Senior Machine Learning Engineer (AI)

TL;DR

Senior Machine Learning Engineer (AI): Designing, training, and iterating on machine learning models for intelligent agents and decision-making systems with an accent on reinforcement learning and related approaches. Focus on improving practical experimentation and training workflows, including data generation, experiment tracking, and reproducibility.

Location: Hybrid – Los Angeles Area (1–2 in-office meetings per month)

Salary: $150,000 ‒ $185,000 Annually

Company

Snail Games strives to create the new high bar for gameplay experience in online gaming.

What you will do

• Design, train, and iterate on machine learning models for intelligent agents and decision-making systems, with an emphasis on reinforcement learning.
• Define and refine state representations, action spaces, reward structures, and evaluation criteria to improve agent behavior.
• Build and improve practical experimentation and training workflows, including data generation, experiment tracking, and reproducibility.
• Analyze results, debug model behavior, and make pragmatic tradeoffs between model performance, iteration speed, and system complexity.
• Work closely with engineers and other partners to help integrate successful ML work into usable product systems.
• Contribute thoughtful technical input on next-step experiments, tooling, and ML direction as Egofold continues to evolve.

Requirements

• Strong foundation in machine learning, with hands-on experience building, training, and iterating on applied ML systems.
• Professional or substantial project experience with reinforcement learning, agent-based systems, sequential decision-making, or closely related areas.
• Strong Python skills and experience with modern ML frameworks such as PyTorch.
• Experience designing experiments, evaluating model behavior, and improving results through systematic iteration.
• T-shaped capability: deep machine learning expertise plus practical range across one or more adjacent areas such as simulation, evaluation, model integration, systems collaboration, or robotics-adjacent machine learning.
• Strong problem-solving ability, sound judgment, and comfort working in ambiguous, fast-changing environments.

Nice to have

• Experience with reinforcement learning methods such as PPO, SAC, DQN, actor-critic, or related approaches.
• Familiarity with simulation environments, multi-agent systems, game AI, or interactive agent behaviors.
• Familiarity with C++, inference runtimes, or collaborating with engineers who deploy machine learning models into production systems.
• Exposure to robotics, embodied AI, or embedded / on-device machine learning constraints.

Culture & Benefits

• True focus on work/life balance.
• Paid company holidays, vacation, and separate sick leave.
• Medical, dental, vision, and Life/LTD.
• 401k with company match.