About the Role:

The Machine Learning team at Tubi drives the innovation behind personalized user experiences.  With the largest inventory in the industry and hundreds of millions of viewers, we tackle problems in the space of recommendations, search, content understanding and ads optimization that shape the future of streaming.

We are seeking a highly skilled Machine Learning Engineer to contribute to transformative projects in video personalization. In this role, you will design and implement advanced algorithms and systems to improve our personalization strategy. As a senior technical expert, you will tackle complex problems in machine learning at scale, collaborating closely with cross-functional teams to develop and optimize machine learning-driven solutions.

What You'll Do:

• Lead the design, development, and implementation of advanced recommendation systems and algorithms for a global audience
• Conduct deep dives into algorithmic components and systems, ensuring that models are optimized for both performance and scalability across multiple regions and product areas
• Build and deploy high-impact robust ML pipelines, including data extraction, feature development, model training, testing, and deployment
• Continuously monitor, evaluate, and optimize the performance of deployed models, ensuring they meet business goals and provide high-quality user experiences.
• Work closely with Product, Engineering, and Data Science teams to align on product requirements, set expectations, and deliver machine learning-driven solutions that improve user engagement

Your Background:

• 8+ years of industry experience building production Machine Learning systems
• MSc or Ph.D. in Computer Science, Machine Learning, Statistics, Mathematics, or a related field
• Experience with deep learning technologies for recommendation systems, including TensorFlow, PyTorch, or similar frameworks
• Proficiency in building and deploying full-stack machine learning pipelines: data extraction, data mining, model training, feature development, testing, and deployment.
• Solid understanding of statistical concepts such as hypothesis testing, regression analysis, and performance evaluation metrics for machine learning.
• Ability to deep dive into individual components and systems, as well as understand the overall architecture of machine learning solutions.

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About the Role:

The Machine Learning team at Tubi drives the innovation behind personalized user experiences. With the largest inventory in the industry and hundreds of millions of viewers, we tackle problems in the space of recommendations, search, content understanding, and ads optimization that shape the future of streaming.

We are seeking a Director of Machine Learning Engineering and Infrastructure to lead a hybrid team bridging advanced ML engineering with world-class infrastructure design. In this role, you will own the strategic direction and execution for scaling our machine learning capabilities while ensuring our distributed systems and infrastructure can support innovation at massive scale. You will combine technical depth with leadership excellence to guide teams that deliver both foundational ML systems and high-performance distributed services.

What You'll Do: 

• Lead and manage high-performing teams across ML engineering and ML infrastructure, fostering a culture of innovation, collaboration, and growth.
• Define and execute the strategic roadmap for ML systems, including recommendation, personalization, and ads optimization.
• Oversee the design, development, and deployment of scalable ML pipelines: data ingestion, feature engineering, model training, evaluation, and serving.
• Architect distributed systems to support ML workloads at scale, ensuring reliability, observability, and operational excellence.
• Partner closely with Product, Engineering, and Content teams to align on business goals and deliver impactful ML-driven experiences.
• Support best practices in experimentation, evaluation, and ML system monitoring.
• Ensure cost efficiency, scalability, and performance in ML infrastructure investments.

Your Background:

• 10+ years of industry experience spanning machine learning engineering and distributed systems.
• 3+ years of leadership and management experience, with a proven ability to build and lead strong technical teams.
• MSc or Ph.D. in Computer Science, Machine Learning, or related field, or equivalent practical experience.
• Proven expertise in building and deploying end-to-end ML systems at scale, including recommendation and personalization systems.
• Strong background in distributed systems architecture, including low-latency services, streaming platforms, and large-scale serving.
• Hands-on experience with deep learning frameworks (e.g., TensorFlow, PyTorch) and ML infrastructure technologies.
• Track record of delivering high-quality, scalable, and fault-tolerant systems.
• Excellent communication skills and ability to influence product and technical strategy.
• Proven experience deploying large-scale serving systems on AWS and demonstrated expertise in leveraging Databricks for large-scale data processing and ML workflows

About the Role:

We’re looking for a Senior Software Engineer specializing in Rust to lead the evolution of our backend platform and tooling. This role will focus on developing and maintaining critical Rust-based tools and infrastructure which could bring Tubi to its next era.

In this role, you’ll architect and implement high-performance, secure, and scalable systems, collaborating closely with teams focused on compliance, privacy, experimentation, and infrastructure. You should be comfortable working independently across system boundaries and mentoring engineers while driving technical strategy.

We’re particularly interested in engineers excited to learn and integrate AI-powered coding tools (e.g., Claude Code, Cursor) into their daily workflow to boost velocity and code quality. Prior experience with such tools is a plus.

What You'll Do:

• Lead the design, development, and maintenance of Rust services and tools.
• Collaborate with other backend & client teams to deliver high performance & scalable components/services.
• Design and optimize systems for reliability, observability, and scale, with an emphasis on clean APIs and modular components.
• Explore and integrate AI-based development tools into engineering workflows; evangelize productivity best practices within the team.
• Partner with engineering leadership and cross-functional partners to drive technical vision and influence roadmap decisions.
• Mentor engineers and foster a culture of technical rigor, curiosity, and continuous learning.

