In this role, you will be responsible for designing and developing customizations, extensions, configurations, and integrations required to meet the company’s strategic business objectives. You will work collaboratively with Engineering Managers, business stakeholders, Product Owners, Program analysts, and engineers on different program design, development, deployment, and support.

Core competencies expected of a Staff Engineer include operating with a high degree of autonomy, technical leadership, and project ownership. This includes architectural ownership and design, project and delivery leadership, mentorship and technical bar-setting, cross-functional influence, and future-forward technical skills.

High-value skills include the ability to build Agents using Agentforce or by leveraging open source libraries to build agents, proficiency in using GitHub Copilot or Cursor or AI workflow orchestration tools, and strategic influence on technology roadmap.

Qualifications include 7+ years of software development experience with experience in Java, Python, or equivalent, 5+ years' hands-on Salesforce development with solid knowledge of Apex, Process Automation, and LWC, and experience in architecture, design, and implementation of various high-complexity projects/programs for Sales Cloud, CPQ, Service Cloud Console, etc.

Our team is collaborative, innovative, and flexible, and we consider work-life balance a top priority.

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Responsibilities:

• Design and implement large-scale infrastructure systems to support AI scientist training, evaluation, and deployment across distributed environments
• Identify and resolve infrastructure bottlenecks impeding progress toward scientific capabilities
• Develop robust and reliable evaluation frameworks for measuring progress towards scientific AGI
• Build scalable and performant VM/sandboxing/container architectures to safely execute long-horizon AI tasks and scientific workflows
• Collaborate to translate experimental requirements into production-ready infrastructure
• Develop large scale data pipelines to handle advanced language model training requirements
• Optimize large scale training and inference pipelines for stable and efficient reinforcement learning

You may be a good fit if you:

• Have 6+ years of highly-relevant experience in infrastructure engineering with demonstrated expertise in large-scale distributed systems
• Are a strong communicator and enjoy working collaboratively
• Possess deep knowledge of performance optimization techniques and system architectures for high-throughput ML workloads
• Have experience with containerization technologies (Docker, Kubernetes) and orchestration at scale
• Have proven track record of building large-scale data pipelines and distributed storage systems
• Excel at diagnosing and resolving complex infrastructure challenges in production environments
• Can work effectively across the full ML stack from data pipelines to performance optimization
• Have experience collaborating with other researchers to scale experimental ideas
• Thrive in fast-paced environments and can rapidly iterate from experimentation to production

Strong candidates may also have:

• Experience with language model training infrastructure and distributed ML frameworks (PyTorch, JAX, etc.)
• Background in building infrastructure for AI research labs or large-scale ML organizations
• Knowledge of GPU/TPU architectures and language model inference optimization
• Experience with cloud platforms (AWS, GCP) at enterprise scale
• Familiarity with VM and container orchestration
• Experience with workflow orchestration tools and experiment management systems
• History working with large scale reinforcement learning
• Comfort with large scale data pipelines (Beam, Spark, Dask, …)

The annual compensation range for this role is $350,000-$850,000 USD.

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We believe in a world where homes are owned by regular people, not corporations. Our mission is to provide authentic belonging experiences, empowering residents to become homeowners and homeowners to achieve financial freedom.

The Role

Belong is looking for an AI Automation Engineer to help transform real-world operations through practical, high-impact AI solutions. You’ll be building and shipping AI-powered workflows that directly improve how our teams operate and how our customers experience Belong.

Responsibilities

• Build AI-powered applications and workflows that automate and enhance real-world business operations, including evaluation and safety mechanisms.
• Rapidly prototype AI-driven solutions, validate them in real scenarios, and evolve them into production-ready systems.
• Integrate AI capabilities into backend services, internal tools, and external platforms through well-designed APIs and services.
• Own AI-driven initiatives end to end, from early experimentation to production deployment, proactively leveraging AI code generation tools to confidently contribute across the backend and frontend stack when needed.
• Work closely with product, operations, customer support and engineering teams to identify automation opportunities and deliver meaningful impact.

What We’re Looking For

• Strong programming skills in Python and/or TypeScript.
• Solid software engineering fundamentals and experience building and shipping production systems.
• Experience deploying, operating, and iterating on AI-powered applications.
• Familiarity with modern AI tooling, agent frameworks and workflow orchestration tools.
• A proactive mindset with a strong sense of ownership and the ability to drive initiatives forward.
• Clear communication skills and a collaborative approach to working in cross-functional teams.

Why Belong

• We’re tackling one of the biggest, most broken industries (housing) and creating something entirely new.
• You’ll work alongside world-class founders and leaders who have scaled successful companies.
• AI isn’t a side project here, it’s at the core of our strategy and product roadmap.
• Competitive compensation, equity, and benefits.
• Ownership, autonomy, and the opportunity to build something that matters.

If you’re excited about building practical AI solutions, owning problems end to end, and pushing what’s possible in real-world operations, we’d love to talk. Apply now.

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As a Research Engineer on our team, you will work end to end across the whole model stack, identifying and addressing key infra blockers on the path to scientific AGI. Strong candidates should have familiarity with elements of language model training, evaluation, and inference and eagerness to quickly dive and get up to speed in areas they are not yet an expert on. This may include performance optimization, distributed systems, VM/sandboxing/container deployment, and large scale data pipelines.

Responsibilities:

• Design and implement large-scale infrastructure systems to support AI scientist training, evaluation, and deployment across distributed environments
• Identify and resolve infrastructure bottlenecks impeding progress toward scientific capabilities
• Develop robust and reliable evaluation frameworks for measuring progress towards scientific AGI.
• Build scalable and performant VM/sandboxing/container architectures to safely execute long-horizon AI tasks and scientific workflows
• Collaborate to translate experimental requirements into production-ready infrastructure
• Develop large scale data pipelines to handle advanced language model training requirements
• Optimize large scale training and inference pipelines for stable and efficient reinforcement learning

You may be a good fit if you:

• Have 6+ years of highly-relevant experience in infrastructure engineering with demonstrated expertise in large-scale distributed systems
• Are a strong communicator and enjoy working collaboratively
• Possess deep knowledge of performance optimization techniques and system architectures for high-throughput ML workloads
• Have experience with containerization technologies (Docker, Kubernetes) and orchestration at scale
• Have proven track record of building large-scale data pipelines and distributed storage systems
• Excel at diagnosing and resolving complex infrastructure challenges in production environments
• Can work effectively across the full ML stack from data pipelines to performance optimization
• Have experience collaborating with other researchers to scale experimental ideas
• Thrive in fast-paced environments and can rapidly iterate from experimentation to production

Strong candidates may also have:

• Experience with language model training infrastructure and distributed ML frameworks (PyTorch, JAX, etc.)
• Background in building infrastructure for AI research labs or large-scale ML organizations
• Knowledge of GPU/TPU architectures and language model inference optimization
• Experience with cloud platforms (AWS, GCP) at enterprise scale
• Familiarity with VM and container orchestration.
• Experience with workflow orchestration tools and experiment management systems
• History working with large scale reinforcement learning
• Comfort with large scale data pipelines (Beam, Spark, Dask, …)

Logistics

• Education requirements: We require at least a Bachelor's degree in a related field or equivalent experience.
• Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.
• Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.

We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work.

Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings.

How we're different

We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale projects, and we're committed to making a positive impact on the world.

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