Software Engineer, Ikigai Labs
$130-160k
Office located in Cambridge, MA
Generative AI for tabular data
Job no longer available
Generative AI for tabular data
101-200 employees
Job no longer available
$130-160k
Office located in Cambridge, MA
101-200 employees
Company mission
Ikigai's on a mission to bring AI to everyone.
Role
Who you are
- We are looking for a team player who is a quick learner, performs in a rapid development cycle, has a drive to surpass expectations, and an eagerness to share their work and knowledge
- 2+ years of experience in a Software Engineering Role
- A bachelor's/master’s degree in Computer Science or Engineering
- Must have advanced experience with Python, AWS services, and/or ETL/ELT pipelines
- Experience with Kubernetes and/or EKS/AKS (optional)
- Understanding fundamentals of system design patterns and testing best practices
- The ability to learn quickly in a fast-paced environment
- Excellent organizational, time management, and communication skills
- Willingness to discuss obstacles, find creative solutions, and take initiative
- The ability to receive and give both constructive and encouraging feedback
- Knowledge of streaming data processing and real-time analytics
- Familiarity with data governance, security, and compliance best practices
What the job involves
- Ikigai Labs is seeking a dynamic and passionate engineer with strong software fundamentals to join a high-performing infrastructure team
- Design and develop scalable data integration (ETL/ELT) processes
- Improve and optimize an on-demand predictive modeling platform with Python
- Utilize Kubernetes to orchestrate scalable deployments and manage Docker containers
- Learn and leverage numerous AWS services to solve cloud-native problems, improve the testing platform which performs sanity checks, unit tests, scale tests, heartbeat tests, and performance tests
- Work to keep the platform secure and follow industry standard compliance practices (ISO 27001:2022 and SOC II + HIPAA)
- Provide periodic support to our customer success team
- Languages: Python3, SQL, GoLang, bash
- Cloud: Kubernetes, Helm, EKS, Terraform, AWS
- Databases: Postgres, Elasticsearch, DynamoDB
- Data Engineering: Apache Arrow, Dremio, Ray
- Misc.: Apache Superset, GRPC, Jenkins, Airbyte
Our take
Business is increasingly data-driven, but the sheer amount of data companies now handle poses major challenges in organizing, analyzing, and making use of that information. Ikigai Labs aims to make data accessible, providing a no-code platform which uses AI to connect disparate data sources and organizes and optimizes the information for scenario planning, forecasting and use in enterprise applications.
Ikigai isn’t the only company using AI to make sense of data, but it stands out with its graphical modelling approach as opposed to the prevailing large language models used by competitors such as Hugging Face and Daitaku. Graphical modelling is well suited to mapping out relationships using enterprise data (which is usually tabular) and is far less expensive to operate than large language models. These advantages have led to Ikigai’s platform being used by thousands of data teams from major companies such as IBM, Honeywell, and FedEx to name just a few.
With substantial funding to its name, Ikigai is ready to further develop its product and scale its team. While it faces intense competition, its focus on enterprise data needs seems likely to help it stand out from the pack and will stand it in good stead over the coming years.
Freddie
Company Specialist at Welcome to the Jungle
Insights
Company
Funding (last 2 of 3 rounds)
Aug 2023
$25m
SERIES A
Dec 2021
$13m
SEED
Total funding: $38.3m
Company HQ
Civic Center, San Francisco, CA
Leadership
Vinayak Ramesh
(Co-Founder & CCO)Initially the CEO of Ikigai before moving to the role of CCO in 2024. Previously co-founded and was CTO of Wellframe. Holds a Master's of Engineering from MIT.
Devavrat Sha
(Co-Founder & CEO)Initially the CTo of Ikigai before moving to the role of CEO in 2023. Previously co-founder and Chief Scientist of Celect. PhD in Computer Science and Post Doc from Stanford University.
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