Your Background:

• 4+ years of backend software development experience, including 1+ years using Rust in production environments.
• Solid experience with Rust common stack, including but not limited to Tokio, Axum, Tonic, Hyper, Bytes, Serde, Tracing, etc. Experience with pingora stack is a big plus.
• Deep understanding of distributed systems, event-driven architectures, and performance engineering.
• Experience with cloud-native technologies (e.g., Kubernetes, gRPC, CDN, S3, Terraform) and modern observability stacks.
• Familiarity with relational and non-relational databases (e.g., PostgreSQL, DynamoDB, Redis, Clickhouse, etc.).
• Strong alignment with engineering craftsmanship — testing, documentation, reliability, and performance optimization.
• Enthusiasm for using and experimenting with AI tools to enhance software engineering workflows.

About the Role:

As a Software Engineer on the ML Infrastructure team, you will collaborate closely with the Machine Learning and Product teams to build world-class machine learning inference platforms. These platforms power essential services like personalized recommendations, search, and content understanding across Tubi.

A core responsibility of this team is developing and maintaining low-latency ML model serving systems that support Deep Learning, LLM, and Search models. This involves building self-service infrastructure and critical components such as the inference engine, feature store, vector store, and experimentation engine.

You will improve the way we deploy and operate our services and even contribute to open-source projects. This role grants the architectural freedom to explore new frameworks, lead critical cross-functional projects, and transform the capabilities of our ML and Product teams.

We currently have multiple openings;

• Staff Software Engineer
• Principal Software Engineer
  • Additional Details: As a Principal Engineer on the ML Infrastructure team, you will be a technical leader and visionary, driving the evolution of our machine learning platform. You will tackle the most complex and impactful technical challenges, shaping the architecture and technology choices that enable our ML capabilities to scale and deliver exceptional user experiences. You will be a key influencer, bridging the gap between engineering and product, and a mentor to senior engineers, fostering a culture of technical excellence and continuous improvement. Your work will be used by millions of users.

Responsibilities:

• Design and build scalable, high throughput, and low latency distributed systems using Scala
• Build reusable components and services that serve various ML applications like Personalization, Search, Ads and Exploration
• Partner closely with ML engineers to understand their challenges and limitations and develop scalable solutions to address them. Proactively recommend solutions to keep our ML Inference stack state of the art.
• Take a data driven approach to identifying & optimizing latency, cost, and efficiency of our infra. Lead large scale cross functional refactorings if necessary
• Mentor other engineers on the team on system design, effective incident management, interviewing, leveraging LLMs for work, etc.
• Collaborate with ML, Product, and cross functional engineering teams to define the long term vision and architecture for ML Infrastructure at Tubi.  

Your Background:

• Experience designing and building scalable, distributed systems in any modern backend language (e.g., Scala, Java, Python, Go, C++); experience with Scala or JVM based language is a plus.
• Strong experience with AWS or an equivalent cloud platform
• Experience building online microservices at scale with low latency serving
• Experience with both SQL (e.g. Postgres) and NoSQL databases (e.g. Cassandra), message brokers (e.g. Kafka), and caches (e.g. Redis)
• Experience with containerization technologies, such as Docker or Kubernetes
• Led the response and resolution efforts for multiple major, large-scale incidents

Bonus:

• Familiarity with the machine learning infrastructure like inference engines (e.g. torschserve, triton, vLLM), vector stores (e.g. LanceDB, FAISS), feature stores (e.g. Feast), ElastiCache, model training orchestration, etc.
• Understanding of ML model training pipelines and model internals. Experience with Recommender Systems, Search, Autocomplete and Ads ML is a plus
• Previous experience with Akka, Erlang, Elixir or Go
• Proficient in data-driven analysis of complex A/B testing results

About Tubi:

Tubi is a free streaming service that entertains over 100 million monthly active users. Tubi offers the world's largest collection of Hollywood movies and TV shows, thousands of creator-led stories and hundreds of Tubi Originals made for the most passionate fans. Headquartered in San Francisco and founded in 2014, Tubi is part of Tubi Media Group, a division of Fox Corporation.

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About the Role:

Site Reliability Engineering (SRE) at Tubi is not a traditional operations team. We are a software engineering organization that applies a developer's mindset and toolkit to the challenges of building and running large-scale, distributed systems. Our mission is to engineer resilience from the ground up, enabling our product teams to innovate rapidly while ensuring our users have a stellar experience. We own the availability, latency, performance, and capacity of our platform, and we achieve our goals through a culture of data-driven decision-making, blameless learning, and relentless automation.

