As a Senior Software Engineer on the Application Traffic team, you will design and build the systems that power Databricks' service-to-service communication across thousands of clusters in a multi-cloud environment. You will also help create abstractions that hide networking complexity from product teams, making connectivity, discovery, and reliability seamless by default.
\nYou'll work across three key areas that define Databricks' networking stack:
\nIngress Control Plane: Build the control plane for Databricks' global ingress layer. Enable programming of API gateways with static and dynamic endpoints, simplify service onboarding, and make it easy to expose APIs securely across clouds.
\nService-to-Service Communication: Design scalable mechanisms for service discovery and load balancing across thousands of clusters. Provide networking abstractions so product teams don't need to worry about underlying connectivity details.
\nOverload Protection: Build intelligent rate limiting and admission control systems to protect critical services under high load. Ensure reliability and predictable performance for both customer-facing and internal workloads.
\nWe're looking for someone with a strong proficiency in one or more languages such as Java, Scala, Go, or C++, and experience with service-oriented architectures and large scale distributed systems. Familiarity with cloud platforms (AWS, Azure, GCP) and container/orchestration technologies (Kubernetes, Docker) is also required. A track record of shipping infrastructure that supports mission-critical workloads at scale is essential.
\nThe pay range for this role is $166,000-$225,000 USD.
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\nYou want to build the cutting-edge systems that train AI models like Claude. You're excited to work at the frontier of machine learning, implementing and improving advanced techniques to create ever more capable, reliable and steerable AI.
\nAs an ML Systems Engineer on our Reinforcement Learning Engineering team, you'll be responsible for the critical algorithms and infrastructure that our researchers depend on to train models. Your work will directly enable breakthroughs in AI capabilities and safety.
\nYou'll focus obsessively on improving the performance, robustness, and usability of these systems so our research can progress as quickly as possible.
\nOur finetuning researchers train our production Claude models, and internal research models, using RLHF and other related methods. Your job will be to build, maintain, and improve the algorithms and systems that these researchers use to train models.
\nYou'll be responsible for improving the speed, reliability, and ease-of-use of these systems.
\nStrong candidates may also have experience with:
\nHigh performance, large scale distributed systems Large scale LLM training Python Implementing LLM finetuning algorithms, such as RLHF
\nRepresentative projects:
\nProfiling our reinforcement learning pipeline to find opportunities for improvement Building a system that regularly launches training jobs in a test environment so that we can quickly detect problems in the training pipeline Making changes to our finetuning systems so they work on new model architectures Building instrumentation to detect and eliminate Python GIL contention in our training code Diagnosing why training runs have started slowing down after some number of steps, and fixing it Implementing a stable, fast version of a new training algorithm proposed by a researcher
\nDeadline to apply: None. Applications will be reviewed on a rolling basis.
\nThe annual compensation range for this role is $500,000-$850,000 USD.
\nLogistics:
\nMinimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position 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.
\nWe 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.
\nResearch 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.
\nWe think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team.
\nYour 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.
\nHow we're different:
\nWe 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 research efforts. And we value impact , advancing our long-term goals of steerable, trustworthy AI , rather than work on smaller and more specific puzzles.
\nWe view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time.
\nAs such, we greatly value communication skills.
\nThe easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences.
\nCome work with us!
\nAnthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues.
\nGuidance on Candidates' AI Usage: Learn about our policy for using AI in our application process
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\nOur finetuning researchers train our production Claude models, and internal research models, using RLHF and other related methods. Your job will be to build, maintain, and improve the algorithms and systems that these researchers use to train models. You’ll be responsible for improving the speed, reliability, and ease-of-use of these systems.
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\nStrong candidates may also have experience with:
\nRepresentative projects:
\nLogistics
\nEducation 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.
\nVisa 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.
\nWe 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. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team.
\nYour 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.
\nHow we're different
\nWe 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 research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills.
\nThe easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior
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