Interested in this AI/ML Engineer role at Pallet?
Apply Now →Skills & Technologies
About This Role
About Pallet
Pallet is building AI Agents to transform logistics — a $12 trillion global industry. We've raised $50M from top investors, including General Catalyst, Bessemer Venture Partners, and Bain Capital Ventures. In under two years, we've achieved 700% revenue growth and are just getting started.
Our mission is to increase the efficiency of the global supply chain by automating the manual workflows that slow logistics teams down — from scheduling and appointment setting to data entry and load management. Our flagship platform provides end\-to\-end visibility, control, and optimization, while our newest product, CoPallet, introduces AI Agents that can understand and execute requests in real time, and integrate directly with customer systems.
As logistics providers look to generative AI to drive efficiency, many are turning to Pallet to lead the way. With deep industry expertise and cutting\-edge AI capabilities, we're positioned to build the next $10B company in logistics.
Join us and work alongside leaders from Google, DoorDash, YC, and more to shape the future of logistics tech.
$120,000 \- $240,000
*Full\-time / Onsite (5 days/week) / \~25\-30% Travel*
About the Role
As Pallet scales into larger, more complex enterprise and mid\-market customers, deployments become higher\-stakes: more stakeholders, evolving requirements, real change management, and real consequences if things break.
The Deployment Strategist owns customer deployments end\-to\-end, from discovery and workflow design through execution, go\-live, and long\-term success. You are the connective tissue between customers, Agent Delivery Engineers, Product, Sales, and Success. You ensure customers feel confident, informed, and supported, even as workflows, product capabilities, and internal processes continue to evolve.
This is a highly client\-facing, high\-ownership role for senior operators who thrive under pressure and ambiguity.
Why This Role Is Different
You won't inherit a playbook, you'll help build it. This role owns enterprise deployments end\-to\-end, translating messy real\-world operations into live AI agent workflows. You'll work directly with executives, shape how CoPallet scales, and play a defining role in how customers come to trust AI in mission\-critical systems.
How You'll Make an Impact
- Serve as the executive point of contact for customer deployments and long\-term success
- Lead customer discovery to understand workflows, constraints, and success criteria
- Translate complex business processes into executable AI agent workflows
- Partner with Agent Delivery Engineers to validate assumptions and execute technically
- Configure, test, and release agent configurations for customer use cases
- Own deployment plans, timelines, risks, and stakeholder communication
- Lead customer kickoffs, milestone reviews, QBRs, and executive updates
- Proactively manage scope changes, escalations, and expectation\-setting
- Improve and scale onboarding and deployment processes over time
- Travel for key customer milestones and kickoffs as needed (\~25%)
What Success Looks Like
- Customers have clearly defined, executable workflows aligned to their business reality
- Deployments stay on track despite ambiguity, changing requirements, or complexity
- Executives feel informed, confident, and trust Pallet as a long\-term partner
- Internal teams are aligned, accountable, and coordinated around customer outcomes
- Deployment processes become more repeatable and scalable without sacrificing quality
What You'll Bring
- 6\+ years in customer\-facing roles (Product, Engagement, Consulting, Founder, etc.)
- Strong technical acumen: able to reason about workflows, APIs, and system behavior
- Comfort operating in fast\-moving, ambiguous environments
- Excellent written and verbal communication skills
- Experience managing multiple concurrent, high\-stakes projects
- Familiarity with LLMs, APIs, JSON
- Strong Excel / PPT skills
- SaaS experience
- Basic SQL literacy
Nice to Have
- Technical degree (CS, Engineering, Math) or equivalent experience
- Experience with AI\-driven or workflow automation products
Location \& Logistics
- In\-person, based in our San Francisco office or NYC office
- Travel required for major customer milestones
- Interviewing: Remote with final in\-person round
Interview Process
- Chat with Recruiter (30 min)
- Chat with Member of the Team (45 min)
- Chat with Member of the Team (45 min)
- Chat with Hiring Manager (45 mins)
- Final Interview Loop (3 hours)
- Background \& reference checks
We move fast and keep candidates informed at every stage.
Compensation
The estimated salary range for this role is $120,000 – $240,000, depending on experience and skill set. In addition to base salary, we offer competitive equity, benefits, and growth opportunities. Final compensation will be determined based on a combination of factors, including experience, qualifications, and location.
Our Benefits
We invest in our people the same way we invest in our product: seriously.
- Health, Vision, and Dental benefits
- ️ Flexible PTO
- ➕ Life Insurance and Accidental Insurance
❤️* Short\-Term Disability Coverage
- Generous salary and equity for all staff
- 401k option; helping you save for the future
- Yearly learning and development stipend
- Commuter benefits for Bay Area employees
- Uber ride stipend if you ever have to work late in the office
- Remote office home stipend to get you comfy in your space
- Daily catered lunches provided
- ✈️ Onboarding trip to San Francisco HQ if you work remotely
- Monthly happy hours
Annual Company Offsites; our last one was in Palm Springs CA
Workplace Policy
We are an in\-person company. Our offices in San Francisco and New York City are where ideas get built, decisions get made, and the team gets stronger. We have invested in making them genuinely great places to spend your day, with catered lunches, monthly happy hours, and people who care about the work and each other.
Most of our team works in office five days a week. For select roles, we hire remotely across the U.S. Remote employees come to San Francisco for their first week to onboard and meet the team, and we cover the full trip. Most remote employees visit once a year after that, with all travel, lodging, and meals on us.
Every role is different. Travel expectations are always shared upfront in the job description and confirmed in your first call with us. No surprises.
Equal Opportunity
Pallet is an Equal Opportunity Employer. We make all employment decisions based solely on merit. We provide equal employment opportunities to all applicants and employees without discrimination on the basis of race, religion, color, national origin, gender (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender identity, gender expression, age, status as a protected veteran, disability status, or any other applicable legally protected characteristic. We prohibit any form of discrimination or harassment. This policy applies to all terms and conditions of employment, including hiring.
Candidate Privacy Notice
Please review Pallet's Candidate Privacy Notice here.
Accommodations
Pallet complies with the Americans with Disabilities Act (ADA), as amended by the ADA Amendments Act, and all applicable state or local laws. We will reasonably accommodate qualified individuals with a disability during the application process and throughout employment as required by law.
If you need any assistance or accommodations due to a disability, please contact us at [email protected].
Salary Context
This $120K-$240K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Pallet, 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. Disclosed range: $120K to $240K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Pallet AI Hiring
Pallet has 2 open AI roles right now. They're hiring across AI/ML Engineer, AI Agent Developer. Based in New York, NY, US. Compensation range: $200K - $240K.
Location Context
AI roles in New York pay a median of $211,000 across 2,643 tracked positions. That's 5% above 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.