Machine learning engineer Work, Jobs in Barking (with Salaries)

Research Engineer, Machine Learning (RL Velocity)
Anthropic —London
- £370,000 - £630,000 a year
- Hybrid work
- Bachelor's degree
- Machine learning
- Communication skills
- Flexible schedule
Research Engineer, Machine Learning (Reinforcement Learning)
Anthropic —London
- £260,000 - £630,000 a year
- Hybrid work
- System design
- Master's degree
- C++
- Flexible schedule
Machine Learning Platform Engineer
Monzo —London
- £85,000 - £110,000 a year
- Part-time
- Remote
- DevOps
- AWS
- Machine learning
- Flexible schedule
Senior Machine Learning Engineer (Recommendation)
Sky —London
- Full-time
- Hybrid work
- System design
- Analysis skills
- Machine learning
Graduate Machine Learning Researcher - Chicago
IMC Financial Markets —London
- £300,000 a year
- Full-time
- Doctoral degree
- Computer Science
- Machine learning
- Annual leave
Math, Physics & Engineering Graduates
Prima —London
- £80,000 - £160,000 a year
- Hybrid work
- English
- Analysis skills
- Maths
- Work from home
Quick apply
HVAC Engineer
CBRE —Dagenham
- Full-time
- Microsoft Word
- Microsoft Excel
- Microsoft Outlook
Senior Machine Learning Engineer
Longshot Systems Ltd —London
- Full-time
- Hybrid work
- Azure
- Computer Science
- AWS
- Gym membership
Quick apply
Machine Learning Scientist
Nucs AI —London
- Full-time
- Hybrid work
- Doctoral degree
- Engineering
- Machine learning
Quick apply
Anthropic Fellows Program
Anthropic —London
- £2,310 - £4,300 a week
- Part-time
- Remote
- Master's degree
- Maths
- Bachelor's degree
- Flexible schedule
Multimedia Designer / Junior Multimedia Designer *(Temp to perm)
BTCC - UK —London
- £2,200 a month
- Full-time
- English
- Microsoft Office
- Business development
- Referral programme
Quick apply
Frontend Developer - HTML, CSS, JavaScript, jQuery
Kate Matthews Solutions —London
- £45,000 - £55,000 a year
- AWS
- JavaScript
- Python
First Line Support Engineer
British Transport Police —London
- £39,871.93 a year
- Full-time
- Computer Science
- Bachelor's degree
- AI
- Annual leave
- Gym membership
Quick apply
12d
Machine Learning Specialist – Systematic Trading
Durlston Partners —London
- Up to £300,000 a year
- Doctoral degree
- Computer Science
- Data analysis skills
Software Engineer, Machine Learning Infrastructure
Deliveroo —London
- Full-time
- Doctoral degree
- Computer Science
- Master's degree
1d
Staff Machine Learning Infrastructure Engineer, Simulation
Waymo —London
- £155,000 - £163,000 a year
- Full-time
- Computer Science
- Bachelor's degree
- Machine learning
Machine Learning Engineer
Faculty —London
- Full-time
- Hybrid work
- Azure
- AWS
- Machine learning
- Annual leave
11d
Machine Learning Engineer
Waymo —London
- £120,000 - £130,000 a year
- Full-time
- Doctoral degree
- Computer Science
- Master's degree
Summer Intern - IT
Swan Mill Paper Company Ltd —Swanley
- £500 a week
- Temporary
- Computer Science
- Project management
- Computer networking
- Free parking
Quick apply
Machine Learning Engineer
G-Research —London
- Bar
- No experience needed
- Machine learning
- Annual leave

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machine learning engineer jobs near barking

Research Engineer, Machine Learning (RL Velocity)

Anthropic -
London

Apply Now

Job details

£370,000 - £630,000 a year

Benefits

UK visa sponsorship
Flexible schedule

Qualifications

PyTorch
Bachelor's degree
Machine learning
Distributed systems
Communication skills

Full job description

About Anthropic

Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.

About the role

The RL Velocity team owns the efficiency and reliability of our RL Science stack - the infrastructure, tooling, and systems that let researchers iterate quickly on training runs. As a Research Engineer on the team, you'll build and improve the core platform that underpins how we do RL at Anthropic, removing bottlenecks that slow down research and making it easier for the broader org to ship better models faster. This is high-leverage work: small improvements to velocity compound across every researcher and every run.

Responsibilities

Build and improve the RL training infrastructure that researchers depend on day-to-day
Identify and remove bottlenecks across the RL stack: debugging, profiling, and rearchitecting where needed
Partner closely with researchers and with adjacent engineering teams (inference, sandboxing, and many more) to understand pain points and ship tooling that makes them faster
Own the reliability and performance of research runs end-to-end
Contribute to design decisions that shape how Anthropic does RL at scale

You may be a good fit if you

Have strong software engineering fundamentals and a track record of building performant, reliable systems
Have worked on ML infrastructure, distributed systems, or research tooling
Care about enabling other people's work and find leverage through platforms rather than individual experiments
Are comfortable operating across the stack, from low-level performance work to RL algorithms
Have a bias toward shipping and iterating quickly, with a mix of high agency and low ego

Strong candidates may also have

Experience with large-scale distributed training (RL, pre-training, or post-training)
Familiarity with JAX, PyTorch, or similar ML frameworks
A track record of operating at the edge of research and infra in a fast-moving environment

Deadline to apply: None. Applications will be reviewed on a rolling basis.

The annual compensation range for this role is listed below.

For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.

Annual Salary:

£370,000 - £630,000 GBP

Logistics

Minimum 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.

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. 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.

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 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.

The 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.

Come work with us!

Anthropic 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. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.

Apply Now

Refine Your Search

machine learning engineer jobs in barking

Research Engineer, Machine Learning (RL Velocity)

Research Engineer, Machine Learning (Reinforcement Learning)

Machine Learning Platform Engineer

Senior Machine Learning Engineer (Recommendation)

Graduate Machine Learning Researcher - Chicago

Math, Physics & Engineering Graduates

HVAC Engineer

Senior Machine Learning Engineer

Machine Learning Scientist

Anthropic Fellows Program

Multimedia Designer / Junior Multimedia Designer *(Temp to perm)

Frontend Developer - HTML, CSS, JavaScript, jQuery

First Line Support Engineer

Machine Learning Specialist – Systematic Trading

Software Engineer, Machine Learning Infrastructure

Staff Machine Learning Infrastructure Engineer, Simulation