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About This Role
Dayforce is a global human capital management (HCM) company headquartered in Toronto, Ontario, and Minneapolis, Minnesota, with operations across North America, Europe, Middle East, Africa (EMEA), and the Asia Pacific Japan (APJ) region.
Our award\-winning Cloud HCM platform offers a unified solution database and continuous calculation engine, driving efficiency, productivity and compliance for the global workforce.
Our brand promise \- Makes Work Life Better™ \- Reflects our commitment to employees, customers, partners and communities globally.
About the opportunity
The Dayforce Tax and Payments (DTP\+) Product Engineering team is building the industry's next generation of Tax and Financial Services — a cloud\-native, microservices\-based platform on Azure that is replacing a decade\-old monolithic system and handling hundreds of billions of dollars in tax liability and fund movement annually.
This is not a role for someone planning to adopt AI tooling someday. We are looking for engineers who are already working AI\-natively — developers who reach for Copilot, Claude, or an LLM API the same way they reach for a compiler. You use AI to write, review, test, and reason about code every day. That is the baseline. What we want to build on top of that baseline is deep platform engineering skill, strong fundamentals, and the kind of technical judgment that comes from having shipped real things.
As a Senior Software Developer on a DTP\+ scrum team, you will design and deliver platform features across the Collections, Payments, Customer Profile, Agency Compliance, and eFile services. You will work directly with Architects, Product Managers, and peer developers to shape technically sound, well\-engineered solutions for a platform that North American employers and regulators depend on.
If you are a senior or lead engineer who takes pride in clean code, thinks carefully about system design, and is genuinely excited about the shift to AI\-augmented software delivery — we want to talk.
What you'll get to do
- Design and deliver highly scalable, event\-driven microservices features across the DTP\+ platform — from data ingestion through fund movement and compliance filing
- Work AI\-natively every day — use AI coding tools (GitHub Copilot, Cursor, Windsurf, or equivalent) as a primary productivity layer for feature development, code review, and debugging
- Integrate LLM APIs and AI patterns where they add genuine product value — including calling Azure OpenAI or equivalent APIs, building prompt chains, and applying RAG patterns to real features
- Contribute to the team's AI practices — share what works, help teammates level up on AI\-assisted testing and code review, and bring new patterns to the scrum team's workflow
- Drive design and code reviews, ensuring solutions are clean, testable, performant, and consistent with platform conventions
- Write high\-quality unit, integration, and regression tests — including AI\-generated test suites where appropriate
- Mentor fellow developers on software engineering fundamentals, clean code principles, and AI\-augmented development practices
- Build working proof\-of\-concepts and make concrete technology recommendations to improve scalability, maintainability, and quality
- Participate in Product discussions to advise on and influence the DTP\+ roadmap
- Take ownership of development initiatives end\-to\-end — design, implement, test, ship, and support
Skills and experience we value
AI\-Native Development — Must Have
These are not aspirational. We expect candidates to demonstrate these in day\-to\-day engineering work:
- Daily, hands\-on use of AI coding tools — GitHub Copilot, Cursor, Windsurf, or equivalent — as a first\-class part of your development workflow (code generation, refactoring, debugging, documentation)
- Experience integrating LLM APIs (Azure OpenAI, OpenAI, Anthropic, or equivalent) into real software features in a product or professional context
- Working knowledge of RAG design patterns and prompt engineering applied to actual software problems — not just familiarity from courses or demos
- Familiarity with tool\-calling and MCP (Model Context Protocol) integration patterns and how agentic workflows compose with existing backend services
- AI\-assisted testing practices — using AI for test case generation, PR review automation, or debugging complex runtime behavior
Software Engineering Fundamentals
- Strong object\-oriented design and programming skills in C\# / .NET / .NET Core (primary) or Java; deep knowledge of SOLID principles, clean code, and maintainable system design
- Proficiency with SQL on modern relational databases; ability to write performant queries, design schemas, and reason about data integrity
- Hands\-on experience applying design patterns, writing comprehensive unit and integration tests, and profiling / optimizing performance
- Strong understanding of microservices architecture — service boundaries, inter\-service communication, data ownership, eventual consistency
Cloud, Infrastructure, and Platform
- Production experience building and deploying services on Microsoft Azure — including Azure Kubernetes Service (AKS), Azure Service Bus, Azure DevOps, Azure Functions, or equivalent services
