senior research engineer

Skylabs AI

<div>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 exceptional Senior AI Research Engineer with a strong focus on training and improving LLMs across the full lifecycle&mdash;from Domain‑Adaptive Pretraining to Supervised 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 have in‑depth understanding of LLM internals (attention/MLP dynamics, normalization, optimization behavior, scaling laws), and modern architectures including Mixture 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<ul>
<li>
Lead the team of AI Engineers/Researchers
</li><li>Train and iterate on LLMs end-to-end across DAPT/CPT, SFT, and RL-based post-training (preference optimization, reward modeling, verifiable rewards, policy optimization, and related variants).
</li><li>Design training recipes: tokenization strategy, curriculum/sampling, batch/sequence packing, optimizer + scheduler choices, stability techniques, and hyperparameter search.
</li><li>Architect and implement efficient training systems using distributed training (data/tensor/pipeline parallelism), FSDP/ZeRO, activation checkpointing, mixed precision, and throughput optimization.
</li><li>Develop and maintain LLM data pipelines: large-scale data ingestion, filtering, deduplication, contamination checks, domain balancing, safety filtering, and dataset versioning.
</li><li>Perform deep EDA and root-cause analysis for training/eval regressions (loss spikes, instability, alignment drift, memorization, toxicity, domain overfitting), and implement mitigations.
</li><li>Build evaluation and benchmarking suites: offline metrics, human preference pipelines, automatic evals, domain-specific test sets, adversarial probing, and model behavior tracking over time.
</li><li>Apply and advance model compression and efficiency: distillation from LLMs, quantization-aware considerations, pruning/sparsity (including MoE routing behavior), and inference-time optimization tradeoffs.
</li><li>Implement and operationalize LLMOps: experiment tracking, reproducible runs, model registry, training telemetry, artifact management, CI for training configs, and safe rollout strategies.
</li><li>Leverage AI coding tools (e.g., Cursor, Copilot) to accelerate development while maintaining strong engineering rigor, testability, and code review standards.
</li><li>Collaborate cross-functionally with platform, infra, and product teams to integrate trained models into agentic/production pipelines (retrieval, tool use, evaluation gates, monitoring).
</li><li>Stay current with the field and translate new ideas (architectures, post-training, data strategies, evals) into internal experiments and improvements.
</li></ul>Qualifications &amp; Skills<ul>
<li>
5+ years in ML engineering / applied research, with significant hands-on experience training large models, especially LLMs.
</li><li>Strong practical knowledge of transformer internals and modern variations (MoE, routing, load balancing, attention variants, normalization choices, long-context strategies).
</li><li>Demonstrated experience with DAPT/CPT, SFT, and RL-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.
</li><li>Excellent understanding of data science fundamentals: EDA, dataset design, bias/contamination, sampling strategies, and error analysis.
</li><li>Proven ability to root-cause and debug training instabilities and performance regressions (numerics, data issues, infra bottlenecks, config drift).
</li><li>Expert-level programming in Python, with strong software engineering fundamentals (data structures, algorithms, systems thinking).
</li><li>Experience with key tooling such as PyTorch, distributed training frameworks (FSDP/DeepSpeed/ZeRO), and training orchestration.
</li><li>Familiarity with LLMOps: tracking (e.g., W&amp;B/MLflow), dataset versioning, reproducibility, deployment constraints, and monitoring of model behavior.
</li><li>Comfortable building agentic pipelines around LLMs (evaluation agents, data generation/labeling loops, model-in-the-loop distillation, automated red-teaming).
</li><li>Bonus: publications, open-source contributions, or demonstrated leadership in shipping LLM training improvements.
</li></ul>Who You Are
You&rsquo;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&rsquo;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&rsquo;re trusted to drive big initiatives.
Benefits<ul>
<li>
Salaries in USD (income tax exemptions)</li>
<li>Work in Pakistan Timezone</li>
<li>Comprehensive health allowance</li>
<li>Monthly team events and activities</li>
<li>Relocation allowance (if you're moving to Islamabad)</li>
<li>Opportunity to work with top minds in the industry and academia</li>
<li>Startup culture where ideas are heard at all levels</li>
<li>Adequate annual, sick, casual and parental leaves</li></ul></div>

