🎁 Before you apply, rehearse this interview. Create your free WorkMundi account and get an Interview Training on HelpsYouSpeak — no cost, no card. I want my training →
About the Role The core technology relies on fusing spectral signatures with visual and multi-sensor data to classify materials and drive precision recycling. As a Spectral ML Engineer , you will own the core classification models and build the online learning system that selects the most informative shot locations on physical materials. What You Will Do Spectral Preprocessing: Own baseline correction, normalization, denoising, and derivative extraction. Core Classification: Develop and optimize models spanning chemometrics baselines, 1D CNNs, and transformer architectures. Online Learning & Decision Layer: Build, deploy, and monitor sleeping and contextual multi-armed bandit policies (e.g., UCB, Thompson Sampling) to choose optimal measurement locations under dynamic arm availability, delayed/noisy rewards, and drift. Multimodal Sensor Fusion: Integrate 1D spectral data with visual and real-time streaming sensor inputs into cohesive, production-grade multimodal architectures. Evaluation & Production: Establish rigorous offline/online evaluation frameworks and regret monitoring pipelines to push algorithms directly to physical machinery in production. Requirements Education: PhD or Postdoc in Physics, Astrophysics, Materials Science, or a related quantitative field. Experience: 0–4 years post-PhD experience (new grads accepted) focused on spectroscopy, signal processing, or applied ML with spectral data. Technical Mastery: Strong Python and PyTorch proficiency. Bandits & Online Learning: Practical experience implementing bandit algorithms (UCB, Thompson sampling, sleeping/contextual bandits) and handling classification under severe class imbalance. Physics Depth: Strong foundational understanding of spectral physics and 1D sensor signal processing, rather than purely high-level applied ML. Nice to Have Spectroscopy or chemometrics experience with LIBS, Raman, NIR, or hyperspectral datasets. Hands-on experience deploying contextual bandits or reinforcement learning in live production environments. Familiarity with streaming systems, sensor fusion, and industrial measurement hardware.
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.