Interested in this AI/ML Engineer role at Textron Aviation?
Apply Now →About This Role
Back in 2012, we were a group of engineers and designers who decided we wanted to build things, so we did. Able started as an engineering and product hub building for a portfolio of early\-stage startups. We built many relationships while developing products that were thoughtful, effective, and genuinely useful. But, since then, we’ve grown… and so has our ambition.
Now, we’re entering our next chapter, defined by applied AI. AI is a powerful force in the end\-to\-end software development cycle, and we’re creating practices that allow us to deliver software fast and more effectively than traditional approaches, creating meaningful value for our partners. Today, our builder mindset is driving us to become an AI\-native organization across every function. We’re still evolving, and that’s part of the opportunity. If you want to build, learn, and tackle challenges alongside an ambitious team, let’s build together.
About the Role at Able
Able is an AI\-native product engineering firm. We partner with organizations that need to move faster, use AI more effectively, and build products that reflect how their teams and customers actually work. We work alongside our clients \- embedded, fast, and outcome\-obsessed \- across industries and stages of product maturity.
This role sits at the center of those engagements. The Principal Product Strategist, AI Engagement Leader will lead discovery, frame opportunities, align decision\-makers, define the path to value, and remain accountable as recommendations become product decisions and product decisions become shipped software. The role also helps shape how Able itself works: improving the use of AI across strategy, design, and delivery, and creating a repeatable product discipline that strengthens every engagement.
What Success Looks Like
- Clients view this person as a trusted senior partner who can make complex, ambiguous work feel clear and actionable.
- Able produces more consistent, rigorous discovery, product strategy, workflow analysis, and recommendation artifacts across engagements.
- AI opportunities are evaluated pragmatically \- with attention to user value, workflow readiness, data, governance, feasibility, evaluation, and change management.
- The current PM and design team receive stronger coaching, clearer standards, and better tools to do exceptional work.
- Able has a practical roadmap for the future product strategy and design practice, including capabilities, hiring needs, and AI\-enabled ways of working.
Experience
- 10\+ years of relevant experience in product strategy, product management, design strategy, consulting, agency work, startup leadership, or a closely related product leadership role; 10\+ years preferred.
- 2\+ years leading product and/or design work through direct management, functional leadership, practice\-building responsibility, or leadership of complex multidisciplinary engagements.
- Strong consulting acumen, including leading client discovery, facilitating workshops, shaping recommendations, and building credibility with senior stakeholders.
- Experience supporting pre\-sales and account growth: participating in early client conversations, framing engagement approaches, contributing to proposals or SOWs, estimating work, and presenting a clear point of view.
- Demonstrated ownership across the full product lifecycle, from ambiguous opportunity framing and discovery through build, launch, measurement, and iteration.
- Hands\-on experience designing, validating, or shipping AI\-enabled products, agents, automations, internal tools, or AI\-assisted workflows \- with practical judgment about where AI creates leverage and where it introduces risk or unnecessary complexity.
- Proficiency using AI applications to improve product work, including research, synthesis, workflow analysis, prototyping, requirements, documentation, and delivery practices.
- Experience partnering directly with product design, software engineering, and AI engineering teams; able to engage in technical feasibility, architecture, data, evaluation, and governance conversations without needing to be the primary engineer.
- Proven ability to translate messy and sometimes conflicting stakeholder input into a clear problem definition, prioritized opportunity set, and defensible recommendation.
- Comfort building lightweight business cases and articulating value using incomplete data, explicit assumptions, proxies, and outcome\-oriented metrics.
- Excellent written and verbal communication, including client\-facing synthesis briefs, opportunity maps, PRDs, roadmaps, and executive recommendation decks.
- Startup, consulting, agency, venture studio, or similarly entrepreneurial operating experience strongly preferred.
Responsibilities
Client and Engagement Leadership
- Build and maintain trusted client relationships, serving as a senior thought partner to product, business, technical, and executive stakeholders.
