Senior AI Research Engineer

About Us

At SkyLabs AI Inc., we are at the forefront of the artificial intelligence revolution. As a US-headquartered company, we conduct applied research on AI for intelligent reasoning. We specialize in complex neurosymbolic AI to solve intricate problems within software engineering. Our team is composed of world-class researchers and engineers dedicated to building the platforms and intelligent agents that will power the next generation of software. If you are passionate about building truly intelligent systems and want to make a lasting impact, join us.

The Role

We are seeking an exceptionalSenior AI Research Engineerwith a strong focus ontraining and improving LLMs across the full lifecycle—fromDomain‑Adaptive PretrainingtoSupervised Fine‑Tuning (SFT),RL/RLVR, and advanced post‑training techniques (e.g., reward modeling, preference optimization, RL‑VR style workflows). You will lead hands‑on training efforts, contribute to research direction, and build robust pipelines for data, training, evaluation, and deployment.

You should havein‑depth understanding of LLM internals(attention/MLP dynamics, normalization, optimization behavior, scaling laws), and modern architectures includingMixture of Experts (MoE)and other frontier design choices. You will work closely with product and platform teams to translate research ideas into performant systems.

Requirements

Key Responsibilities

• Lead the team of AI Engineers/Researchers
• Train and iterate on LLMs end-to-endacross DAPT/CPT, SFT, and RL-based post-training (preference optimization, reward modeling, verifiable rewards, policy optimization, and related variants).
• Design training recipes: tokenization strategy, curriculum/sampling, batch/sequence packing, optimizer + scheduler choices, stability techniques, and hyperparameter search.
• Architect and implement efficient training systemsusing distributed training (data/tensor/pipeline parallelism), FSDP/ZeRO, activation checkpointing, mixed precision, and throughput optimization.
• Develop and maintain LLM data pipelines: large-scale data ingestion, filtering, deduplication, contamination checks, domain balancing, safety filtering, and dataset versioning.
• Perform deep EDA and root-cause analysisfor training/eval regressions (loss spikes, instability, alignment drift, memorization, toxicity, domain overfitting), and implement mitigations.
• Build evaluation and benchmarking suites: offline metrics, human preference pipelines, automatic evals, domain-specific test sets, adversarial probing, and model behavior tracking over time.
• Apply and advance model compression and efficiency: distillation from LLMs, quantization-aware considerations, pruning/sparsity (including MoE routing behavior), and inference-time optimization tradeoffs.
• Implement and operationalize LLMOps: experiment tracking, reproducible runs, model registry, training telemetry, artifact management, CI for training configs, and safe rollout strategies.
• Leverage AI coding tools(e.g., Cursor, Copilot) to accelerate development while maintaining strong engineering rigor, testability, and code review standards.
• Collaborate cross-functionallywith platform, infra, and product teams to integrate trained models into agentic/production pipelines (retrieval, tool use, evaluation gates, monitoring).
• Stay current with the fieldand translate new ideas (architectures, post-training, data strategies, evals) into internal experiments and improvements.

Qualifications & Skills

• 5+ yearsin ML engineering / applied research, withsignificant hands-on experience training large models, especially LLMs.
• Strong practical knowledge oftransformer internalsand modern variations (MoE, routing, load balancing, attention variants, normalization choices, long-context strategies).
• Demonstrated experience withDAPT/CPT,SFT, andRL-based post-training(e.g., reward modeling, preference datasets, verifiable rewards, policy optimization). Ability to reason about why a method works and when it fails.
• Excellent understanding ofdata science fundamentals: EDA, dataset design, bias/contamination, sampling strategies, and error analysis.
• Proven ability toroot-cause and debugtraining instabilities and performance regressions (numerics, data issues, infra bottlenecks, config drift).
• Expert-level programming inPython, with strong software engineering fundamentals (data structures, algorithms, systems thinking).
• Experience with key tooling such asPyTorch, distributed training frameworks (FSDP/DeepSpeed/ZeRO), and training orchestration.
• Familiarity withLLMOps: tracking (e.g., W&B/MLflow), dataset versioning, reproducibility, deployment constraints, and monitoring of model behavior.
• Comfortable buildingagentic pipelinesaround LLMs (evaluation agents, data generation/labeling loops, model-in-the-loop distillation, automated red-teaming).
• Bonus: publications, open-source contributions, or demonstrated leadership in shipping LLM training improvements.

Who You Are

You’re a pragmatic researcher-engineer: you can read papers, run experiments, and also build the training and data systems that make results reproducible and shippable. You’re meticulous about data and measurement, fast at debugging, and comfortable making tradeoffs between quality, cost, and timeline. You communicate clearly, mentor others, and thrive in an environment where you’re trusted to drive big initiatives.

Benefits

• Salaries in USD (income tax exemptions)
• Work in Pakistan Timezone
• Comprehensive health allowance
• Monthly team events and activities
• Relocation allowance (if you're moving to Islamabad)
• Opportunity to work with top minds in the industry and academia
• Startup culture where ideas are heard at all levels
• Adequate annual, sick, casual and parental leaves