Interested in this AI/ML Engineer role at Atlas Technologies, Inc.?
Apply Now →Skills & Technologies
About This Role
We are seeking a Senior Applied AI/ML Engineer to design, build, and operationalize advanced AI/ML solutions that solve high-impact mission, operational, and business problems. This role blends deep technical expertise with practical, hands-on engineering, building AI enabled applications, deploying them into production systems, and working closely with product, engineering, and domain teams to deliver measurable value.
You will play a lead role in shaping solution architectures, selecting the right models and frameworks, developing experiments, and translating complex concepts into working capabilities that end users can rely on. The ideal candidate is equally comfortable in research, prototyping, and applied engineering environments.
Key Responsibilities
- Design, build, and deploy scalable AI/ML tools for generative AI use cases
- Develop end-to-end ML pipelines, including LLM tool integration, data ingestion, feature engineering, model training, evaluation, and monitoring
- Implement production-grade inference systems and integrate ML models into applications, APIs, and microservices
- Lead experimentation, define hypotheses, run evaluations, assess model performance, and identify improvement strategies
- Work closely with engineering teams to build robust, secure, and maintainable AI-enabled architectures
- Provide periodic instruction and prompt fine-tuning to resolve newly discovered hallucination vectors, user-reported biases, or changes in operational terminology.
- Partner with domain SMEs to translate real-world constraints into effective features, labels, and model requirements
- Evaluate emerging AI technologies, frameworks, and foundation models and recommend their use in mission-relevant applications
- Mentor junior engineers and contribute to internal standards, coding practices, and reusable components
- Ensure responsible AI practices, bias evaluation, data governance, model explainability, and ethical use of automated systems
- Prepare demos, prototypes, and proof-of-concept integrations that clearly illustrate feasibility and value
Required Qualifications
- Bachelor’s or Master’s degree in Computer Science, Machine Learning, Data Science, Engineering, or related field
- 3+ years of experience in applied AI/ML engineering, including production deployments
- Experience with LLMs, fine-tuning, retrieval-augmented generation (RAG), prompt engineering, or custom model training
- Experience with vector databases, high-dimensional embeddings, and semantic search
- Experience applying LoRA adapters for efficient fine-tuning of foundation models, including training, evaluation, and integration into production pipelines
- Strong proficiency in Python and ML libraries such as TensorFlow, PyTorch, scikit-learn, Hugging Face, LangChain, or similar
- Experience building and operating AI/ML systems in air-gapped environments
- Deep understanding of model development: supervised/unsupervised learning, neural networks, transformers, time-series, embeddings
- Familiarity with modern AI/ML ops tooling: MLflow, SageMaker, Vertex AI, Databricks, or equivalent
- Strong understanding of API design, microservices, containers, and CI/CD for ML
- Ability to connect technical solutions to business value and communicate effectively with executives and non-technical stakeholders
Preferred Qualifications
- Experience with Azure AI Foundry environments
- Exposure to reinforcement learning or agentic systems
- Experience building real-time or low-latency ML inference systems
- Experience in mission-driven, regulated, or national security-oriented environments (if applicable)
- Strong UI/UX or front-end awareness for integrated model experiences (optional)
What We’re Looking For
A senior engineer who is:
- Curious and experimental, but also practical and grounded
- Capable of taking a loosely defined problem and creating a working solution
- Comfortable presenting concepts, demoing prototypes, and explaining tradeoffs
- Passionate about building AI systems that deliver measurable, real-world impact
Want to hear more? Read on to see what our Team Members say about their experience:
“From day 1, I knew Atlas Tech was where I wanted to be. Atlas’ culture embraces creativeness, community involvement, and taking care of their employees. If you’re looking for a great place to work, come join us!” - Blane
“I could not have asked for a better company to transition from Active-Duty military to civilian life. Atlas not only provides opportunities for professional growth, but it’s also highly encouraged. The passion and appreciation for what we do that is displayed every day, makes it exciting for me to come to work each day.” - Laurence
Atlas Tech is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, age, sexual orientation, gender identity, national origin, disability, genetic information, or protected veteran status. We are committed to fostering an inclusive workplace where diverse perspectives are valued and respected.
Accessibility & Accommodations
Atlas Tech strives to make our website, www.atlas-tech.com, accessible to all users. If you need assistance completing the application process or require a reasonable accommodation due to a disability, please contact us at [email protected].
This contact information is designated for accommodation requests only and cannot be used to inquire about the status of an application. Atlas Tech is committed to providing reasonable accommodations in accordance with the Americans with Disabilities Act (ADA).
Atlas Tech offers a competitive salary, generous benefits package, and an opportunity to make a positive impact in your own community.
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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Atlas Technologies, Inc., 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 $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Atlas Technologies, Inc. AI Hiring
Atlas Technologies, Inc. has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Washington, DC, US.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.