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About This Role
Description
ICF seeks a Training and Technical Assistance (T/TA) Coordinator to provide project management and oversight to a team of Grantee Specialists, Early Childhood Specialists, and other content specialists who provide high quality training and technical assistance (T/TA) that improves the capacity of Head Start and Early Head Start programs to meet or exceed the national Head Start Performance Standards. The West Head Start Training and Technical Assistance Center is part of the OHS National Training and Technical Assistance System including National Centers, and direct funding to recipients.
The TTA Coordinator will serve as a liaison to the ACF OHS West Regional office; support TTA staff to work with recipients in school readiness initiatives; professional development of TA staff; sustainability of non\-compliance and deficiency corrections; and other regional and OHS priorities as identified. The TTA Coordinator will also assist with strategies that develop collaboration between Head Start Programs, state and local agencies as well as other community partners supporting the HS/EHS grant recipients. Additional duties include maintaining ongoing communication with the Regional Program Director, the Regional Program Manager, COR, Supervisory Program Specialist, and Program and Grant Specialists (to include RO messaging, priorities, T/TA updates, new materials/resources). In addition, the TTAC will supervise the EC Manager, GS Manager, Administrative Assistant, and various other content specialists. The TTAC will also develop progress reports, goals and quality improvement plans to ensure high\-quality services are being provided. The TTA Coordinator will work in collaboration with the regional office to develop and implement a yearly TTA plan for recipients, ensuring implementation of all OHS/RO priorities. ICF is committed to ensuring that the position will be filled by the best professional dedicated to delivering excellence.
This position is home\-based within the West Geo\-Region states of Idaho, Oregon, Washington, and Alaska. Extensive travel within the region and to Washington, DC will be required to fulfill the position's requirements.
Basic Qualifications
The Regional Training and Technical Assistance Coordinator shall have:
- A minimum of a BA or BS Degree in management, human resources, education leadership or administration or related field from an accredited college or university. Master’s preferred.
- If the highest degree was awarded more than ten years ago, the resume should be specific regarding such events as courses, conferences, seminars attended or relevant work experience within the last 3\-5 years.
- 7\+ years’ experience, with ten years preferred, that includes at least three years with the provision of training and technical assistance; three to five years of progressive supervisory/management experience, staff development, and managing remote\-located staff.
- 1\-year experience coaching staff.
- 1\-year expertise in budget oversight, management and project quality control.
- 1\-year expertise developing, implementing and managing/improving complex, multi\-faceted projects.
- 1\-year experience working in close collaboration/coordination with multiple entities.
- 1\-year experience using data to improve the quality and effectiveness of TTA.
- 1\-year experience communicating, both orally and in writing, with the ability to adapt to various audiences and formats.
- 1\- year experience facilitating group discussions and presenting to range of audiences using a variety of formats to include virtual.
Key Responsibilities
The Regional Training and Technical Assistance (TTA) Coordinator shall provide the following in\-person or virtually as determined by the regional office.
- Support the regional office in developing short, intermediate, and long\-term training and technical assistance (TTA) planning that addresses OHS priorities and initiatives.
- Manage and coordinate TTA services to support high quality, responsive, and coordinated TTA services and contract deliverables, as well as timely communication and resolution of quality concerns and issues as coordinated with the COR and RO.
- Develop and implement quality assurance processes related to the accuracy of TTA reports and other deliverables.
- Develop and implement a system of ongoing supervision and coaching for all TTA staff that supports professional development and performance improvement.
- Ensure the completion of at least two formal observations of TTA personnel in each performance period to assess quality of TTA provided and determine professional development needs.
- Ensure the timely and accurate completion of OHS required reports, including but not limited to, the Annual Training Plan (ATP), Recipient Training and Technical Assistance Plan Agreement (RTTAPA), TTA activity reports, staffing roster and other reports described in the Schedule of Deliverables.
- Develop and implement systems for a coordinated TTA team approach to provide direct TTA to recipients.
- Develop and implement coordinated systems and processes to support the health and safety of children.
- Implement data use and reporting processes that inform and improve the quality, responsiveness, and effectiveness of TTA services.
- Participate in national, regional, state, and local work groups and meetings as directed by the OHS COR.
- Support emerging OHS initiatives and priorities.
Working at ICF
ICF is a global advisory and technology services provider, but we’re not your typical consultants. We combine unmatched expertise with cutting\-edge technology to help clients solve their most complex challenges, navigate change, and shape the future.
We can only solve the world's toughest challenges by building a workplace that allows everyone to thrive. We are an equal opportunity employer. Together, our employees are empowered to share their expertise and collaborate with others to achieve personal and professional goals. For more information, please read our EEO policy.
We will consider for employment qualified applicants with arrest and conviction records.
Reasonable Accommodations are available, including, but not limited to, for disabled veterans, individuals with disabilities, and individuals with sincerely held religious beliefs, in all phases of the application and employment process. To request an accommodation, please email Candidateaccommodation@icf.com and we will be happy to assist. All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations.
Read more about workplace discrimination rights or our benefit offerings which are included in the Transparency in (Benefits) Coverage Act.
Candidate AI Usage Policy
At ICF, we are committed to ensuring a fair interview process for all candidates based on their own skills and knowledge. As part of this commitment, the use of artificial intelligence (AI) tools to generate or assist with responses during interviews (whether in\-person or virtual) is not permitted. This policy is in place to maintain the integrity and authenticity of the interview process.
However, we understand that some candidates may require accommodation that involves the use of AI. If such an accommodation is needed, candidates are instructed to contact us in advance at candidateaccommodation@icf.com. We are dedicated to providing the necessary support to ensure that all candidates have an equal opportunity to succeed.
Pay Range \- There are multiple factors that are considered in determining final pay for a position, including, but not limited to, relevant work experience, skills, certifications and competencies that align to the specified role, geographic location, education and certifications as well as contract provisions regarding labor categories that are specific to the position.
The pay range for this position based on full\-time employment is:
$98,614\.00 \- $167,644\.00
Nationwide Remote Office (US99\)
Salary Context
This $98K-$167K 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 ICF, 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 ($133K) sits 20% below the category median. Disclosed range: $98K to $167K.
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.
ICF AI Hiring
ICF has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Reston, VA, US. Compensation range: $104K - $167K.
Remote Work Context
Remote AI roles pay a median of $156,000 across 1,221 positions. About 7% of all AI roles offer remote work.
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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