Engineering Manager - MLOps & Analytics

Canonical Ltd. · AnywhereRemotosenior

En corto

▸Gestiona un equipo distribuido enfocado en MLOps y análisis con herramientas de código abierto.
▸Diriges procesos, mentorías y evolución técnica del equipo, con foco en calidad, productividad y cultura.
▸Destaca por tu experiencia técnica en Python, ML y contribuciones reales al software de código abierto.

Proficiency in English is required for collaboration with global teams.

En ~1 minuto te damos: quién te entrevista, las preguntas probables con respuestas desde tu CV, y tu CV adaptado a esta vacante. Gratis, sin tarjeta.

¿Qué piden?

✓Experiencia comprobada en entrega de software.
✓Desarrollo profesional en Python, preferiblemente con contribuciones a código abierto.
✓Conocimiento sólido del espacio de machine learning y sus desafíos.
✓Experiencia en diseño e implementación de soluciones MLOps.
✓Capacidad demostrada para liderar equipos distribuidos y fomentar su crecimiento.
✓Título universitario con excelente trayectoria académica.

¿No cumplís todo? Es lo normal — tu dossier gratis te dice qué gaps tenés y cómo cubrirlos en la entrevista.

PythonKubeflowMLFlowFeastDockerLXDKubernetesAWSAzureGoogle Cloud

¿A quién escribirle en Canonical Ltd.?

Tu dossier gratis identifica a las personas que te entrevistarían — con su background, qué valoran y cómo escribirles para destacar antes de aplicar.

The role of an Engineering Manager at Canonical As an Engineering Manager at Canonical, you must be technically strong, but your main responsibility is to run an effective team and develop the colleagues you manage. You will develop and review code as a leader, while at the same time staying aware of that the best way to improve the product is to ensure that the whole team is focused, productive and unblocked. You are expected to help them grow as engineers, do meaningful work, do it outstandingly well, find professional and personal satisfaction, and work well with colleagues and the community. You will also be expected to be a positive influence on culture, facilitate technical delivery, and regularly reflect with your team on strategy and execution. You will collaborate closely with other Engineering Managers, product managers, and architects, producing an engineering roadmap with ambitious and achievable goals. We expect Engineering Managers to be fluent in the programming language, architecture, and components that their team uses, in this case popular open-source machine learning tools like Kubeflow, MLFlow, and Feast. Code reviews and architectural leadership are part of the job. The commitment to healthy engineering practices, documentation, quality and performance optimisation is as important, as is the requirement for fair and clear management, and the obligation to ensure a high-performing team. Location: This is a Globally remote role. What your day will look like Manage a distributed team of engineers and its MLOps/Analytics portfolio Organize and lead the team’s processes in order to help it achieve its objectives Conduct one-on-one meetings with team members Identify and measure team health indicators Interact with a vibrant community Review code produced by other engineers Attend conferences to represent Canonical and its MLOps solutions Mentor and grow your direct reports, helping them achieve their professional goals Work from home with global travel for 2 to 4 weeks per year for internal and external events What we are looking for in you A proven track record of professional experience of software delivery Professional python development experience, preferably with a track record in open source A proven understanding of the machine learning space, its challenges and opportunities to improve Experience designing and implementing MLOps solutions An exceptional academic track record from both high school and preferably university Willingness to travel up to 4 times a year for internal events Additional skills that you might also bring The following skills may be helpful to you in the role, but we don't expect everyone to bring all of them. Hands-on experience with machine learning libraries, or tools. Proven track record of building highly automated machine learning solutions for the cloud. Experience with building machine learning models Experience with container technologies (Docker, LXD, Kubernetes, etc.) Experience with public clouds (AWS, Azure, Google Cloud) Experience in the Linux and open-source software world Working knowledge of cloud computing Passionate about software quality and testing Experience working on a distributed team on an open source project -- even if that is community open source contributions. What we offer you We consider geographical location, experience, and performance in shaping compensation worldwide. We revisit compensation annually (and more often for graduates and associates) to ensure we recognise outstanding performance. In addition to base pay, we offer a performance-driven annual bonus. We provide all team members with additional benefits, which reflect our values and ideals. We balance our programs to meet local needs and ensure fairness globally. Distributed work environment with twice-yearly team sprints in person - we’ve been working remotely since 2004!

No apliques sin prepararte

Investigamos quién te entrevista, adaptamos tu CV y te ensayamos en vivo — gratis la primera.