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About This Role
Transflo | Freight Technology SaaS About the Role Transflo is seeking a proven, hunter-minded Director of Sales to drive new business for our AI and insights portfolio, including Workflow AI (WFAI) and the Transflo Intelligence Suite. This is an individual contributor role for a seasoned enterprise seller who has already built a track record of landing complex, high-value deals in freight technology. You will lead pursuits at the largest carriers, brokers, factors, and LTL fleets. You will lead the strategic pursuits and often partner directly with Transflo’s C-suite on strategic pursuits, working alongside our Solution Engineers, Product, Marketing, and Leadership teams to win complex, enterprise-level deals. You will be as credible with a CEO as with a frontline operator, and relentless about turning pipeline into Closed Won revenue. You will be capable of opening doors at the top of the largest freight organizations, win transformational multi-year deals, and set the standard for how Transflo sells AI and insights to the market. What You’ll Do
- Deliver against an enterprise-level quota, per the comp plan, by selling WFAI, the Transflo Intelligence Suite, and the broader Transflo/ATI SaaS portfolio
- Build pipeline organically through disciplined outbound prospecting and a network of senior industry relationships
- Own strategic enterprise pursuits end to end, from first conversation through negotiation and close
- Map each target account and build multi-threaded relationships, from frontline operators to the C-suite
- Lead executive-level discovery that ties Transflo’s AI and insights solutions to each customer’s operational and financial priorities
- Run a team-selling motion with Solution Engineers, Subject Matter Experts, Product, Marketing, and Transflo executives, and bring in the C-suite at the right moments to move deals forward
- Shape complex deal structures, pricing, and multi-year agreements in partnership with Transflo leadership
- Include the full Transflo/ATI and partner solution set in the initial sales cycle (post-contract expansion belongs to Account Management and Customer Success)
- Provide market and competitive feedback to Product and Marketing to sharpen positioning and roadmap priorities
- Maintain a clean, accurate pipeline in Salesforce, with correct stages, dates, and commitment levels, to support executive reporting
- Travel 25 to 50% of the time for customer onsites, executive meetings, trade shows, and industry events, representing Transflo at the highest level, per the Transflo Employee Agreement
- Uphold Transflo’s core values, policies, and procedures
What We’re Looking For We are hiring A+ talent. The ideal candidate combines a proven record of enterprise results with polished professionalism, flawless follow-through, and relentless pursuit of the win. Minimum Qualifications
- 8+ years of consultative sales experience in a SaaS business model, including significant SaaS sales experience within the freight or transportation industry
- Proven record of consistent quota overachievement, with annual quotas typically ranging from $3M to $8M
- History of independently leading and closing new enterprise deals larger than $3M TCV
- Demonstrated ability to lead conversations with confidence and credibility at every level, from CEO to front line
- Hunter mentality, with a record of building pipeline organically
- Strong team-selling instincts and a track record of working effectively with executives, technical resources, and cross-functional partners
- Outstanding written and verbal communication skills, with tact, diplomacy, and executive presence
- Ability to manage a heavy workload in a highly competitive, highly visible environment
- Bachelor’s degree in Business, Marketing, Information Technology, or a related field
Preferred Qualifications
- Experience selling data, analytics, or market intelligence solutions, or other AI and logistics automation applications
- Established relationships within the Transport Topics Top 500, carriers, brokers, factors, and LTL fleets
- Familiarity with FMCSA requirements, telematics, and TMS software
Why Transflo You’ll sell differentiated technology in a category where AI is reshaping freight, with direct access to senior leadership and a meaningful voice in how we pursue and win the industry’s most important accounts. Physical demands: sit, stand, listen, hear, read, talk, use a computer and phone, and travel up to 50% of the time.
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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Transflo, 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 $211,950 based on 13,048 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,300.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Transflo AI Hiring
Transflo has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $188,000 across 2,316 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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