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About This Role
Senior Machine Learning Engineer
Remote US \& Canada
We are seeking a highly motivated and autonomous Senior Machine Learning Engineer to join our team. In this role, you will be a driving force behind our core ML models and underlying data and training pipelines. We are looking for a self\-starter, someone who is internally driven, passionate about their craft, and capable of collaboratively identifying, prioritizing, and executing workflows with their peers and stakeholders. If you thrive in an environment where you are trusted to own your projects end\-to\-end and can seamlessly explain complex technical concepts to both technical and non\-technical stakeholders, we want to hear from you.
We expect you to:
- Have deep passion for video games and think on behalf of our players
- Be curious, a problem solver and a self starter who is comfortable taking risks and proactively identifying and pursuing business needs. Know how and when to apply your knowledge, and be willing to share it with those around you
- Be comfortable with ambiguity and able to navigate it to iteratively refine the problem space
- Be responsible for your domain of expertise on projects from inception to delivery and beyond
Required Skills
- 5\+ years of experience in Machine Learning, Data Engineering, or a related quantitative field, with a track record of delivering end\-to\-end ML products
- Expert knowledge of statistical modeling and ML methods including Classical ML, Deep Learning, NLP, and Anomaly Detection, with the ability to select the right tools for the task
- Demonstrated proficiency in experimental design, hypothesis testing, and evaluating model performance
- Proven track record of building and deploying scalable ML solutions in production, ideally using Kubernetes and GCP
- Strong proficiency in Python, Java, or Scala, with an emphasis on writing clean, maintainable, and production\-ready code, alongside the ability to write highly optimized SQL for complex dataset construction
- Mastery of optimized SQL and experience with big data processing frameworks (e.g., Spark, Beam) and modern data lakehouse formats (e.g., BigQuery, Snowflake, Iceberg, Parquet)
- Exceptional ability to translate complex technical architectures and ML concepts for non\-technical stakeholders
Preferred Experience
Any of the following would be highly preferred, but most of all, we value team players who are eager to learn and contribute:
- Experience with graph databases, collaborative filtering, or user vectorization
- Experience with large\-scale sentiment analysis
- Experience with Typescript or Golang
- Experience with source control, CICD, Protobuf, or infrastructure as code
- Experience in the video game industry
- Experience in a small company with a startup feel
- Interest in technical writing and sharing your work
Perks:
- Paid Time Off, Holidays and Two Weeks Winter Break
- Employees and their dependents get medical, dental, and vision coverage, regardless of their level, tenure, or position within the company. Moreover, these benefits start on the first day of the job—there’s no waiting period before they kick in.
- Pet Insurance for those who need it too.
- Compassionate leave for employees who needs to take care of their family members
- Pre\-tax wellness stipend
- Pre\-tax work from home stipend
- Access our savings plan (401K program) with company match
- Mental health resources including Headspace membership and Employee Assistance Program (EAP)
- Discount portal for everyday goods and services
- Employee inclusive and diversity initiatives such as Grow Together
- Support for personal professional development
We look forward to meeting you!
Applicants must be authorized to work for any employer in the U.S. We are unable to sponsor or take over sponsorship of an employment Visa at this time.
The salary range for this position is $120,000 USD to $195,000 USD annually, with the opportunity to earn an annual discretionary bonus. This salary range is an estimate, and the actual salary may vary based on the Company’s compensation practices. Employees in this position are eligible to participate in the Company’s standard employee benefit programs, which currently include the following: medical, dental, vision, 401k, and paid time off.
\#LI\-Remote
Salary Context
This $120K-$195K range is above the median for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At thatgamecompany, 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 $166,983 based on 13,781 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($157K) sits 6% below the category median. Disclosed range: $120K to $195K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
thatgamecompany AI Hiring
thatgamecompany has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Los Angeles, CA, US. Compensation range: $195K - $195K.
Location Context
AI roles in Los Angeles pay a median of $178,000 across 1,695 tracked positions. That's 3% 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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