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About This Role
Description:
Sifted Logistics Intelligence (“Sifted”) is at the forefront of logistics optimization, providing a cutting\-edge SaaS platform and predictive analytics tools designed to revolutionize the way parcel shippers manage their operations.
Our platform goes beyond traditional consulting methods, empowering businesses to continuously calibrate, automate, and monitor their shipping spend, carriers, and contracts with unparalleled precision and insight.
We're driving a transformative shift in the logistics industry, redefining what's possible for our clients — and AI is at the core of that evolution.
At Sifted, we don't just use AI — we depend on it. Our proprietary SiftedAI platform delivers predictive intelligence and automation that enable businesses to make smarter, faster, and more confident decisions. Internally, every employee is expected to leverage AI to optimize how they think, create, and deliver. We believe mastering AI is fundamental to our speed, excellence, and competitive edge.
Be part of a company that created a category and is continuously building. If you get excited about defining market verticals and making an impact, Sifted is the place for you. Join us and be part of something extraordinary.
We're looking for a product\-minded data engineer who ships end\-to\-end features, not just pipelines. You'll own the full lifecycle of data and AI\-powered products — from pipeline to production — and work directly with customers, ops, and product to define and solve the right problems.
As a Product Engineer, you’ll help build the tools and automation that shippers and 3PLs rely on to run their business — owning problems end to end. You’ll collaborate closely with your team, product partners, domain experts, and clients to build a deep understanding of a domain problem, then work backwards to the right technical solution. You’ll use automation and AI as core tools in how you approach and deliver solutions that make a real difference for the shippers and 3PLs who depend on them.
- Build and maintain the data pipelines, automation, and product features that shippers and 3PLs depend on
- Partner with product, operations, domain experts, and customers to define problems and validate that what you build actually solves them
- Own problems end\-to\-end — from understanding the business need through designing, building, shipping, and supporting the solution in production
- Write and own your own tests and data\-quality checks, catching correctness issues before they reach customers
- Use AI and automation to accelerate how you build, while verifying and standing behind everything you ship
- Work across the stack — data, backend, and the customer\-facing surfaces — going wherever the problem leads
Requirements:
- Experience with a modern cloud data warehouse (e.g. Snowflake, BigQuery, Redshift) and its transformation tooling (e.g. dbt)\*
- Demonstrated success implementing CI/CD pipelines (e.g., GitHub Actions, Azure DevOps, Jenkins, BitBucket pipelines)
- 3\+ years of professional experience in software engineering or data engineering
- Designed or managed data pipelines handling millions of records daily
- Proven experience gathering requirements and working directly with stakeholders\*
- Experience deploying and maintaining systems in cloud environments (AWS, Azure, or GCP)
- Demonstrated ability and willingness to embrace AI as a core tool for problem\-solving and innovation
Nice to Have
- Hands\-on experience with C\# and/or React
- Background in parcel shipping or third\-party logistics (3PL)
What We Provide
- Well capitalized Private Equity investor focused on Growth
- Best\-in\-class compensation package and an unmatched comprehensive benefit program
- Flexibility to achieve and accomplish results
- Extremely collaborative work environment across teams to drive results
- Culture is built on how we improve people’s lives
- *Denotes an essential function/qualification of the role.*
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 Sifted, 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.
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.
Sifted AI Hiring
Sifted has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Overland Park, KS, US.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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
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