Interested in this AI/ML Engineer role at Ahead?
Apply Now →About This Role
AHEAD builds platforms for digital business. By weaving together advances in cloud infrastructure, automation and analytics, and software delivery, we help enterprises deliver on the promise of digital transformation.
At AHEAD, we prioritize creating a culture of belonging, where all perspectives and voices are represented, valued, respected, and heard. We create spaces to empower everyone to speak up, make change, and drive the culture at AHEAD.
We are an equal opportunity employer, and do not discriminate based on an individual's race, national origin, color, gender, gender identity, gender expression, sexual orientation, religion, age, disability, marital status, or any other protected characteristic under applicable law, whether actual or perceived.
*We embrace all candidates that will contribute to the diversification and enrichment of ideas and perspectives at AHEAD.*
The AI Adoption Lead is AHEAD's embedded human answer to the gap between AI deployment and realized value. This is a people\-first, business\-embedded transformation role, part change manager, part org designer, part coach, and part workforce strategist, dedicated to a specific business unit (BU). Reporting to the VP, Talent and Workforce Transformation, the AI Adoption Lead ensures that the business unit’s employees are genuinely ready to work alongside AI: with the right skills, redesigned roles, reimagined workflows, and the confidence to operate in a continuously evolving environment.
### Duties/Responsibilities
AI Change Strategy \& Adoption
- Partner with the OCM team to design and execute a BU\-level AI change strategy, moving employees from AI experimenters to AI accelerators
- Develop and run stakeholder engagement plans, including resistance mapping and influence strategies
- Build a network of champions and superusers within the BU to sustain adoption momentum
- Monitor adoption signals and adjust interventions based on real\-time uptake data
- Address employee anxiety and build psychological safety around AI\-augmented work
Role \& Job Redesign
- Conduct systematic task deconstruction across BU roles to identify what AI automates, augments, or creates
- Redesign job descriptions and role profiles to reflect new human–AI responsibilities
- Work with BU leaders to define new AI\-augmented job families and career pathways
- Identify roles at risk of displacement and proactively design transition pathways
- Partner with job architects and compensation teams to update role\-leveling frameworks
Capability \& Skill Building
- Assess BU\-level AI fluency and identify skill gaps across functions, levels, and workforce segments
- Commission or co\-design targeted learning programs — technical, workflow, and mindset
- Integrate AI capability\-building into daily workflows, not just stand\-alone training events
- Develop manager enablement specifically for leading AI\-augmented teams
- Track and report on capability uplift against BU transformation milestones
Strategic Workforce Planning
- Build a dynamic skills supply/demand picture for the BU accounting for humans and AI agents
- Identify future workforce requirements based on AI\-driven process changes (12–36 month horizon)
- Inform build/buy/borrow/automate decisions for critical capability gaps
- Support BU leaders in scenario planning for headcount and structure as AI scales
- Feed BU insights into enterprise\-wide strategic workforce planning processes
Organizational Design
- Assess whether the BU's current structure enables or inhibits AI\-augmented ways of working
- Advise on structural changes — spans, layers, team composition, and human–agent workflow design
- Support BU leadership in redefining management accountabilities as AI takes on operational tasks
- Identify and pilot new operating models, such as human\-in\-the\-loop decision flows
- Document and share org design patterns for replication across the enterprise
Culture \& Responsible AI
- Build a culture of active participation in AI transformation, not passive compliance
- Surface and address fear, mistrust, and equity concerns across BU employee groups
- Embed responsible AI practices, transparency, human validation, governance, into team norms
Manager Enablement
- Partner with the Talent Development team to equip managers to lead AI\-augmented teams, orchestrating humans and AI agents together, not just using AI tools themselves
- Run dedicated BU manager learning programs focused on judgment, coaching, and human–AI workflow oversight
Adoption Measurement \& Intelligence
- Own the BU's AI adoption measurement framework, design and instrument metrics, not just report on them
- Partner with eTech to ensure adoption data (usage, frequency, depth) is accessible and actionable
- Surface informal and shadow AI use within the BU and channel it into governed, productive adoption
- Track adoption health across workforce segments including tenure, function, generation, and role level
- Feed BU intelligence upward into the Hive Transformation Office to inform org\-wide strategy
### Education and Experience
Minimum Required:
- 8\+ years in change management, HR transformation, org effectiveness, or related people and OD roles
- Demonstrated experience leading large\-scale technology or digital transformation from the people side
- Strong grasp of change frameworks and ability to adapt them to AI contexts
- Experience in workforce planning, role design, or job architecture
- Ability to build trust and credibility with business unit leaders and frontline employees
- Data literacy with comfort using adoption metrics and workforce analytics to drive decisions
- Personal proficiency with AI tools and genuine enthusiasm for AI\-augmented work
Preferred:
- Experience embedded in a business unit (not just HR or CoE roles)
- Exposure to agentic AI, automation workflow design, or human\-in\-the\-loop process redesign
- Background in learning \& development or capability\-building at scale
- Knowledge of org design principles and structural assessment methodologies
- Experience with skills\-based talent models and dynamic workforce planning tools
- Industry knowledge aligned to the target business unit
- Certification or coursework in AI transformation leadership
$130,000 \- $150,000 a year
*The compensation range indicated in this posting reflects the On\-Target Earnings (“OTE”) for this role, which includes a base salary and any applicable target bonus amount. This OTE range may vary based on the candidate’s relevant experience, qualifications, and geographic location.*
Why AHEAD:
Through our daily work and internal groups like Moving Women AHEAD and RISE AHEAD, we value and benefit from diversity of people, ideas, experience, and everything in between.
We fuel growth by stacking our office with top\-notch technologies in a multi\-million\-dollar lab, by encouraging cross department training and development, sponsoring certifications and credentials for continued learning.
USA Employment Benefits include:
- Medical, Dental, and Vision Insurance
- 401(k)
- Paid company holidays
- Paid time off
- Paid parental and caregiver leave
- Plus more! See benefits https://www.aheadbenefits.com/ for additional details.
Use of AI:
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, assessing responses, or to capture recordings and create transcriptions or summaries during interviews. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans.
If you would like more information about how your data is processed, please refer to the Candidate Privacy Notice or contact us at \[email protected].
You may opt\-out of the review or analysis of your application and resume by AI tools by using the General Application. Please include the role you wish to apply for in the Additional Information field. You may also choose to opt\-out of recording and transcription at any time, including after joining an interview. Candidates will not be penalized for choosing to opt\-out.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
Salary Context
This $130K-$150K range is below the median 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 Ahead, 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($140K) sits 35% below the category median. Disclosed range: $130K to $150K.
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.
Ahead AI Hiring
Ahead has 8 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Remote, US, US. Compensation range: $150K - $300K.
Location Context
AI roles in Austin pay a median of $214,343 across 143 tracked positions.
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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