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About This Role
Technical Proposal Writer
Here at Siemens, we take pride in enabling sustainable progress through technology. We do this through empowering customers by combining the real and digital worlds, improving how we live, work, and move today and for the next generation. We know that the only way a business thrives is if our people are thriving. That’s why we always put our people first. Our global, diverse team is ready to support you and challenge you to grow in new ways. Who knows where our shared journey will take you?
Transform the everyday with us!
About Electrification \& Automation:
Headquartered in Wendell, NC, the Electrification \& Automation (E\&A) Business Unit offers the full range of energy distribution systems and solutions for all markets and through all sales channels. We provide reliable power distribution grids of today while investing in our Future Grids portfolio to create the power distribution grid of the future.
We are looking for a Technical Proposal Writer. This position will be based remotely within the United States.
You’ll make an impact by:
The Technical Proposal Writer is responsible for authoring, organizing, and finalizing customer\-facing technical proposals in response to RFQs and RFPs. This role serves as the primary proposal document owner, gathering technical, commercial, and schedule inputs from Project Developers, Application Engineers, and other internal stakeholders, and transforming them into clear, compliant, and professionally structured proposals. This is a non\-engineering role, and does not require ownership of designs, calculations, or schematics.
The role focuses on proposal drafting, document control, compliance, and consistency, ensuring that complex technical information is accurately represented while aligned with customer requirements, internal standards, and proposal strategy.
Key Responsibilities:
- The role focuses on proposal drafting, document control, process compliance, and consistency, ensuring that complex technical information is accurately represented while aligned with customer requirements, internal design standards, and proposal strategy.
- Authoring, organizing, and finalizing customer\-facing technical proposals in response to RFQs and RFPs.
- Serving as the primary owner of proposal documentation, ensuring clarity, structure, compliance, and overall quality.
- Synthesizing technical, commercial, and schedule inputs from cross\-functional stakeholders into clear, concise narratives.
- Reviewing customer requirements and developing compliance matrices to ensure all specifications and expectations are addressed.
- Managing proposal timelines, deliverables, and revisions to support on\-time, high\-quality submissions.
- Collaborating with Sales, Project Development, Engineering, and other stakeholders to align proposal content with strategy and customer needs.
You’ll win us over by having the following qualifications:
*Basic Qualifications:*
- 5\+ years of experience in engineering, proposal writing, or a related role within an EPC, engineering, manufacturing, utility, industrial, or similar corporate environment.
- Experience drafting technical proposals or bid documentation.
- Ability to analyze customer expectations, RFQs/RFPs, and proposal requirements.
- Ability to manage multiple proposals and deadlines in a fast\-paced environment.
- Willingness to travel up to 10%.
- Legally authorized to work in the United States on a continual and permanent basis without company sponsorship.
*Preferred Qualifications:*
- 3–5 years of administrative and proposal writing experience in an EPC, engineering, manufacturing, utility, or industrial corporate environment (advanced degree may substitute for 2 years of experience).
- Inputs and manage active leads in the Siemens opportunity management tool. Gather customer and vendor details and ensure completeness of opportunities prior to authorization for release
- Update active opportunities for win/loss or delay in award
- Ensure consistency with respect to timeline, scope, cost and validity between proposal documents and opportunities in web\-based tracker
- Proficiency with Microsoft Office tools (particularly Word) and strong overall PC skills; willingness to learn product configuration and pricing applications.
- Experience working with Project Developers, Application Engineers, or design teams in a pre\-sales environment.
- Experience developing compliance matrices and managing RFQ/RFP responses.
- Strong attention to detail with disciplined version control practices.
- Strong communication skills to effectively collaborate with internal and external stakeholders.
- Strong written communication, editing, and document organization skills You’ll benefit from:
About Siemens:
We are a global technology company focused on industry, infrastructure, transport, and healthcare. From more resource\-efficient factories and resilient supply chains to smarter buildings and grids, sustainable transportation, and advanced healthcare, we create technology with purpose—adding real value for customers.
Our Commitment to Equity and Inclusion in our Diverse Global Workforce:
We value your unique identity and perspective. We are fully committed to providing equitable opportunities and building a workplace that reflects the diversity of society while ensuring we attract the best talent based on qualifications, skills, and experiences. We welcome you to bring your authentic self and transform the everyday with us.
$81,753 $140,148 10%
Salary Context
This $81K-$140K range is above the median for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Siemens, 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 $166,983 based on 13,781 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $131,300. This role's midpoint ($110K) sits 34% below the category median. Disclosed range: $81K to $140K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Siemens AI Hiring
Siemens has 4 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Alpharetta, GA, US, Lebanon, OH, US, Wendell, NC, US. Compensation range: $75K - $188K.
Location Context
Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 require on-site attendance. Top AI hiring metros: Los Angeles (1,695 roles, $178,000 median); New York (1,670 roles, $200,000 median); San Francisco (1,059 roles, $244,000 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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