Senior MLOps Engineer (f/m/d)

Berlin /
Engineering /
Full Time
/ Hybrid
🙌 Who are we?

- A commercial open source company that focuses on MLOps platform for multimodal and neural search.
- Founded in Feb. 2020, raised $37.5M in 20 months. Now a global team of 50 with four offices: Berlin (HQ), San Jose, Shenzhen, and Beijing.
- One of the high-valued & high-potential AI startups in the world, featured on Forbes DACH AI30 2020, CBInsights AI 100 2021 & 2022.

😊 Benefits & Perks

💰 Competitive salary & stock options
🌎 Multi-cultural & diverse team
🎓 Numerous opportunities to present/attend top AI/OSS/industry conference
🦄 Rapid career development opportunities alongside the company
🏢 Central office in downtown Berlin, San Jose, Shenzhen, Beijing
⛱️ Free snacks & drinks, monthly team events, flexible working hours, home office options
💻 Macbooks & top-notch equipment

✨ Who do we want?

- You are passionate about multimodal intelligence and making it accessible to everyone.
- You want to work with the latest technologies and are fascinated by AI/ML.
- You are a fast learner and a team player and enjoy working in an async, distributed environment.
- You are proactive and take ownership of your projects.
- You have excellent communication skills in English.

💁 About this position

What you should bring to the team:
- A passion and deep understanding about deep learning, multi-modal representation learning and their applications (e.g.: search and creative AI)
- A passion for deploying and scaling up complex machine learning workflows on the cloud
- A motivation for solving real-world problems and delivering outstanding user experiences
- A team player mindset, where you enjoy working in a collaborative environment
- A proactive take on ownership of your projects


    • Contribute to all the components of the software stack, from the low-level training libraries to the cloud APIs and user interface
    • Build, test and maintain the cloud training infrastructure to improve the model performance and reduce costs
    • Collaborate with external teams and partners to integrate new features to the platform
    • Mentor other engineers in the team and have the ability to break down big tasks into smaller coherent sub-tasks
    • Promote the product by giving technical talks at conferences and meet-ups or writing instructional and technical blog posts
- Bachelors Degree or higher
- Experience in building ML related APIs and microservices
- Competent in cloud technologies, especially Docker, Kubernetes and AWS
- Strong English language skills
- 3+ years of experience in building/integrating software using Python
- (Bonus) Experience working with training and inference optimization techniques, such as runtime/inference engines
- (Bonus) Experience with major ML frameworks - Pytorch, JAX, Tensorflow

💼 Hiring Process

Candidates can expect the hiring process to follow the order below. Please keep in mind that candidates can be declined from the position at any stage of the process. 

- The first round is the CV screening, candidates will receive an email that contains a link for booking the next round. This process takes a maximum of one week.

- Qualified candidates will be invited to schedule a 30-minute screening call specifically on Zoom with one of our global recruiters. For engineering candidates, after this interview candidates will receive an email and be asked to complete an offline code challenge. On average the candidates can finish it in 30 minutes.

- Next, candidates will be invited to schedule Peer Interviews with team members from the relevant team. There are two rounds of Peer Interview, 1st is Technical Peer Interview and the 2nd is Team Peer Interview. For engineering candidates, the team will examine the quality of the offline challenge as well as you fundamental knowledge and coding skill during the Technical Peer Interview; one should also expect a live-coding challenge in 10 to 15 minutes. As long as candidates passed the Technical Peer Interview, they will be invited to talk with specific Team Lead in the Team Peer Interview stage. The interview will be more relevant to practical problem solving.

- Finally, candidates will be invited to schedule a 30-minute interview with CXO.

We will collect the feedback from all interviewers and make a decision in a maximum of two weeks (on average it takes 5 working days). Then the candidate will be invited to another 15-minute call with our recruiters to discuss the terms of the offer.