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- Tasks: Own calibration and uncertainty quantification pipelines in cutting-edge AI research.
- Company: Bespoke Labs, a VC-backed applied AI startup with a focus on innovative research.
- Benefits: Competitive pay, fully remote work, and flexible hours for a balanced lifestyle.
- Other info: Inclusive environment with a commitment to diversity and career growth opportunities.
- Why this job: Join a team of experts and contribute to groundbreaking AI projects that make a real impact.
- Qualifications: PhD or active candidacy in relevant physical sciences or engineering fields.
The predicted salary is between 30000 - 40000 £ per year.
Bespoke Labs is a VC-backed applied AI research startup building core infrastructure and RL environments to train and evaluate intelligent agents. Home of OpenThoughts (100K+ monthly downloads, 200+ models trained) and Terminal Bench, a leading agentic coding benchmark used by frontier labs. Founded by ex-Google DeepMind and UC Berkeley faculty. Advised by Jeff Dean.
About the role: This role sits at the intersection of physics-based modeling and ML. You'll own calibration, inversion, deconvolution, and uncertainty quantification pipelines across multiple scientific domains — implementing directly from primary literature to build production-grade infrastructure that supports agent training and evaluation at frontier scale. This is a contractor engagement. Fully async, no core hours, milestone-based delivery.
Education requirement: PhD awarded or active candidacy (3rd year minimum) in a relevant physical science, engineering, or computational field.
Who we're looking for: Strong fits come from one of these backgrounds:
- Astrophysics research scientist — spectral fitting, telescope calibration, time‑series modeling
- Computational physicist / geophysicist — seismic inversion, atmospheric retrieval, waveform modeling
- Applied signal processing engineer — radar, sonar, RF, MEMS
- Biomedical imaging engineer — MRI, cryo‑EM, flow cytometry
- Remote sensing / earth observation engineer — LiDAR, satellite altimetry, eddy covariance
- Scientific software engineer — production pipelines around physical models
- ML research engineer (physics background) — JAX/PyTorch, physics‑informed models
- Analytical chemist / materials scientist — XRD, NMR, ellipsometry
- Quantitative neuroscientist — spike sorting, Neuropixels, electrophysiology
Domain tracks:
- Astrophysics & physical sciences: spectral energy distribution fitting, telescope calibration pipelines, seismic waveform inversion, atmospheric radiative transfer, mass spectrometry deconvolution, cryogenic detector calibration
- Engineering & signal processing: adaptive optics wavefront reconstruction, sonar beamforming, RF near‑field transformation, MEMS Allan variance characterization, RCS modeling
- Biomedical & life sciences: MRI shimming and eddy current correction, cryo‑EM CTF estimation, Neuropixels spike sorting, flow cytometry compensation, pharmacokinetic modeling
- Earth & environmental: LiDAR point cloud classification, eddy covariance flux partitioning, satellite altimetry waveform retracking, tide gauge datum transformation
- Materials & chemistry: XRD Rietveld refinement, ellipsometry model fitting, NMR multiplet analysis
Requirements:
- PhD awarded or active candidacy (3rd year minimum)
- Numerical methods depth: least squares, regularization, spectral analysis, optimization
- Python + scientific stack: NumPy, SciPy, PyTorch or JAX
- Ability to implement algorithms directly from primary literature
- Async communication and independent milestone delivery
Nice to have:
- Publication record in a relevant domain
- Instrument‑specific artifact experience (eddy currents, Allan variance, CTF estimation)
- GPU‑accelerated numerical computation
- Open‑source scientific software contributions
Education Preference: We give preference to candidates from top engineering programs, with a strong emphasis on globally recognized institutions.
Schedule: Fully async, no core hours
Compensation: Competitive rate, commensurate with experience
Our Commitment: At Bespoke Labs.AI, we believe that diverse perspectives and backgrounds make us stronger. We are proud to be an equal opportunity employer and are committed to creating an inclusive, respectful, and welcoming environment for everyone, regardless of race, gender, age, nationality, disability, sexual orientation, or any other characteristic. We are dedicated to ensuring that our hiring process is fair, transparent, and accessible. If you require any reasonable accommodations at any stage of the recruitment process, please don't hesitate to let us know — we're happy to help. All applicant data is collected and handled responsibly, in full compliance with applicable privacy laws and our internal data protection policies. Your information will only be used for recruitment purposes and will be treated with the utmost confidentiality. We're building something special at Bespoke Labs.AI, and we want the best minds, from all walks of life, to be a part of it.
Scientific ML Engineer - PhD Intern employer: Bespoke Labs
Contact Detail:
Bespoke Labs Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Scientific ML Engineer - PhD Intern
✨Tip Number 1
Network like a pro! Reach out to people in your field on LinkedIn or at conferences. A friendly chat can lead to opportunities that aren’t even advertised yet.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those related to ML and physics-based modelling. This gives potential employers a taste of what you can do.
✨Tip Number 3
Prepare for interviews by brushing up on relevant topics. Dive into the latest research papers and be ready to discuss how you can apply that knowledge to real-world problems.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who are proactive!
We think you need these skills to ace Scientific ML Engineer - PhD Intern
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to highlight your relevant experience in physics-based modelling and machine learning. We want to see how your background aligns with the role, so don’t hold back on showcasing your skills!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you’re passionate about the intersection of ML and physical sciences. We love seeing enthusiasm and a clear understanding of our work at Bespoke Labs.
Showcase Your Projects: If you've worked on any projects related to calibration, inversion, or uncertainty quantification, make sure to mention them. We’re keen to see how you’ve applied your knowledge in real-world scenarios, especially if they relate to our focus areas.
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it helps us keep everything organised!
How to prepare for a job interview at Bespoke Labs
✨Know Your Stuff
Make sure you’re well-versed in the specific scientific and ML concepts mentioned in the job description. Brush up on your knowledge of numerical methods, Python libraries like NumPy and SciPy, and any relevant algorithms. Being able to discuss these topics confidently will show that you’re not just a candidate, but a potential asset.
✨Showcase Your Projects
Prepare to talk about your previous work or projects that align with the role. If you've implemented algorithms from primary literature or worked on calibration pipelines, be ready to share those experiences. This is your chance to demonstrate how your background fits perfectly with what they’re looking for.
✨Ask Insightful Questions
Interviews are a two-way street! Prepare thoughtful questions about their current projects, team dynamics, or future goals. This not only shows your interest in the role but also helps you gauge if the company culture aligns with your values and work style.
✨Embrace Async Communication
Since this role is fully async, highlight your experience with remote work and milestone-based delivery. Discuss how you manage your time and communicate effectively in a distributed team. This will reassure them that you can thrive in their working environment.