Senior Data Scientist (Transformers/Deep Learning)

Akaike Technologies; Bengaluru; On-site; INR 1,500,000 - 2,500,000; Full time

Job summary

Akaike Technologies seeks a Senior Data Scientist in Bengaluru with 4+ years' experience in Transformer-based models. This role involves analyzing architectures, training and fine-tuning models, and implementing optimization techniques. The ideal candidate will have strong research and engineering skills, a deep understanding of Transformers, and hands-on experience with model training. The position is full-time and emphasizes core engineering, experimentation, and improving model efficiency and behaviors.

Qualifications

• Deep theoretical and implementation-level understanding of Transformers.
• Practical experience with parameter-efficient fine-tuning techniques.
• Hands-on experience with training or fine-tuning models.

Responsibilities

• Analyze and improve Transformer architectures and training strategies.
• Train and fine-tune models using custom pipelines.
• Implement optimization techniques for model efficiency.

Skills

Advanced Understanding of Transformer Architectures

Intermediate-Level PEFT Expertise

Model Training and Modification

Core Engineering and Research Skills

Tools

PyTorch

Job description

Senior Data Scientist (Transformers/Deep Learning)

Senior Data Scientist – 4+ Years Experience

4+ years

Full-Time

Role Overview

We are looking for a Senior Data Scientist with 4+ years of experience and strong hands‑on expertise working with Transformer-based models beyond API usage. This role sits between research and engineering, focusing on understanding, training, modifying, and improving models rather than simply integrating them.

The ideal candidate is comfortable working deep inside model architectures, training pipelines, fine‑tuning methods, and inference optimization, with a strong first‑principles mindset.

Eligibility Requirement (Read Carefully)

Applicants must already have prior hands‑on experience training or modifying Transformer-based models or related systems, either open‑source or in‑house.

Candidates whose experience is limited to using hosted APIs or prompting models without working at the training or architecture level should not apply.

Must Have

Advanced Understanding of Transformer Architectures

Deep theoretical and implementation‑level understanding of Transformers, including:

• Encoder–Decoder and Decoder‑only architectures
• Attention mechanisms and positional encodings
• Training dynamics and scaling behavior
• Strong understanding of common limitations such as context length constraints, efficiency bottlenecks, and hallucinations, along with approaches to mitigate them.

Intermediate‑Level PEFT Expertise

Practical experience with parameter‑efficient fine‑tuning techniques, including:

• LoRA
• QLoRA
• Adapter‑based methods
• Clear understanding of trade‑offs between PEFT approaches and full fine‑tuning.

Model Training and Modification (Mandatory)

Hands‑on experience with:

• Training or fine‑tuning models from checkpoints or from scratch
• Implementing and customizing training loops
• Designing or modifying loss functions and optimization strategies
• Fine‑tuning without reliance on hosted APIs

Core Engineering and Research Skills

Strong experience with PyTorch (preferred), GPU training workflows and performance debugging, ability to read and implement research papers, experience diagnosing training instability and model failures, designing experiments and evaluating model behavior.

Key Responsibilities

• Analyze and improve Transformer architectures and training strategies
• Train and fine‑tune models using custom pipelines
• Implement optimization techniques such as mixed precision, quantization, and pruning
• Improve inference efficiency across latency, memory, and throughput
• Run hypothesis‑driven experiments and document findings

Good to Have

Experience with multimodal or generative models, including:

• Vision or audio transformers
• Image, video, or audio generation systems

Additional strengths include:

• Experience modifying model architectures, attention mechanisms, or training objectives
• Familiarity with efficient attention implementations
• Contributions to open‑source machine learning or independent research projects

Ideal Candidate

• Thinks like a researcher and builds like an engineer
• Curious about why models fail, not just how to use them
• Comfortable experimenting, iterating, and improving systems