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Join us at Entrust
At Entrust, we’re shaping the future of identity centric security solutions. From our comprehensive portfolio of solutions to our flexible, global workplace, we empower careers, foster collaboration, and build solutions that help keep the world moving safely.
Get to Know Us
Headquartered in Minnesota, Entrust is an industry leader in identity-centric security solutions, serving over 150 countries with cutting-edge, scalable technologies. But our secret weapon? Our people. It’s the curiosity, dedication, and innovation that drive our success and help us anticipate the future.
Position Summary
The Director, Business Development & Category Definition, Agentic Trust will turn an emerging customer problem into a recognized and commercially viable market category for Entrust. The role will define the category, validate priority buyers and use cases, secure lighthouse customers and partners, and create the evidence, commercial models and repeatable plays required to scale.
This is a hands-on market-making role, not a conventional field sales or alliance position. Working across Product, Architecture, Engineering, Marketing, Sales, Strategy and Executive Leadership, the Director will be the commercial voice of the market and shape how Entrust differentiates trust for enterprise AI agents across identity, delegated authorization, assurance, evidence, auditability, compliance and governance.
Core Outcomes:
- Credible category narrative, buyer problem definition and differentiated Entrust point of view.
- Prioritised market map covering use cases, buying centres, ideal customers, partners and alternatives.
- Lighthouse engagements that validate urgency, value, solution fit, willingness to pay and reference potential.
- Repeatable discovery, qualification, workshop, pilot, partner and executive-engagement plays.
- Fact-based input to product strategy, packaging, pricing, partnerships and investment choices.
Key Responsibilities:
- Category Definition & Market Creation
- Define the customer problem, category boundaries, buyer urgency, value drivers, alternatives and Entrust right to win.
- Refine the category narrative and terminology with customers, partners, analysts, Marketing and Product.
- Position Agentic Trust against adjacent markets including AI governance, agent security, non-human identity and trust infrastructure.
- Turn market evidence into recommendations on where we should lead, partner, integrate or not invest.
Market Discovery & Commercial Validation
- Run structured discovery to test use cases, personas, budgets, procurement paths, barriers and willingness to pay.
- Lead executive workshops and design-partner engagements that test pain, urgency, outcomes and commercial readiness.
- Prioritise opportunities by impact, repeatability, strategic fit, ecosystem leverage and speed to evidence.
Lighthouse Customers
- Originate and lead a focused set of lighthouse pursuits with account teams, from discovery through agreement.
- Define mutual success plans, measurable outcomes, learning goals, expansion paths and reference criteria.
- Codify winning use cases, objections, proof points and qualification standards for wider sales adoption.
Ecosystem & Partnerships:
- Prioritise hyperscalers, AI platforms, model providers, systems integrators, identity and security vendors, and industry alliances.
- Create joint value propositions, reference architectures, commercial frameworks and co-innovation or co-sell motions.
- Focus on partnerships that materially improve credibility, product relevance, distribution or customer access.
Product & Commercial Model Influence
- Convert buyer evidence into prioritised product, solution and integration requirements.
- Test packaging, pricing, services, partner economics and contracting models with the market.
- Define minimum sellable and scalable offers and inform build-partner-buy decisions.
Go-to-Market Incubation:
- Build the initial playbook: target accounts, personas, triggers, discovery, value hypotheses, competitive framing and qualification.
- Partner with Marketing on thought leadership, analyst engagement, demand creation and executive messaging.
- Coach strategic pursuits and establish clear hand-off criteria from incubation to scaled sales execution.
Operating Rhythm
- Maintain a scorecard covering learning velocity, lighthouse progress, evidence maturity, qualified pipeline and scale readiness.
- Run cross-functional governance and provide executive updates separating facts, hypotheses, risks and decisions required.
Measures of Success
Category traction
Customers, partners and analysts recognise a clear problem definition and differentiated Entrust point of view.
Market validation
Evidence confirms priority buyers, use cases, urgency, measurable value and willingness to invest.
Lighthouse progress
Strategic engagements advance through explicit learning, technical, commercial and reference milestones.
Commercial creation
Qualified opportunities and partner routes show credible paths to scalable revenue.
Repeatability
Proven plays, assets, qualification, packaging and hand-offs can be executed by field teams.
Strategic influence
Market evidence changes roadmap, partnership, packaging, pricing or investment decisions.
Required Experience & Capabilities
- 10+ years in business development, category creation, market development, strategic partnerships, product strategy or enterprise technology growth.
- Demonstrated success creating or scaling an emerging technology category, new market, platform proposition or ecosystem-led business.