- Hands\-on experience with Apache Kafka or Confluent Cloud for event\-driven, asynchronous messaging in a distributed system
- Docker and Kubernetes — containerizing services, writing Helm charts or Kubernetes manifests, and deploying to managed clusters
- NoSQL databases — production experience with MongoDB, Azure Cosmos DB, or equivalent; understanding of document modeling, indexing, and consistency trade\-offs
- CI/CD pipelines as code using Azure DevOps, GitHub Actions, or equivalent — automated builds, tests, deployments, and quality gates
- Experience designing and implementing REST and/or gRPC APIs with appropriate authentication, versioning, and contract hygiene
Delivery and Collaboration
- Strong agile delivery skills — active contributor in sprint ceremonies, owns stories end\-to\-end, flags blockers early
- Excellent communication skills — able to explain technical decisions clearly to both engineers and non\-engineers
- Disciplined self\-starter who works effectively independently and within a close\-knit scrum team
- Strong analytical skills and a systematic, evidence\-driven approach to problem\-solving
Education
- Bachelor's or Master's degree in Computer Science, Software Engineering, or a related technical field (or equivalent practical experience)
What would make you really stand out
- Frontend experience with Angular, React, or Node.js
- GraphQL API design and implementation
- gRPC and protobuf schema design
- ElasticSearch / OpenSearch for indexing and query
- Behavior\-driven testing with SpecFlow or similar BDD frameworks
- Experience with NuGet packaging and library distribution
- Fintech, payroll tax, or financial services domain experience
Why This Role
- You will work on real infrastructure at scale — the DTP\+ platform processes tax liability and fund movement for thousands of North American employers
- The team is actively transitioning to AI\-native engineering — your experience will accelerate the team, not just support it
- Greenfield microservices on Azure, Kafka, and Kubernetes — the architecture is modern and the decisions are yours to influence
- Senior engineers here have a voice in product direction, architecture, and engineering culture
What’s in it for you
Dayforce is fueled by the diversity of our talented employees. We are an equal opportunity employer and consider and embrace ALL individuals and what makes them unique. We believe our employees should be happy and healthy, with peace of mind and a sense of fulfillment.
We encourage individuals to apply based on their passions.
Dayforce employees and their families are eligible to participate in the following benefits programs: medical, dental, vison, and life insurance. Dayforce employees are also eligible to participate in a 401k plan (plus match) and a Global Employee Stock Purchase Plan. Employees also receive unlimited Time Away From Work (in lieu of accrued vacation time), 10 paid US holidays, up to 80 hours of paid sick time and 17 weeks of paid parental leave, subject to the terms of the applicable policy or program.
With a commitment to community impact, including volunteer days and our charity, Dayforce Cares we provide opportunities for you to thrive both in your career and personal life. Our focus is not just on your job but on supporting you to be the best version of yourself.
About the Salary Ranges
Please note that the salary range mentioned in this job description should serve simply as a guide. The final compensation offered may vary based on a variety of factors, including bonuses and/or incentives, or a candidate’s experience, skills, budget and location. Our company is committed to providing a fair, equitable, and competitive package that reflects the value an individual brings to the organization.
Fraudulent Recruiting
Beware of fraudulent recruiting. Legitimate Dayforce contacts will use an @dayforce.com email address. We do not request money, checks, equipment orders, or sensitive personal data during the recruitment process. If you have been asked for any of the above, or believe you have been contacted by someone posing as a Dayforce employee, please refer to our fraudulent recruiting statement found here: https://www.dayforce.com/be\-aware\-of\-recruiting\-fraud
Dayforce actively monitors all job applications to ensure authenticity. Submissions determined to be fraudulent or misleading will be declined from the recruitment process
\#LI\-Remote
Salary Context
This $95K-$169K range is in the lower quartile 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 Dayforce, 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. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($132K) sits 27% below the category median. Disclosed range: $95K to $169K.
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.
Dayforce AI Hiring
Dayforce has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US. Compensation range: $169K - $169K.
Location Context
AI roles in Austin pay a median of $215,300 across 523 tracked positions. That's 8% 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
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