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 engineering support in the autonomous vehicle space to building bespoke, scalable experiment 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 .
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.
We are looking to hire a Machine Learning Software Engineer. Titles can be arbitrary, but for us this means a software engineer who writes code, builds infrastructure, and implements machine learning algorithms. You should consider yourself a good fit for this if:
- You have experience building and improving ML-powered systems and an interest in developing your skills there – bonus points if you’re interested in LLMs, generative AI, or deep learning in general.
- You enjoy solving hard technical problems at scale, for real business impact.
- You’re comfortable proposing end-to-end technical architectures that balance modularity, scalability, operations, security, and cost.
- You understand that clarity and simplicity -- in code and in communication -- are worth striving for.
- You have 5+ years of professional experience in software engineering and are able to present as an expert in client-facing situations.
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 lay people and engineers alike about issues, and keep teammates and clients informed on progress.
- A collaborative mindset: You approach your work as part of a larger team effort, and can take and give constructive feedback well.
- 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.
What we’re looking for specifically for this role:
- Experience with construction and design of the entire ML pipeline, including training, deployment, and hosting in a production environment, using engineering best practices (e.g., unit testing, monitoring, logging)
- Fluency in the Python ML/data science stack (e.g., PyTorch, Scikit-Learn, Pandas)
- Proficiency in cloud engineering (AWS/Azure/GCP) and infrastructure-as-code (e.g., Terraform or equivalent)
- Proficiency with ETL/data pipelines and data stores
- Facility with web application technologies (e.g., FastAPI, UI development)
- Open to all backgrounds, though deep learning/LLM background strongly preferred
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.
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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