- Experience leading ambiguous, zero-to-one initiatives and converting market learning into repeatable commercial motions.
- Proven ability to engage senior executives, shape new demand and secure sponsorship for first-of-kind initiatives.
- Experience structuring complex partnerships, design-partner programmes, co-innovation agreements or new commercial models.
- Experience in enterprise software, cybersecurity, identity, cloud, SaaS, AI or adjacent technology markets.
Core Capabilities:
- Category thinking: defines a market problem and builds a differentiated, externally credible point of view.
- Commercial judgement: converts interest into value, sponsorship, investment and qualified action.
- Market sensing: synthesises customer, competitor, partner, analyst, standards and regulatory signals.
- Executive communication: translates complex technical and commercial issues into decisions.
- Operating discipline: manages hypotheses, lighthouse pursuits, partners, dependencies and measurable outcomes.
- Technical fluency across AI agents, identity, authentication, authorisation, governance, assurance, cryptography, evidence and trust architectures.
Preferred Qualifications
- Experience with agentic AI architectures, non-human identity, delegated authorisation, AI governance, Zero Trust, interoperability or enterprise trust infrastructure.
- Experience with hyperscalers, systems integrators, AI platforms, major software providers, analysts or standards bodies.
- Experience in regulated industries such as financial services, insurance, government, healthcare or critical infrastructure.
- Bachelor’s degree in Business, Technology, Engineering, Computer Science or a related field; advanced degree preferred.
Leadership Profile:
A market maker rather than a caretaker: externally curious, commercially rigorous and comfortable operating before the category, product or buying process is fully formed. The successful candidate creates clarity from ambiguity, challenges weak assumptions, earns trust with executives and technical teams, and remains accountable for moving from insight to evidence to repeatable commercial outcomes.
At Entrust, we don’t just offer jobs – we offer career journeys. Here is what you can expect when you join our team:
- Career Growth: Whether you’re a budding developer or a seasoned expert, we’re invested in your professional journey. With learning-forward initiatives and exciting challenges, your growth is our priority.
- Flexibility: Life is all about balance. Whether you’re remote, hybrid, or on-site, we offer flexible options that fit your lifestyle.
- Collaboration: Here, your voice matters. Our teams thrive on sharing ideas, brainstorming solutions, and working together to build a better tomorrow.
We believe in securing identities—but it doesn’t stop there. At Entrust, we’re passionate about valuing all identities. Our culture is built on diversity, inclusion, and respect. From unconscious bias training for our leaders to global affinity groups that connect colleagues across the globe, we’re creating a community where everyone is encouraged to be themselves.
Ready to Make an Impact?
If you’re excited by the prospect of innovating, growing your career, and collaborating in a dynamic environment, Entrust is the place for you. Join us in making a difference. Let’s build a more secure world—together.
Apply today!
For more information, visit www.entrust.com. Follow us on, LinkedIn, Facebook, Instagram, and YouTube
Compensation Range:
The anticipated starting base pay for this position is: $136,032-$199,514 per year (in the primary posting location). This position is also eligible for variable pay via a sales compensation plan. These plans pay according to achievement level against sales targets and/or business objectives. Actual compensation will be determined based on geographic location, education, skills and experience and actual performance against the assigned sales compensation plan. In addition to your pay, Entrust offers eligible colleagues and their dependents comprehensive health and well-being programs which include medical, vision, dental, a generous 401(k) matching contribution, life and disability insurance, mental health coaching, virtual fitness programs, paid personal time off plus 12 paid holidays, parental leave and education reimbursement. Please speak with the recruiter for more details. Note: Benefit and Compensation programs are subject to eligibility requirements and other terms of the applicable plan or program. Entrust has the right to end, suspend or amend any of its plans at any time in whole or in part.For US roles, or where applicable:
Entrust is an EEO/AA/Disabled/Veterans Employer
For Canadian roles, or where applicable:
Entrust values diversity and inclusion and we are committed to building a diverse workforce with wide perspectives and innovative ideas. We welcome applications from qualified individuals of all backgrounds, and we strive to provide an accessible experience for candidates of all abilities.
If you require an accommodation, contact [email protected]*.*
Recruiter:
Richa Srivastava [email protected]
Salary Context
This $136K-$199K 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 Entrust Corporation, 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. Director-level AI roles across all categories have a median of $272,250. This role's midpoint ($167K) sits 21% below the category median. Disclosed range: $136K to $199K.
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.
Entrust Corporation AI Hiring
Entrust Corporation has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $199K - $199K.
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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