Member of Technical Staff: Research Engineer, Product

San Francisco

essential AI

Building the Enterprise Brain

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Essential AI’s mission is to deepen the partnership between humans and computers, unlocking collaborative capabilities that far exceed what could be achieved today. We believe that building delightful end-user experiences requires innovating across the stack - from the UX all the way down to models that achieve the best user value per FLOP.

We believe that a small, focused team of motivated individuals can create outsized breakthroughs. We are building a world-class multi-disciplinary team who are excited to solve hard real-world AI problems. We are well-capitalized and supported by March Capital and Thrive Capital, with participation from AMD, Franklin Venture Partners, Google, KB Investment, NVIDIA.

The Role

The Research Engineer, Product will be responsible for working with our customers to deploy our product into customer’s environments to improve their business workflows. You will immerse yourself into their business workflows to deeply understand their business operations and apply learnings to our ML models. You will work cross-functionally with various teams to help reiterate our solutions to ensure that they deliver high customer value.

What you’ll be working on
  • Own the development and deployment of custom solutions for our customers by deeply understanding their workflows, writing integrations and curating data and evals.

  • Develop trusting relationships with customers by embedding yourself into our customer’s business units to understand their unique business workflows.

  • Identify relevant datasets from customer workflows and work with our backend engineering team to integrate data into a stable and extensible pipeline.

  • Collaborate with other research scientists and engineers to post-train, validate, and fine-tune machine learning models.

  • Collaborate cross-functionally to provide customer feedback for future model development by identifying new capabilities and evaluations that improve the overall product and inform our research roadmap.

  • Deploy machine learning models into production environments and integrate them into existing systems and workflows.

  • Educate and build tutorials and documentation to help customers onboard and easily get started with the product. Collect feedback and document user experiences to make it seamless over time.

What we are looking for
  • Hands-on experience working (training, fine-tuning, optimizing, deploying) with large language models

  • Solid understanding of model learning algorithms, model evaluation, and deployment strategies.

  • Experience in applied machine learning with a strong track record of delivering successful machine learning projects

  • Experience iterating on 0→1 products in an enterprise/B2B setting together with customers

  • Excellent communication skills (evidence of this includes creation of educational material or documentation around hard technical concepts or software packages)

  • Proven track record of successfully deploying software in enterprise settings

  • Experience with being embedded in external clients’ environments. Ideally, you can talk about instances where you’ve handled customer issues, helped them navigate tricky and difficult situations and improved overall customer experience.

  • Deep interest in and/or prior exposure to building with LLMs in production

  • [Bonus] Experience with generating or using synthetic data in production

  • [Bonus] Understanding how to deploy software in highly secure or regulated environments

We encourage you to apply for this position even if you don’t check all of the above requirements but want to spend time pushing on these techniques.

We are based in-person in SF. We offer relocation assistance to new employees.

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* Salary range is an estimate based on our AI, ML, Data Science Salary Index 💰

Tags: CX Engineering LLMs Machine Learning ML models Research UX

Perks/benefits: Relocation support

Region: North America
Country: United States
Job stats:  1  0  0

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