- Lead discovery on client engagements: stakeholder interviews, workflow and process analysis, opportunity framing, AI use\-case mapping, value articulation, and prioritized recommendations.
- Bring structure to ambiguous problem spaces by surfacing assumptions, clarifying decisions, making risks and tradeoffs visible, and creating shared alignment around the path forward.
- Facilitate executive workshops and working sessions that move teams from disconnected input to clear priorities, product direction, and next steps.
- Develop clear, persuasive written artifacts \- including briefs, opportunity maps, product strategy documents, PRDs, roadmaps, and client\-facing recommendation decks.
Product Ownership, Delivery, and AI Product Strategy
- Maintain product ownership through build and implementation: translating strategy into buildable work, supporting sprint planning and backlog decisions, and keeping delivery grounded in client outcomes.
- Partner closely with product design, engineering, and AI engineering to validate feasibility, shape product and architecture tradeoffs, and make practical sequencing decisions.
- Define outcome hypotheses, success measures, and learning plans; help clients make decisions that balance user value, business impact, feasibility, governance, and time\-to\-value.
- Help clients identify, evaluate, and design AI\-enabled products and workflows, including agentic experiences, automation opportunities, human\-in\-the\-loop decisions, evaluation approaches, privacy, security, reliability, and change\-management needs.
- Use AI as an extension of the product practice \- accelerating research, synthesis, prototyping, documentation, and delivery workflows while maintaining quality and judgment.
Pre\-Sales, Account Growth, and Practice Building
- Support pre\-sales efforts by joining early client conversations, helping frame opportunities, developing engagement approaches, contributing to proposals and SOWs, estimating work, and presenting Able's point of view.
- Identify opportunities to deepen client relationships and expand impact while protecting trust, delivery quality, and a clear understanding of scope and outcomes.
- Build and evolve Able's product strategy and design discipline: establish methods, standards, AI\-enabled workflows, artifacts, and operating practices that enable exceptional client work today and scale with the company over time.
- Provide functional leadership, coaching, and quality oversight for Able's current PM and design team; establish expectations for strong discovery, strategy, design collaboration, and delivery.
- Help define the future structure, capabilities, hiring needs, onboarding approach, and internal tooling roadmap for an AI\-native product and design practice.
- Contribute to the strategy and roadmap for Able's internal platform, tooling, and operating systems when those efforts will strengthen client delivery or the broader practice.
Able is powered by curious, thoughtful people who care about what they build and how they build it. We’re actively investing in our team through AI training, knowledge\-sharing, and hands\-on experimentation to ensure everyone grows alongside the technology.
This position is 100% remote within USA. Strong verbal and written communication skills in English are a requirement. As a team member, you can expect:
- To work 40 hours per week, and be available during normal business hours as needed.
- 18 days of PTO per year, observance of local holidays, and an annual break between Christmas and New Years.
- 100% of health, dental and vision insurance coverage.
- $50 monthly in reimbursements for AI tool subscriptions and costs.
- We provide the equipment.
About Able
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Able builds technology products in a portfolio model. We believe that people, teams, and processes are more important than the ideas themselves, so we’ve focused on bringing great people together, and investing in their growth.
We’ve built products in a variety of industries. Everything from media to finance to toys to healthcare. Sometimes we work with management teams to help their businesses grow faster or unlock value using technology. Other times we start or buy businesses outright. Each time, we look for opportunities to leverage technology built at the portfolio\-level to drive value faster.
Able is committed to inclusion and diversity and is an equal\-opportunity employer. All applicants will receive consideration without regard to race, color, religion, gender, gender identity, sexual orientation, national origin, disability, or veteran status.
This is but the beginning of a conversation we’d love to have with you.
Apply, and let’s get this adventure started!
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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Textron Aviation, 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Textron Aviation AI Hiring
Textron Aviation has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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