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About This Role
Space is a warfighting domain. True Anomaly seeks those with the talent and ambition to build the technology that secures it.
OUR MISSION
True Anomaly delivers decisive capabilities for space superiority. We build autonomous spacecraft, advanced payloads, mission software, and space\-based interceptors — enabling the U.S. and its Allies to secure the space environment and counter threats from the ultimate high ground.
OUR VALUES
- Be the offset. We create asymmetric advantages with creativity and ingenuity.
- What would it take? We challenge assumptions to deliver ambitious results.
- It’s the people. Our team is our competitive advantage and we are better together.
YOUR MISSION
At True Anomaly, we’re building the next generation of space defense. The way we build, deliver, and scale is itself a source of advantage, and the AI and infrastructure that everything else runs on is one of the most important force multipliers in the company. We are investing in platform engineering as a core operating capability, not a side experiment, and this role exists to set the technical direction that makes that investment pay off.
As the Principal Platform Engineer, AI \& Infrastructure, you will be the most senior individual contributor and the most trusted AI technologist on the platform engineering team. This is not a research role. It is a role about direction and leverage. You will steer how True Anomaly builds and operates with AI, help set the vision for how the company uses AI to deliver space superiority, and bring the enterprise to the cutting edge of agentic engineering. And while AI and infrastructure are your center of gravity, you will be a technical leader across the whole of platform engineering, helping shape decisions on developer experience, CI/CD, DevOps, and compute infrastructure the same way you shape AI platform decisions, and supporting every team in the org, including data.
The scope is broad by design. Your work will range from shaping the technical roadmap for the platform engineering org, to guiding the high\-level architecture and design of the platforms and infrastructure that will enable these capabilities for the company, to advising teams across the business on how to put AI to work. You will ultimately support every team in platform, including infrastructure, AI, and data, helping set technical direction on AI providers and architectures, on DevOps and CI/CD best practices, on developer experience, and on the compute and infrastructure decisions the platform depends on. You will do this in concert with the Staff engineers and teams who own and build these systems, and you will set the standards that the rest of the platform team builds against.
Beyond strategy and high\-level architecture, you will be the person we deploy against the hardest, least\-defined technical problems in the company, where the business need is critical and the path to a solution is wide open. These problems call for a new system to manage something central to how we operate. You will parachute in, frame the problem, and design and deliver the platform that solves it.
The platform engineering org, reporting to the Chief Security Officer, works across the enterprise, from engineering and product to marketing and finance. You will report to the Chief Security Officer or the VP of Platform Engineering, with direct visibility to executive leadership and the opportunity to shape how a fast\-growing defense technology company operates with AI and infrastructure.
As the technical anchor for the team, you’ll bring a breadth of cross\-discipline experience across AI platforms, cloud infrastructure, DevOps, SRE, systems design, and technical decision\-making. You will partner with senior leaders across the organization, exercise cross\-organizational influence to align teams on a shared technical direction, and work through significant ambiguity to establish technical requirements and deliver results. You will set the technical bar for how AI and infrastructure are built at True Anomaly, and coach and mentor the engineers around you to meet it.
RESPONSIBILITIES
- Help shape and set the technical vision and multi\-quarter roadmap for how True Anomaly builds and operates with AI and the platforms and infrastructure that enable these capabilities, partnering with leadership and the infrastructure team to steer the direction of the company’s systems in service of space superiority.
- Parachute into the hardest, most ambiguous technical problems across the enterprise, where the business need is critical but the implementation is undefined, and get hands\-on designing and delivering the platforms and systems that solve them.
- Set high\-level system design and architecture direction across the platform, spanning AI, infrastructure, and data, and bring the enterprise to the cutting edge of agentic engineering. You will do this in concert with the teams and Staff engineers who own these systems, reviewing architecture, helping course\-correct, and raising the technical bar rather than designing in isolation.
- Play a leading role in DevOps, CI/CD, developer experience, and compute infrastructure decisions and best practices, just as you do in AI, helping the infrastructure and data teams establish paved\-road patterns that scale across the enterprise.
- Guide technical evaluation and selection of AI providers, platforms, and tooling, helping the team make build\-vs\-buy decisions based on capability, compliance requirements, and speed to value.
- Set the standards and paved\-road patterns that the platform engineering team builds against, and help drive their adoption across teams.
- Serve as the company’s most trusted AI technologist: advise engineering, product, and business functions on how to apply AI, and help set the vision for how AI is used across the enterprise.
- Partner with engineering, security, and IT teams to ensure AI and infrastructure deployments meet government security and compliance requirements across data classification levels.
- Act as the technical anchor and mentor for the engineers on the team, setting a high engineering bar and multiplying the people around you.
- Stay current with the rapidly evolving AI landscape and bring informed recommendations on new models, tools, and capabilities that could accelerate the company.
QUALIFICATIONS
- 12\+ years of software engineering, DevOps, SRE, or cloud engineering experience, with a track record of setting technical direction across multiple teams or domains, owning roadmaps, and making architectural decisions that others build on.
