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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. AI\-assisted work is the biggest force multiplier available to a company at True Anomaly's stage. It accelerates how we build, how we deliver, and how we scale. We are investing in AI as a core operating capability, not a side experiment, and this role exists to help make that investment real.
As a Senior AI Platform Engineer, you will be one of the first hires to build the infrastructure, tooling, and integrations that put AI directly into the hands and workflows of every team across the company. This is not a research role. It is a building role. You will ship capabilities that engineers, program managers, operators, and business teams use every day to move faster and produce better work.
The scope is broad by design. Your work will range from building connectors between AI models and internal systems, developing custom applications for a specific business workflow, and more. You will contribute to platform\-level technical decisions about AI providers, tools, and architectures. You will also help build and deliver AI training and enablement for the company, including office hours, brown bags, and hands\-on resources.
The platform engineering org, reporting to the Chief Security Officer, will work across the enterprise, from engineering and product to marketing and finance. You will have meaningful autonomy, visibility to executive leadership, and the opportunity to shape how a fast\-growing defense technology company operates with AI.
RESPONSIBILITIES
- Build and maintain AI platform infrastructure: API integrations, model access pipelines, system connectors (MCP, tool use), and on\-premises compute environments as needed
- Develop custom AI\-powered applications, workflows, and integrations that connect frontier models to internal systems such as project management, documentation, communications, and operational tools
- Contribute to technical evaluation and selection of AI providers, tools, and platforms, helping inform build\-vs\-buy decisions based on capability, compliance requirements, and speed to value
- Help design and deliver AI training and enablement programs for the company, including hands\-on workshops, office hours, documentation, and shared resources that build real fluency across teams
- Partner with engineering, security, and IT teams to ensure AI deployments meet government security and compliance requirements across data classification levels
- Stay current with the rapidly evolving AI landscape and surface new models, tools, and capabilities that could accelerate the company
QUALIFICATIONS
- Typically 5\+ years of software engineering, DevOps, or cloud engineering experience building and shipping production systems, with demonstrated ability to work independently and deliver complex projects with minimal guidance
- Software engineering or similar background with experience building and shipping production systems, ideally in Python, TypeScript, or similar languages common in AI/ML tooling
- Experience using the latest AI powered development tools to deliver production code and automate routine tasks. You can read, write, and understand code in common programming languages like Python, Typescript, Java, etc.
- Experience with large language model APIs, including prompt engineering, tool use, retrieval\-augmented generation (RAG), or agent frameworks (e.g. CrewAI, Pydantic AI).
- Experience building integrations between software systems using APIs, webhooks, data pipelines, or authentication flows
- Ability to work across a broad technical surface area, moving between infrastructure, application development, and platform work as priorities require
- Effective communication skills and ability to explain technical AI concepts to non\-technical audiences through training, documentation, or direct support
- Comfortable operating with 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 deploying AI or ML systems in government, defense, or regulated environments with security and compliance constraints
- Experience deploying and operating production workloads to cloud environments (AWS, Azure, GCP).
- Familiarity with government cloud environments (AWS GovCloud, Azure Government) and authorization frameworks (FedRAMP, NIST 800\-53, IL4/IL5\)
- Background in developer tooling, platform engineering, or internal tools teams at high\-growth technology companies
- Experience building knowledge retrieval systems, embedding pipelines, or enterprise search infrastructure
- Experience contributing to technical roadmaps and architectural decisions that affect multiple teams or products
- Active U.S. Secret or Top\-Secret security clearance
COMPENSATION
- Base Salary: Denver \- $170,000 \- $260,000, Long Beach \- $180,000 \- $275,000
- 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— Long Beach, CA, or Denver, CO. 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.
- Work environment—the work environment; temperature, noise level, inside or outside, or other factors that will affect the person's working conditions while performing the job.
- Physical demands—the physical demands of the job, including bending, sitting, lifting and driving.
This position will be open until it is successfully filled. To submit your application, please follow the directions below. \#LI\-Onsite
*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.*
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 $170K-$275K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 2130 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 4,133 AI roles we're tracking, AI/ML Engineer positions make up 69% 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 $185,000 based on 13,200 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($222K) sits 20% above the category median. Disclosed range: $170K to $275K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
True Anomaly AI Hiring
True Anomaly has 3 open AI roles right now. They're hiring across AI/ML Engineer. Based in Denver, CO, US. Compensation range: $225K - $330K.
Location Context
AI roles in Denver pay a median of $184,000 across 171 tracked positions. That's 8% 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 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 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 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). 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 (106) are outnumbered by mid-level (1,901) and senior (1,663) 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 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 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 $200,700. Top-quartile roles start at $254,000, 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 Safety roles lead at $274,200 median, while Prompt Engineer roles sit at $140,000. 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 (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 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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