Machine Learning Researcher (Applied LLMs)

Hop Labs works with organizations to build and deploy machine learning production systems at scale. Our clients range from startups to Fortune 10, and projects can vary widely – from greenfield LLM generative AI products to autonomous vehicle R&D to building bespoke evaluation frameworks. We are a fully distributed company, working remotely across the United States. While our projects often involve Python, PyTorch, and AWS, we are always ready to use the right tool for the job. Some quick facts about Hop Labs:

• 100% remote, primarily based in the U.S.
• 10-15 team members, depending on our project mix at any given time
• Exclusively focused on technical consulting around production-scale machine learning
• Results-oriented work environment with a lot of flexibility -- we value life outside of work

For more information about the company and the team, please check out our website. Please no staffing agencies/C2C. We are unable to handle visa sponsorship or H1B transfers at this time. BE AWARE OF SCAMS: Our team communicates with applicants only from a workablemail.com or hoplabs.com email address. Any similar email domains are fraudulent.

Requirements

We’re looking to hire a Machine Learning Researcher with experience applying LLMs to solve real problems. Titles can be arbitrary, but for us, this means engaging deeply with complex problems as well as proposing and prototyping solutions. In this role, you will:

• Stay up-to-date with the latest literature and advancements in machine learning for LLMs. We believe in the importance of continuous learning and applying cutting-edge techniques to solve real-world problems.
• Break down complex problems into smaller, tractable components and develop feasible solutions.
• Conduct rigorous experiments using a scientific approach. You will design and implement experiments, analyze results, and draw meaningful conclusions to guide the development of ML models.
• Focus on thorough evaluation and iterative improvement. You will establish evaluation metrics, assess model performance, and iterate on model architectures and hyperparameters to achieve optimal results.

You should consider yourself a good fit for this if:

• You have significant experience with conducting machine learning research and working with LLMs.
• You enjoy solving research-grade problems at scale, for real business impact.
• You demonstrate a solid understanding of the latest AI/ML research trends and techniques, coupled with a commitment to continual learning and professional growth in these areas.
• You understand that clarity and simplicity -- in code and in communication -- are worth striving for.

As a distributed team, some key qualities are particularly important for all of us at Hop Labs:

• A strong sense of ownership and initiative: You take your task or issue seriously, and proactively drive it start-to-finish.
• Clear and consistent communication: You can speak to both technical and non-technical stakeholders about issues, and keep teammates and clients informed on progress.
• A collaborative mindset: You approach your work as part of a larger team effort, can take and give constructive feedback well, and generally prioritize building rapport and trust with teammates and clients alike.
• Clear thinking and attention to detail for complex projects: You are committed to a crisp understanding of what you’re doing and why, paired with careful planning and attention to details.
• Comfort with operating independently as well as part of a small, targeted team: You are flexible in working successfully in either context, in a fully remote environment.

Learn more about our values and how we practice them here. Residency in the U.S. or Canada, and authorization to work in the U.S. is required. If this role sounds like a good fit for you, please apply! We’ve made an effort to create a hiring process that is low-pressure, skills-focused, and compatible with the other commitments in your life.

Benefits

For our employees, we offer:

• Medical, dental, and vision benefits
• Life and disability insurance
• Paid holidays and vacation time
• Flexible work schedules

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