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About This Role
Build, Deploy, and Maintain AI for an Unpredictable World
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Striveworks helps organizations harness the power of artificial intelligence to solve real\-world national security and business challenges by serving as the command center between data, models, and business outcomes. Founded by data scientists and engineers, Striveworks set out to make the journey from deployment to ongoing optimization simple and effective.
With Striveworks, organizations aren’t just deploying AI—they’re building systems that remain reliable, adaptable, and ready to scale in an unpredictable world. Mission\-critical operations require models that perform where they’re deployed, scale as workloads grow, and adapt rapidly as AI capabilities advance. Striveworks meets these demands, increasing reliability and performance while lowering costs—and enabling confident, data\-driven decision\-making in dynamic environments.
The Role
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As a Machine Learning Engineer at Striveworks, you’ll be challenged—and trusted—on day one to be a core contributor to both the customer\-driven projects and the enduring products of the company. You will represent Striveworks as a technology builder on projects and solutions that leverage Chariot, our proprietary AI operations (AIOps) platform, and you will inform and contribute to future capabilities of that platform. You will inform, envision, and help extend Striveworks’ core software products. You will work alongside data scientists, software engineers, and DevOps engineers to transform machine learning models into operational capabilities.
You’re right for this opportunity if you value and possess technical expertise and enjoy pushing the boundaries of your own capabilities. You’re outcome driven and are passionate about applying both software engineering and data science to solve real\-world problems. You know that building customer\-centric solutions, communicating clearly, and capturing repeatable value into productized capabilities are all critical to success.
Your day\-to\-day will include:
- Developing machine learning pipelines and custom analytics that are applied to image, video, text, geospatial, time series, and structured data
- Orchestrating and automating complex data engineering and analytic pipelines
- Envisioning, specifying, and, at times, designing and implementing core product functionality
- Conducting mission\-critical fieldwork in support of customers and other stakeholders
This position offers a fully remote work environment, or you can work hybrid/on site at customer locations at Joint Base Lewis–McChord in Tacoma, WA. If remote, you will be expected to travel up to 30% of the time. If local, you will be expected to travel up to 25% of the time.
The Right Fit
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In addition to the specific skills and expertise detailed below, we are looking for individuals who share our values. Sharing a set of values allows us to move at the speed of trust.
Collectively, we value a high\-trust work environment where people respect each other and use candor kindly and constructively. We value work that intersects passion and perseverance, we geek out about the potential of our contributions, and we find joy in working hard on things that matter. Finally, we value taking ownership, having agency, and feeling individual responsibility for collective results.
Here’s what we’re looking for:
- BS degree in computer science, machine learning, or a related discipline and 2\+ years of relevant experience
- Experience contributing to data\-centric systems (e.g., data engineering, data cleaning, ETL pipelines, machine learning, and other production analytics)
- Proficiency in software engineering fundamentals to include algorithms, data structures, design patterns, and at least one systems programming language (e.g., Go, Rust, C\+\+, Java, Scala, etc.)
- Proficiency in Python and exposure to libraries like TensorFlow, PyTorch, and/or scikit\-learn
- Exposure to modern software engineering tools and processes (Agile, version control, issue tracking, CI/CD, debugging, etc.)
- Active Secret (or above) US security clearance
- Due to the nature of this role, candidates must have US citizenship
The Wish List
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We’re very interested in candidates who possess the above qualifications, and we appreciate and consider the addition of:
- An advanced degree (e.g., MS, MEng, PhD) in computer science, machine learning, data science, or a related discipline
- Excellence in Python and deep knowledge of libraries like TensorFlow, PyTorch, and/or scikit\-learn
- Knowledge of relevant architectures and design patterns for client\-server systems (e.g., asynchronous programming, REST, GraphQL, React, Vue, Angular)
- Experience implementing and deploying software into containerized or cloud environments (e.g., Docker, Kubernetes \[K8s], cloud architectures)
- Experience with machine learning applied to imagery and/or video data
- Experience building agentic systems, agentic workflows, or AI agents
- Experience defining, scoping, planning, and delivering complex technical solutions
- Experience delivering technology solutions in secure government environments
The anticipated base pay range for this position is $160,000–$190,000/year. Striveworks’ total compensation package includes a competitive base salary, equity grants, and cash bonuses.
The Benefits
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- Medical/dental/vision insurance
- Voluntary life, long\-term disability, accident, and hospital indemnity insurance
- HSA and FSA (including dependent care FSA) plans
- 401(k) plan
- Unlimited PTO
- Paid parental leave
Check us out on Built In!
*Striveworks is an Equal Opportunity Employer and does not discriminate in employment on the basis of race, color, religion, belief, sex (including pregnancy and gender identity or expression), national origin, social or ethnic origin, political affiliation, sexual orientation, marital status, disability, genetic information, age, membership in an employee organization, retaliation, parental status, military service, or other non\-merit factors. Striveworks will not tolerate discrimination or harassment of any kind.*
*If you require assistance or a reasonable accommodation in the application process, please contact Operations at* *[email protected]**.*
*In compliance with federal law, all persons hired will be required to verify identity and eligibility to work in the United States and to complete an employment eligibility verification form upon hire.*
*Striveworks is a participating employer in the E\-Verify program.*
Salary Context
This $160K-$190K range is below the median 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 Striveworks, 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. Mid-level AI roles across all categories have a median of $165,778. This role's midpoint ($175K) sits 5% below the category median. Disclosed range: $160K to $190K.
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.
Striveworks AI Hiring
Striveworks has 2 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Based in Remote, US. Compensation range: $190K - $190K.
Remote Work Context
Remote AI roles pay a median of $173,300 across 2,012 positions. About 14% 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 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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