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Our client is looking for a highly capable Machine Learning Research Engineer to join the AI R&D team and work on the development and optimization of next-generation language models for specialized AI applications. You will work across model training, fine-tuning, post-training, data development, evaluation, and inference optimization. This is a hands-on research engineering role for someone who enjoys running experiments, understanding why models behave the way they do, and turning research ideas into working models. What You will Do Fine-tune and post-train open-source language models. Experiment with supervised fine-tuning, continued pretraining, distillation, preference optimization, and reinforcement learning. Develop high-quality training datasets and synthetic data pipelines. Generate and curate high-quality reasoning and instruction datasets. Experiment with different model architectures, training strategies, and hyperparameters. Build rigorous evaluation benchmarks to measure model capabilities. Analyze model failures and identify opportunities for improvement. Optimize models for inference latency, memory usage, throughput, and cost. Experiment with models ranging from hundreds of millions to several billion parameters. Research techniques for transferring capabilities from larger models into smaller models. Work closely with AI systems engineers to integrate models into production AI systems. Reproduce relevant academic research and translate promising ideas into experiments. Maintain clear experiment tracking, documentation, and reproducible training pipelines. Required Experience Strong experience with PyTorch and modern ML frameworks. Hands-on experience fine-tuning or training language models. Strong understanding of transformer architectures. Experience with Hugging Face Transformers and related tooling. Experience with SFT, LoRA/QLoRA, knowledge distillation, or other post-training methods. Strong Python programming skills. Experience preparing and processing large-scale datasets. Strong understanding of model evaluation and benchmarking. Ability to design controlled experiments and interpret results. Nice to Have Experience with models under 7B parameters. Experience with continued pretraining. Experience with DPO, GRPO, RLHF or related methods. Experience generating synthetic training data. Experience with coding models or developer-focused AI. Experience with distributed training, FSDP, DeepSpeed or similar. Experience with model quantization and inference optimization. Publications, research projects, or meaningful open-source contributions. What We are Looking For We care less about titles and more about what you have actually built . If you have taken an open model, trained it on a new dataset, improved its capabilities, diagnosed why it failed, and iterated until you got a measurable improvement, we want to hear from you.
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.
