Join a team of passionate innovators who are redefining what's possible with AI and machine learning. We value creativity, collaboration, and continuous learning.
Innovation First
We're building cutting-edge technology that pushes the boundaries of what's possible with AI and machine learning.
- Freedom to experiment with new ideas
- Regular hackathons and innovation days
- Celebrating creative solutions
- Learning from both successes and failures
Collaborative Environment
We believe great ideas come from diverse perspectives and open communication.
- Cross-functional team collaboration
- Open and transparent communication
- Inclusive decision-making process
- Knowledge sharing sessions
Professional Growth
We invest in your development and provide opportunities to advance your career.
- Mentorship from industry experts
- Conference and learning stipends
- Clear career progression paths
- Challenging projects that expand your skills
We're looking for talented individuals to join our team and help us build the future of MLOps. Explore our current openings below.
DevOps Engineer
We are looking for a skilled and forward-thinking DevOps Engineer to help us scale, secure, and automate the infrastructure behind our AI-powered platform. You'll be a critical part of the engineering team, working on the reliability, availability, and performance of our system while implementing DevOps best practices.
Key Responsibilities
- Design, build, and maintain CI/CD pipelines for deploying machine learning applications.
- Automate infrastructure provisioning using tools like Terraform or Pulumi.
- Monitor system performance, identify issues, and optimize reliability and scalability.
- Manage Kubernetes clusters and container orchestration at scale.
- Ensure high availability, security, and disaster recovery for all environments.
- Collaborate with developers, data scientists, and product teams to improve deployment cycles.
- Maintain internal documentation and contribute to process improvement.
Qualifications
- Bachelor's degree in Computer Science, Engineering, or a related field (or equivalent experience).
- 3+ years of experience in a DevOps or Site Reliability Engineering role.
- Proficiency with CI/CD tools (GitHub Actions, Jenkins, CircleCI, etc.).
- Strong experience with Docker and Kubernetes in production environments.
- Expertise in cloud platforms (AWS, GCP, or Azure).
- Experience with infrastructure as code (Terraform, Ansible, or CloudFormation).
- Solid understanding of networking, Linux systems, and system security.
- Experience with observability tools like Prometheus, Grafana, or Datadog.
Nice to Have
- Exposure to MLOps tools (MLflow, Kubeflow, Seldon, etc.).
- Experience supporting AI/ML workloads and GPU-based infrastructure.
- Familiarity with service mesh technologies (Istio, Linkerd).
What We Offer
- Competitive salary and equity package
- Flexible remote work policy
- Opportunity to shape infrastructure for an industry-disrupting AI platform
Machine Learning Engineer
We are seeking a talented Machine Learning Engineer to join our engineering team and help us develop scalable, production-grade machine learning systems. You'll work closely with data scientists, software engineers, and DevOps to build and optimize our automation pipelines that power the AutoMLOps platform.
Key Responsibilities
- Design and implement scalable ML pipelines for training, evaluation, and deployment.
- Collaborate with data scientists to translate research models into production-ready code.
- Optimize ML models for performance, latency, and resource utilization.
- Implement monitoring and observability for ML systems in production.
- Contribute to the development of AutoMLOps' core automation features.
- Research and integrate new ML tools and frameworks into our platform.
- Participate in code reviews and maintain high code quality standards.
Qualifications
- Bachelor's or Master's degree in Computer Science, Machine Learning, or related field.
- 2+ years of experience building production ML systems.
- Strong programming skills in Python and familiarity with ML frameworks (TensorFlow, PyTorch, etc.).
- Experience with ML deployment and serving technologies.
- Understanding of ML lifecycle management and MLOps principles.
- Knowledge of software engineering best practices (version control, testing, CI/CD).
- Ability to work in a fast-paced, collaborative environment.
Nice to Have
- Experience with distributed training and large-scale ML systems.
- Familiarity with containerization and orchestration (Docker, Kubernetes).
- Background in AutoML, meta-learning, or neural architecture search.
- Contributions to open-source ML projects.
- Experience with cloud ML services (SageMaker, Vertex AI, etc.).
What We Offer
- Competitive salary and equity package
- Flexible remote work policy
- Opportunity to shape infrastructure for an industry-disrupting AI platform