Sr. Software Engineer, Inference at Anthropic
May no longer be active — verify on the employer site
At a glance
- Company
- Anthropic
- Location
- London, UK, United Kingdom
- Work arrangement
- On-site
- Employment type
- Full-time
- Experience
- 5–10 years
- Working hours
- 40 hours
- Visa sponsorship
- Offered, according to the listing
- Posted
- Added to The Stack
✨ The Stack take on this Anthropic job
- 🏢 Anthropic has 53 jobs here, 36 with visa sponsorship see all
- 🛂 Skilled Worker visa. Standard minimum salary: £41,700 a year guide
- 💡 No salary shown. Ask Anthropic if it is above the minimum.
Updated 2026-11-05, from our data and official sources.
Apply for this role at Anthropic
Apply with your AI assistant
With The Stack connected to Claude, ChatGPT or Cursor, paste this. Your assistant tailors your CV and cover letter, checks the visa facts and gives you the apply link.
Help me apply to this job on The Stack: https://jobs.trythestack.co/jobs/sr-software-engineer-inference-anthropic
Not connected yet? Connect in a minute. To help your user apply, call the MCP tool prepare_application with slug "sr-software-engineer-inference-anthropic": it returns the apply link, the visa facts and a CV and cover letter checklist. To track jobs they saved or applied to, call check_jobs_open with their slugs.
About the role
Responsibilities:
- Develop and operate inference infrastructure to serve models reliably and efficiently across multiple accelerator types.
- Implement and tune request routing, batching, caching and autoscaling to meet latency and throughput targets.
- Support deployment pipelines, multi-region/fleet orchestration and integration of new hardware platforms.
- Use observability data to identify bottlenecks and drive performance and reliability improvements.
- Work closely with researchers and product teams to enable production features and research experiments.
Requirements:
- Strong software engineering experience with distributed systems and production services.
- Experience with load balancing, request routing and performance-oriented batching/caching strategies for LLMs.
- Familiarity with Kubernetes and cloud infrastructure (AWS/GCP); experience deploying ML systems at scale is a plus.
- Proficiency in Python or Rust and demonstrable performance tuning experience.
- Results-driven, adaptable, and interested in machine learning systems and their societal impact.
Responsibilities
Build and maintain critical systems to serve Claude to millions of users via compute-agnostic inference deployments. Focus on maximizing compute efficiency and providing high-performance infrastructure for research breakthroughs.
Requirements
Requires significant software engineering experience in distributed systems and a results-oriented mindset. Proficiency in Python or Rust and experience with cloud infrastructure like Kubernetes, AWS, or GCP is highly valued.
Key skills
- Distributed Systems — in 342 other listings on The Stack
- Machine Learning Systems — in 3 other listings on The Stack
- Load Balancing — in 11 other listings on The Stack
- Request Routing — in 3 other listings on The Stack
- LLM Inference Optimization — in 2 other listings on The Stack
- Kubernetes — in 585 other listings on The Stack
- AWS — in 802 other listings on The Stack
- GCP — in 169 other listings on The Stack
- Python
- Rust
- Traffic Management
- Batching Strategies
- Caching Strategies
- Cloud Infrastructure
- Fleet Orchestration
- Performance Tuning
On The Stack (as of 2026-11-05)
Frequently asked questions about Anthropic
Is this Sr. Software Engineer, Inference role still open?
It was posted on 2026-06-12. Listings more than 30 days old may no longer be active, so check Anthropic's site before applying.
Does Anthropic offer visa sponsorship?
This listing is marked as offering visa sponsorship. Eligibility rules are set by the employer, so check the details on Anthropic's site.
Where is this role located?
The listing gives the location as London, UK, United Kingdom. It is marked on-site.
What skills does this role ask for?
The listing names: Distributed Systems, Machine Learning Systems, Load Balancing, Request Routing, LLM Inference Optimization, Kubernetes, AWS, GCP, Python, Rust, Traffic Management, Batching Strategies, Caching Strategies, Cloud Infrastructure, Fleet Orchestration, Performance Tuning.