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About This Role
remote type
Hybrid
locations
Portland, OR US
time type
Full time
posted on
Posted Yesterday
time left to apply
End Date: August 18, 2026 (12 days left to apply)
job requisition id
DT\-19115Inside the Role
As a Software Architect focused on AI\-enabled workflow architecture, you will play a key role in defining scalable automation platforms, agentic workflow patterns, model\-serving capabilities, and enterprise software integrations for complex engineering environments. You will design architecture building blocks that connect engineering systems, data sources, automation pipelines, and AI services into robust, maintainable, and governed end\-to\-end workflows. This role emphasizes system\-level thinking, software architecture depth, and close collaboration with product, platform, validation, and software engineering teams to improve engineering productivity and delivery quality.
This position operates within the Software Verification and Release organization and supports global vehicle platforms, software\-defined vehicle programs, and engineering automation initiatives.Posting Information
We provide a scheduled posting end date to assist our candidates with their application planning. While this date reflects our latest plans, it is subject to change, and postings may be extended or removed earlier than expected.
We Take Care of Our Team
Position offers a starting salary range of $117,000 – $150,000 USD
Pay offered dependent on knowledge, skills, and experience.
Benefits include annual bonus program; 401k company contribution with company match up to 6% as well as non\-elective company contribution of 3 \- 7% depending on age; starting at 4 weeks paid vacation; 13\+ calendar holidays; 8 weeks paid parental leave; employee assistance program; comprehensive healthcare plans and wellness programs; onsite fitness (at some locations); tuition assistance and volunteer paid time off; short\-term and long\-term disability plans.
What You Will Do
- Define reference architectures for AI\-enabled workflow platforms, agent orchestration, automation pipelines, and enterprise engineering integrations
- Design scalable software building blocks for workflow execution, model serving, tool integration, data exchange, observability, and lifecycle management
- Develop architecture patterns that combine deterministic automation pipelines with AI\-driven agents, human\-in\-the\-loop review gates, and traceable decision points
- Collaborate with product owners, software engineers, system engineers, data scientists, and platform teams to translate business needs into robust technical designs
- Establish API\-first, event\-driven, and cloud\-native integration approaches across engineering toolchains, enterprise systems, and automation services
- Apply software engineering best practices including architecture governance, coding standards, source control, security, CI/CD, automated validation, and operational monitoring
- Evaluate emerging AI, automation, and platform technologies and shape technical roadmaps for reusable workflow architecture capabilities
- Analyze and design large\-scale, distributed, and complex systems with a focus on maintainability, interoperability, reliability, security, scalability, and operational excellence
- Provide technical leadership, mentoring, and architectural guidance to implementation teams working on AI\-enabled workflows and engineering automation platforms
Knowledge You Should Bring
- Bachelor’s degree in Computer Science, Software Engineering, Computer Engineering, Electrical Engineering, or related technical field; advanced degree preferred
- 8\+ years of relevant experience in software architecture, distributed systems, enterprise application development, automation platforms, or engineering software systems
- Strong programming and software design experience in modern languages such as Python, Java, C\#, TypeScript, C/C\+\+, or similar
- Experience designing modular, maintainable, and scalable software components for workflow platforms, orchestration frameworks, enterprise integrations, or cloud\-native systems
- Understanding of AI\-enabled software systems, including agentic workflows, model\-serving patterns, prompt/tool orchestration, retrieval\-augmented generation, evaluation, and governance
- Experience with API design, event\-driven architecture, microservices, data pipelines, CI/CD, infrastructure as code, observability, secure development, and production operations
- Ability to integrate heterogeneous systems through REST APIs, event streams, workflow engines, data services, and enterprise integration patterns
- Familiarity with software lifecycle, requirements management, test management, DevOps, release management, traceability, and quality practices in engineering environments
- Strong analytical and communication skills with the ability to translate ambiguous business needs into clear architecture decisions and implementation guidance
Exceptional Candidates
- Experience architecting enterprise AI platforms, multi\-agent systems, autonomous workflow orchestration, or AI\-enabled engineering automation at production scale
- Experience with LLMOps/MLOps, model lifecycle management, model evaluation, safety guardrails, semantic validation, telemetry, and governance for AI\-enabled applications
- Experience designing event\-driven orchestration across requirements management, test management, CI/CD, issue tracking, simulation, HiL/SiL, or validation
- environments
- Automotive software, Software Defined Vehicle, embedded systems, test automation, virtual validation, ECU.test, IBM ELM/ETM, Jira, GitHub, or comparable engineering toolchain experience
- Ability to define reference architectures, technical roadmaps, platform standards, and reusable architectural patterns adopted across multiple teams and global engineering sites
- Demonstrated leadership in complex cross\-functional initiatives involving software architecture, data platforms, AI systems, cybersecurity, compliance, and change management
Behavioral Competencies
- Strong collaboration skills across software, validation, platform, infrastructure, data, and product teams
- Clear and structured communication of complex architecture topics to technical contributors and leadership audiences
- Self\-motivated, with a continuous improvement mindset and ability to operate in ambiguous, evolving technical environments
- Pragmatic focus on business value, delivery outcomes, operational reliability, and long\-term maintainability
\#LI\-DC1
\#LI\-HYBRID
Where We Work
This position is open to applicants who can work in (or relocate to) the following location(s)\-
Portland, OR US. Relocation assistance is not available for this position.Schedule Type:
Hybrid (4 days per week in\-office / 1 day remote). This schedule builds our \#OneTeamBestTeam culture, provides an unparalleled customer experience, and creates innovative solutions through in\-person collaboration.
At Daimler Truck North America, we recognize our world is changing faster than ever before. By listening to the needs of today, we’re building to solve with cutting\-edge solutions in sustainability and future driving technology across electric, hydrogen and autonomous. These solutions, backed by years of innovative success and achievement, continue DTNA’s legacy as the undisputed industry leader. Our evolving brand portfolio is second to none, including Freightliner Trucks, Western Star, Demand Detroit, Thomas Built Buses, Freightliner Custom Chassis, and Financial Services. Together, we work as one team towards our envisioned future – building a cleaner, safer and more efficient tomorrow for all.
That is what we are working toward \- for all who keep the world moving.
Additional Information
- Visa sponsorship will only be open to current Daimler Truck North America employees working under an existing U.S. Daimler Truck North America Visa
- All other applicants must be legally authorized to work permanently in the country the position is located in at the time of application
- Final candidate must successfully complete a criminal background check
- Final candidate may be required to successfully complete a pre\-employment drug screen
- Contractors, professional services, or other contingent workers should confirm with their local agency if they are eligible to apply for FTE positions
- EEO \- Disabled/Veterans
Daimler Truck North America is committed to workforce inclusion and providing an environment where equal employment opportunities are available to all applicants and employees without regard to race, color, sex (including pregnancy), religion, national origin, age, marital status, family relationship, disability, sexual orientation, gender identity and expression (including transgender and transitioning status), genetic information, or veteran status.
For an accommodation or special assistance with applying for a posted position, please contact our Human Resources department at 503\-745\-8982 or toll free 800\-206\-3369\. For TTY/TDD enabled call 503\-745\-2137 or toll free 866\-355\-6935\.
Salary Context
This $117K-$150K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Daimler Truck North America, 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($133K) sits 38% below the category median. Disclosed range: $117K to $150K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Daimler Truck North America AI Hiring
Daimler Truck North America has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Detroit, MI, US, Portland, OR, US. Compensation range: $150K - $150K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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