We are seeking an experienced and visionary Senior SRE Manager to lead and grow our newly built Site Reliability Engineering team. You are more than a people manager or a tech lead; you are the strategic leader responsible for architecting our reliability roadmap. You will build and mentor a team of talented engineers, foster a culture of blameless learning and continuous improvement, and champion the engineering practices that allow us to balance rapid innovation with rock-solid stability. You will be a key influencer in our engineering leadership, partnering with peers across the organization to ensure reliability is a shared responsibility and a core tenet of our engineering culture.

What You'll Do:

• Team Leadership & Mentorship:
  • Lead, mentor, and grow a team of Site Reliability Engineers. Foster a culture of innovation and technical excellence where engineers feel empowered to do their best work. Provide personalized coaching, create professional development plans, and guide the careers of senior and emerging talent within the team.
  • Establish equitable, sustainable on-call practices (including global coverage where applicable) that protect focus time and avoid burnout.
  • Define team rituals - runbook reviews, game days, and incident retros - that reinforce quality and learning.
• Strategic Planning & Vision: Define and drive the multi-year technical strategy and vision for Tubi’s observability, and automation platforms. Partner with infra lead to align Tubi’s infrastructure & SRE roadmap. Partner with tech leaders to align the SRE roadmap with business objectives. Champion a data-driven approach to reliability, using Service Level Objectives (SLOs) and error budgets to facilitate productive conversations about risk and feature velocity.
• Operational Excellence & Incident Management: 
  • Own the end-to-end availability, performance, and efficiency of our critical user-facing services. Evolve our incident response practice to reduce Mean Time to Resolution (MTTR) and Mean Time Between Failures (MTBF). Champion a rigorous, blameless, and data-driven post-mortem culture to ensure we learn from both successes and failures, driving eng teams for systemic fixes and automation to prevent the recurrence of incidents.
  • Streamline and improve our existing processes and practices, and collaborate with other teams to enhance our production release standards by improving current processes.
  • Define and tune a 24×7 on-call rotation for low noise and fast response; act as executive escalation partner during major incidents.
  • Own disaster-recovery strategy (playbooks, failover drills, recovery simulations) and track SLO gaps with time-bound remediations.
• Financial & Vendor Management: Own the SRE budget, tooling, and headcount. Manage relationships with key third-party vendors for our observability and SRE related AI platforms, work with infra lead and finance team for contract negotiations and ensure we derive maximum value from our investments.
• Cross-Functional Collaboration: Act as a key influencer and strategic partner to leaders in Software Engineering, Product Management, and Infra/Sec. Drive the adoption of SRE best practices and principles throughout the organization, ensuring new services are designed for reliability, scalability, and observability from day one.
• The AI Mandate: Building the Future of Observability with AI. You will not just manage a team that uses AI; you will lead the charge in building an AI-native SRE function. This is a strategic mandate that requires a forward-thinking leader who understands both the potential and the pitfalls of integrating intelligent systems into critical operations. This includes:
  • AIOps Strategy Development: Developing and executing the strategy for integrating AIOps and machine learning into our observability stack. Your goal will be to move the team from a reactive monitoring posture to one of predictive maintenance and automated anomaly detection, fundamentally changing how we ensure reliability.
  • Accelerating Automation with AI: Championing the effective and responsible use of AI-assisted coding tools (e.g., Claude Code, Cursor) within the SRE team. You will set the standards and practices to leverage these tools to accelerate the development of automation, operational tooling, and infrastructure code.
  • Building the Business Case: Building the techno-economic case for new AI tooling, managing vendor relationships, and ensuring the cost-effective and secure implementation of these powerful systems. You must be able to articulate the ROI of these investments in terms of reduced downtime, improved operational efficiency, and faster incident resolution.
  • Fostering Critical AI Literacy: Fostering a culture that can critically evaluate, debug, and learn from the outputs of AI systems. This involves extending our blameless post-mortem philosophy to AI-driven actions and recommendations, ensuring that the team remains in control and understands the "why" behind automated decisions.

Your Background:

• 8+ years of experience in a technical field, with at least 3+ years in an engineering leadership position managing SRE, DevOps, or Production Engineering teams.
• A deep, principled understanding of SRE tenets, including Service Level Indicators (SLIs), SLOs, error budgets, toil reduction, and capacity planning.
• Exceptional communication, negotiation, and influencing skills, with the ability to articulate complex technical concepts and strategies to both technical and non-technical stakeholders at all levels of the organization.
• A strong technical background as a hands-on software engineer or site reliability engineer prior to moving into management. Deep knowledge of AWS services (especially networking, IAM, EKS, ALBs/NLBs, Route 53, CloudWatch). Proven experience with Kubernetes in production (EKS preferred), including service exposure, networking, and availability engineering.
• Hands-on familiarity with modern SRE tools and technologies, including Infrastructure as Code (e.g., Terraform, Ansible), container orchestration (Kubernetes), observability platforms (e.g., Prometheus, Grafana, Datadog, Splunk), and incident tooling (e.g., PagerDuty, FireHydrant), deployment-safety tooling (e.g., Argo Rollouts, LaunchDarkly), and observability standards (e.g., OpenTelemetry).

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