- Deep expertise in AI platforms and agentic engineering, paired with strong, hands\-on command of cloud infrastructure, DevOps, CI/CD, developer experience, and compute, sufficient to set direction and course\-correct across the platform.
- A track record of taking ambiguous, high\-stakes problems from zero to a delivered system, including framing the problem when the requirements and implementation are undefined.
- Strong general software background, with real experience building, deploying, and operating production systems on cloud. You understand a professional software development lifecycle end to end, and you understand what constitutes maintainable code.
- Hands\-on experience with large language model APIs, including prompt engineering, tool use, retrieval\-augmented generation (RAG), and agent frameworks (e.g. CrewAI, Pydantic AI), and a point of view on where agentic engineering is headed.
- Strong experience with the foundations modern engineering teams rely on: infrastructure\-as\-code (e.g. Terraform), CI/CD and deployment standards, developer platforms and shared tooling, and the cloud networking and identity underneath them.
- Experience deploying and operating production workloads across cloud environments (AWS, Azure, GCP), including the networking, identity, and CI/CD foundations they depend on.
- Experience using AI\-powered development tools to deliver production code and automate routine work, and the ability to leverage AI as a force multiplier for yourself and the teams around you.
- Demonstrated ability to work across a broad technical surface area, context\-switching between AI platforms, infrastructure, application development, and platform architecture, and to work across teams to understand requirements and seek consensus.
- Strong communication skills and the ability to translate technical AI concepts for non\-technical audiences, and to influence executive leadership on technical direction.
- Self\-directed and comfortable operating with high autonomy in a fast\-paced environment where priorities shift and the playbook is being written in real time.
- US Citizenship and ability to obtain and maintain a Top\-Secret security clearance.
PREFERRED SKILLS AND EXPERIENCE
- Experience with data platforms, pipelines, or big\-data infrastructure, and the ability to help set technical direction for the data team.
- Experience deploying AI or ML systems in government, defense, or regulated environments with security and compliance constraints.
- Familiarity with government cloud environments (AWS GovCloud, Azure Government) and authorization frameworks (FedRAMP, NIST 800\-53, IL4/IL5\).
- General experience with various AI technologies, like model harnesses, agentic coding tools, skills, MCPs, RAG, embedding pipelines, etc.
- Experience with LLM eval frameworks, OpenTelemetry tracing, and other model measurement and observability frameworks.
- Experience with containers and orchestration, service mesh, and artifact/registry management.
- Experience building knowledge retrieval systems, embedding pipelines, or enterprise search infrastructure.
- Background in developer tooling, platform engineering, or internal platform teams at high\-growth technology companies.
- Active U.S. Secret or Top\-Secret security clearance.
COMPENSATION
Base Salary: Denver \- $255,000 \- $355,000, Long Beach \- $270,000 \- $375,000, Washington, DC \- $270,000 \- $375,000
Equity \+ Benefits including Health, Dental, Vision, HRA/HSA options, PTO and paid holidays, 401K, Parental Leave
*Your actual level and base salary will be determined on a case\-by\-case basis and may vary based on the following considerations: job\-related knowledge and skills, education, location, and experience.*
ADDITIONAL REQUIREMENTS
- Work Location — Denver, CO, Long Beach, CA, or Washington, DC. While we observe a hybrid work environment, you will need to be onsite as the business needs require. Onsite requirements are subject to change, particularly when work on classified systems is required. On an average week, you can expect to spend at least 3 days per week in office. \#LI\-Onsite
- Work environment — Fast\-paced, mission\-critical environment supporting national security space operations
- Physical demands — Occasional travel to government sites, classified facilities, launch facilities, or partner locations may be required
This position will be open until it is successfully filled. To submit your application, please follow the directions below.
*To conform to U.S. Government space technology export regulations, including the International Traffic in Arms Regulations (ITAR), you must be a U.S. citizen, lawful permanent resident of the U.S., protected individual as defined by 8 U.S.C. 1324b(a)(3\), or eligible to obtain the required authorizations from the U.S. Department of State.*
True Anomaly is committed to equal employment opportunity on any basis protected by applicable state and federal laws. If you have a disability or additional need that requires accommodation, please do not hesitate to let us.
To conform to U.S. Government space technology export regulations, including the International Traffic in Arms Regulations (ITAR) you must be a U.S. citizen, lawful permanent resident of the U.S., protected individual as defined by 8 U.S.C. 1324b(a)(3\), or eligible to obtain the required authorizations from the U.S. Department of State.
True Anomaly is committed to equal employment opportunity on any basis protected by applicable state and federal laws. If you have a disability or additional need that requires accommodation, please do not hesitate to let us.
Salary Context
This $255K-$375K range is above the 75th percentile 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 True Anomaly, 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. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($315K) sits 47% above the category median. Disclosed range: $255K to $375K.
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.
True Anomaly AI Hiring
True Anomaly has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Denver, CO, US. Compensation range: $375K - $375K.
Location Context
AI roles in Denver pay a median of $199,950 across 66 tracked positions. That's 7% below 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 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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