to support continuous learning.
Even if you don't meet every single requirement, we encourage you to apply - Whip Around is a place for learning, growth and building what's next. Following your application, a member of the Whip Around team will be in touch. If you have any accessibility requirements you can then let us know, so we can assist throughout the process. We participate in E-Verify and will verify employment eligibility via the I-9 Form.
Salary Context
This $100K-$115K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Whip Around, 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 $212,000 based on 11,857 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($107K) sits 49% below the category median. Disclosed range: $100K to $115K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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: $272,275; VP: $235,000.
Whip Around AI Hiring
Whip Around has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US. Compensation range: $115K - $115K.
Location Context
AI roles in Austin pay a median of $207,800 across 271 tracked positions. That's 3% below 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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