Interested in this AI/ML Engineer role at Amentum?
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Amentum is a global leader in advanced engineering and innovative technology solutions, trusted by the United States and its allies to address their most significant and complex challenges in science, security and sustainability. Our people apply undaunted curiosity, relentless ambition and boundless imagination to challenge convention and drive progress. Our commitments are underpinned by the belief that safety, collaboration and well\-being are integral to success. Headquartered in Chantilly, Virginia, we have approximately 50,000 employees in more than 70 countries across all 7 continents.
Amentum is seeking a results\-driven and innovative Business Development Solution Architect with expertise in Artificial Intelligence (AI) and emerging technologies. In this role, you will provide strategic and technical leadership in the integration of advanced AI and related technologies into pursuits and proposals for Department of Defense (DoD) customers.
The Solution Architect will ensure that tailored, mission\-oriented solutions are seamlessly integrated into capture and proposal efforts, aligning closely with customer requirements. You will work in collaboration with internal capture teams, proposal teams, and technical experts, while playing a critical role in influencing customer engagements. This is a remote position with the ability to travel to customer sites and Amentum locations as needed. This position is US Remote telework and requires US Citizenship.
Key Responsibilities:
- Capture and Proposal Integration:
+ Collaborate with Amentum capture and proposal teams to embed innovative AI solutions into proposals, ensuring alignment with customer priorities and mission objectives.
+ Partner with capture managers to analyze RFPs, identify technical gaps, and design mission\-driven AI and enterprise solutions that enhance Amentum’s competitiveness.
+ Develop proposal narratives, themes, and technical content that highlight cutting\-edge AI and innovative technologies while addressing customer requirements.
- Solution Development and Design:
+ Define and develop customized AI\-based solutions for DoD programs, focusing on enhancing mission effectiveness, operational efficiency, and technological innovation.
+ Integrate AI/ML and emerging technologies into enterprise solutions, with an emphasis on ensuring their strategic impact to meet customer needs.
+ Align internal resources with solution development efforts, ensuring synergy between business development functions, consulting teams, and technological expertise.
- Customer Engagement:
+ Act as a trusted technical advisor for DoD customers, fostering strong relationships and developing a detailed understanding of customer requirements, goals, and challenges.
+ Communicate the business and operational value of proposed solutions to customer stakeholders, ensuring alignment with their strategic objectives.
+ Participate in customer meetings and engagements to refine solutions in real\-time, addressing customer inquiries and concerns.
- Technology Advancement and Application:
+ Research, evaluate, and recommend emerging technologies, with a focus on AI, machine learning, advanced analytics, and automation relevant to DoD missions.
+ Identify and innovate use cases that leverage AI systems to optimize mission\-critical capabilities for readiness, decision\-making, and operational performance.
Knowledge, Skills, and Abilities:
- Demonstrated experience in driving customer engagement in relation to technical solution design.
- Proven ability to lead and integrate enterprise and innovative AI\-based solutions into capture and proposal efforts.
- Deep understanding of AI/ML technologies, including their practical application in enterprise\-level systems to optimize mission\-critical functions.
- Ability to assess and integrate technical solutions with operational and mission priorities effectively.
- Exceptional verbal and written communication skills, with the ability to clearly articulate complex technical solutions to both technical and non\-technical audiences.
- Strong interpersonal skills to build and maintain relationships with DoD customers, as well as cross\-functional internal teams.
- Demonstrated ability to influence and lead discussions with senior decision\-makers and stakeholders.
Minimum Requirements:
- Bachelor’s degree in Business Administration, Computer Science, Systems Engineering, Information Systems, or a related technical field.
- Typically, 10 years of experience working within the DoD market, including solution development and large\-scale proposal integration for government contracts.
- Experience with digital technologies, data analytics, automation, and technology integration within DoD prinnograms and operations.
- Experience with DoD acquisition processes and systems, with a past track record of success in federal government capture and proposal environments.
Preferred:
- Master’s degree in a technical or business discipline is highly preferred.
- Prior experience filling a key technical or strategic role in the design and submission of winning proposals valued over $100M for DoD pursuits.
- Expertise in developing and delivering complex enterprise solutions within DoD environments.
- Strong knowledge of government contracting solutions, including mission\-critical sustainment, modernization, and innovation initiatives.
- Experience collaborating with business development, proposal, and technical teams to deliver compliant, high\-quality proposals that align with customer needs.
Compensation Details:
200 \- 230K Base with Bonus
The compensation range or hourly rate listed for this position is provided as a good\-faith estimate of what the company intends to offer for this role at the time this posting was issued. Actual compensation may vary based on factors such as job responsibilities, education, experience, skills, internal equity, market data, applicable collective bargaining agreements, and relevant laws.
Benefits Overview:
Our health and welfare benefits are designed to support you and your priorities. Offerings include:
- Health, dental, and vision insurance
- Paid time off and holidays
- Retirement benefits (including 401(k) matching)
- Educational reimbursement
- Parental leave
- Employee stock purchase plan
- Tax\-saving options
- Disability and life insurance
- Pet insurance
*Note: Benefits may vary based on employment type, location, and applicable agreements. Positions governed by a Collective Bargaining Agreement (CBA), the McNamara\-O'Hara Service Contract Act (SCA), or other employment contracts may include different provisions/benefits.*
Original Posting:
Amentum anticipates this job requisition will remain open for at least three days, with a closing date no earlier than three days after the original posting. This timeline may change based on business needs.
Amentum is proud to be an Equal Opportunity Employer. Our hiring practices provide equal opportunity for employment without regard to race, sex, sexual orientation, pregnancy (including pregnancy, childbirth, breastfeeding, or medical conditions related to pregnancy, childbirth, or breastfeeding), age, ancestry, United States military or veteran status, color, religion, creed, marital or domestic partner status, medical condition, genetic information, national origin, citizenship status, low\-income status, or mental or physical disability so long as the essential functions of the job can be performed with or without reasonable accommodation, or any other protected category under federal, state, or local law. Learn more about your rights under Federal laws and supplemental language at Labor Laws Posters.
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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At Amentum, 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 in Demand for This Role
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 $178,940 based on 11,900 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $160,000.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Amentum AI Hiring
Amentum has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Remote, US, Colorado Springs, CO, US. Compensation range: $165K - $165K.
Remote Work Context
Remote AI roles pay a median of $169,035 across 1,817 positions. About 16% 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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, 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 $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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