Machine Learning Engineer

Our mission

Who are we if not the sum of our memories? Isn’t what we call experience simply the memory of our past? What is death but the loss of all our memories?

Biological evolution is too slow compared with our environment, and our memory is now limited when faced with the mass of information available. We need to take matters into our own hands to overcome our limitations.

At Episodic we're building a personal artificial memory system that brings all the memories needed for creative, innovative people. Through our brand-new type software using machine and deep learning and a revolutionary organization of data in an intuitive and comprehensive interface.

Let’s build the superconscious minds of tomorrow.

What are we looking for?

As a machine learning engineer at Episodic you must have the following:

Values :

• The most important things for us is that you are a highly driven person. We are only looking for people who show personal motivation, that blossom through challenges.

• You must be obsessed by the need to be useful and to have a beneficial impact on the world.

• You have to be curious and show a strong passion for what you do (in all aspects of your life).

• Be honest and accept a mode of communication based on radical transparency, where the mission comes before egos.

All of the above must be evident when looking at your background.

Aptitudes:

• Smart, able to quickly and deeply understand problems or situations

• Learning capacities, master quickly unfamiliar knowledge

• Ability to focus, get intensely immersed in subjects and get sh*t done

All of the above must be evident when looking at your background.

Skills:

• A minimum of 3 years of full time experience in machine learning and or deep learning
• Strong software engineering skills (API, programming languages versatility)

• Passion for AI, with a constant drive to stay updated and ahead of the curve, up to date on the state of the art

• MS or PhD in Computer Science, Engineering or a related technical field

• Experience building LLM applications or RAG products using Python

• Experience in the use of graphs

• Ability to architect systems with a focus on performance, scalability and security

• Experience with MLOps and DevOps principles and technologies (e.g. Containerization, Kubernetes, Cloud infrastructures)

• Exceptional problem-solving, analytical skills and the ability to tackle complex problems with a critical thinking approach

• You have manipulated and processed large sets of textual data

• Self starter mentality with the ability to thrive in fast paced unstructured environment

• Ability to communicate and collaborate with non-technical team members

All of the above must be evident when looking at your background.

About the job:

• As a machine learning engineer you will be taking on both hands-on development and technical responsibilities as we scale.
• In the early stages, you'll be deeply involved in coding, product development, and platform architecture.
• As the company grows, your role will evolve into shaping the technical strategy, and driving innovation.
• Implement best practices for full-stack development, AI integration, and cloud infrastructure.
• Develop AI features, including natural language processing (NLP), LLM, RAG, Graph, speech-to-text and APIs.
• Continuously evaluate emerging AI technologies to keep the company at the cutting edge of innovation.
• Serve as an engineer, writing clean, scalable, and efficient code across the stack.
• Design and implement AI-driven features, full-stack applications, and core systems, starting from the MVP.
• Collaborate closely with the founders to align the technical roadmap with the product vision.
• Foster a culture of innovation, technical excellence, and continuous improvement.

Benefits:
Attractive wage and compensation in equity.
As an early employee you will benefit from an interesting career plan with rapid hierarchical progression.
You'll have the opportunity to fully build features that will be used and that will have an impact while learning continuously in fascinating subjects with a passionate team.

Location:
On-site, Austin or San Francisco Bay Area

Contact:
[email protected]