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Algorithm Research Jobs in Bremerton, WA (NOW HIRING)

Apple

LLM Machine Learning Research Engineer

Seattle, WA

$139.50K - $258.10K/yr

LLM Machine Learning Research Engineer Apple is seeking a Research Engineer to join our Foundation Model Preparation and Algorithm Team. We are looking for all levels of talent to bring innovative AI ...

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Algorithm Research information

What are the key skills and qualifications needed to thrive as an Algorithm Researcher, and why are they important?

To excel as an Algorithm Researcher, you need a strong background in mathematics, computer science, and algorithm design, often supported by an advanced degree such as a master's or PhD. Proficiency with programming languages (like Python, C++, or Java), machine learning frameworks, and version control systems is essential. Analytical thinking, creativity, and effective communication are crucial soft skills that set top performers apart in this field. These skills are vital for developing innovative, efficient solutions and collaborating within interdisciplinary teams to solve complex computational problems.

What are the typical challenges faced by professionals in Algorithm Research roles and how can they best address them?

Algorithm Research professionals often encounter challenges such as bridging the gap between theoretical solutions and practical implementation, staying updated with rapid advancements in the field, and collaborating with cross-functional teams to integrate research outcomes into real-world products. To address these challenges, it is helpful to maintain strong communication with engineering teams, participate in continual learning through academic papers and conferences, and adopt an iterative approach to testing and refining algorithms. Building a habit of documenting experiments and results also streamlines collaboration and future development.

What is Algorithm Research?

Algorithm research involves studying, designing, analyzing, and optimizing algorithms to solve complex problems efficiently. Researchers in this field explore new computational methods, improve existing algorithms, and evaluate their performance in various contexts. This work is fundamental in areas like computer science, artificial intelligence, data science, and cryptography, driving technological advances and innovation.

What is the difference between Algorithm Research vs Data Scientist?

AspectAlgorithm ResearchData Scientist
Required CredentialsAdvanced degrees in CS, Mathematics, or related fieldsDegree in CS, Statistics, or related fields; certifications like SAS or Python
Work EnvironmentResearch labs, R&D departments, academiaBusiness environments, analytics teams, tech companies
Industry UsageDeveloping new algorithms, theoretical researchAnalyzing data, building predictive models, insights generation
Common Search/ComparisonYesNo

Algorithm Research focuses on developing and testing new algorithms, often in research or academic settings, requiring advanced technical credentials. Data Scientists analyze data to generate insights and build models, working primarily in business environments. While both roles involve data and programming, their core objectives and work settings differ significantly.

Algorithm Research jobs near you

Research Scientist - Driven Agent Self-Evolution - Global Frontier Tech Recruitment Program - 202...

ByteDance

Seattle, WA • On-site

Full-time

Posted 20 days ago

Job description

Job Summary:
ByteDance is a global technology company known for its innovative products like TikTok and CapCut. They are seeking a Research Scientist to develop agent frameworks that continuously learn and improve through real-world interactions, focusing on self-evolving agent systems and large-scale log analytics.
Responsibilities:
• Research and develop agent frameworks that continuously learn and improve from execution traces, user feedback, and environmental signals.
• Build large-scale log analytics pipelines to extract quality signals, usage patterns, and actionable insights from model and agent invocation logs, driving data-informed system and model improvements.
• Explore and apply frontier techniques in LLM post-training, reasoning, and planning to enhance agent capabilities.
• Collaborate across algorithm research, platform engineering, and product teams to turn research ideas into production-grade systems at scale.
Qualifications:
Required:
• Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, or a closely related discipline.
• Strong theoretical and practical foundation in machine learning, deep learning, reinforcement learning, or optimization.
• Research experience in at least one of the following areas: LLM-based agents, planning and reasoning, multi-agent systems, continual/lifelong learning, or LLM post-training (e.g., RLHF, DPO, GRPO, self-play).
• Strong programming skills in Python and proficiency with ML frameworks (e.g., PyTorch, TensorFlow, JAX).
• Publication record at top-tier venues (e.g., NeurIPS, ICML, ICLR, ACL, EMNLP, NAACL, AAAI, AAMAS, COLM).
• Strong problem-solving skills and ability to thrive in a fast-paced, collaborative environment.
Preferred:
• Publications in areas directly related to agent learning and adaptation, such as tool use, self-improvement, skill discovery, trajectory optimization, reward modeling, or agent evaluation.
• Research experience in LLM reasoning and planning, including chain-of-thought, tree/graph search, Monte Carlo methods, or inference-time compute scaling.
• Experience training or fine-tuning large language models, including supervised fine-tuning, preference optimization, or curriculum learning.
• Hands-on experience building or evaluating LLM-based agent systems (e.g., ReAct, function calling, code generation agents, or multi-agent orchestration).
• Familiarity with meta-learning, few-shot generalization, or transfer learning in the context of LLM-based systems.
• Experience with feedback-driven optimization loops, such as online learning, bandit methods, or evolutionary strategies applied to agent improvement.
• Strong interest in bridging frontier AI research with production-grade engineering — turning papers into systems that work at scale.
• Internship experience at technology companies or research organizations.
Company:
ByteDance is a technology company that develops content creation platforms and services. Founded in 2012, the company is headquartered in Beijing, CHN, with a team of 10001+ employees. The company is currently Late Stage.

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