Senior AI Research Engineer

<div>KBR is seeking a Senior Reverse Engineer to provide technical leadership across reverse engineering and vulnerability research efforts. This role emphasizes system‑level thinking, architectural judgment, and customer empathy while intentionally developing engineers with defined, mission‑aligned technical proficiencies. The Senior Reverse Engineer designs scalable solutions that leverage industry advances, support current mission objectives, and enable long‑term capability growth and future customer requirements.
Key Responsibilities:<ul><li>
Lead and mentor a team of reverse engineers, intentionally shaping individual development into defined technical proficiencies</li><li>
Conduct and oversee full‑scope reverse engineering across hardware, firmware, software, and network protocols</li><li>
Design and guide scalable system architectures, tools, frameworks, and methodologies</li><li>
Analyze complex system architectures and component interactions to identify vulnerabilities, attack vectors, and resilient solution opportunities</li><li>
Collaborate directly with customers to understand near‑term technical needs and long‑term mission objectives</li><li>
Communicate technical findings and design decisions clearly to technical and non‑technical stakeholders</li><li>
Research emerging reverse engineering techniques and threat landscapes to inform strategic technical direction</li></ul>
Minimum Qualifications:<ul><li>
Education: Master&rsquo;s degree in Computer Engineering, Electrical Engineering, or a related field</li><li>
Security Clearance: Must have an active U.S. government Secret security clearance, which is something only a U.S. citizen can obtain</li><li>
10+ years of experience, including at least 5 years in reverse engineering</li><li>
Strong knowledge of computer architecture, operating systems, and software development</li><li>
Expert‑level proficiency in assembly, C/C++, Python, and related languages</li><li>
Advanced experience with reverse engineering tools such as IDA Pro, Ghidra, Binary Ninja, and debuggers</li><li>
Knowledge of network protocols, cryptography, and security vulnerabilities</li><li>
Demonstrated leadership, mentorship, and customer engagement experience</li></ul>
Preferred Qualifications:<ul><li>
Experience in hardware reverse engineering, firmware analysis, and embedded systems</li><li>
Embedded system emulation, hardware‑in‑the‑loop development, or creation of reusable test harnesses</li><li>
Published research or presentations at security conferences</li></ul>
Basic Compensation: $142,000 - $213,000 (For Beavercreek, OH Only) 
The offered rate will be based on the selected candidate&rsquo;s knowledge, skills, abilities and/or experience and in consideration of internal parity. 
 
Additional Compensation: 
KBR may offer bonuses, commissions, or other forms of compensation to certain job titles or levels, per internal policy or contractual designation. Additional compensation may be in the form of a sign on bonus, relocation benefits, short-term incentives, long-term incentives, or discretionary payments for exceptional performance.
Belong, Connect and Grow at KBR 
 
At KBR, we are passionate about our people and our Zero Harm culture. These inform all that we do and are at the heart of our commitment to, and ongoing journey toward being a People First company. That commitment is central to our team of team&rsquo;s philosophy and fosters an environment where everyone can Belong, Connect and Grow. We Deliver &ndash; Together.
KBR is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, disability, sex, sexual orientation, gender identity or expression, age, national origin, veteran status, genetic information, union status and/or beliefs, or any other characteristic protected by federal, state, or local law.</div>

Senior Reverse Engineer

Brillio LLC

<div><h1 class="jobSectionHeader">Senior AI/ML Engineer - R01560293</h1>
<div>Senior AI/ML Engineer</div><h6 class="jobSectionHeader">
Primary Skills</h6><ul><li>
Classification (Decision Trees, SVM), Distance (Hamming Distance, Euclidean Distance, Manhattan Distance), Forecasting (Exponential Smoothing, ARIMA, ARIMAX), Great Expectation, Evidently AI, Hypothesis Testing, ML Frameworks (TensorFlow, PyTorch, Sci-Kit Learn, CNTK, Keras, MXNet), Probabilistic Graph Models, Python/PySpark, R/ R Studio, Regression (Linear, Logistic), SAS/SPSS, Statistical analysis and computing, Tools(KubeFlow, BentoML), T-Test, Z-Test
</li></ul><h6 class="jobSectionHeader">Job requirements</h6><ul><li>
JD is below: The Agentic AI Lead is a pivotal role responsible for driving the research, development, and deployment of semi-autonomous AI agents to solve complex enterprise challenges. This role involves hands-on experience with LangGraph, leading initiatives to build multi-agent AI systems that operate with greater autonomy, adaptability, and decision-making capabilities. The ideal candidate will have deep expertise in LLM orchestration, knowledge graphs, reinforcement learning (RLHF/RLAIF), and real-world AI applications. As a leader in this space, they will be responsible for designing, scaling, and optimizing agentic AI workflows, ensuring alignment with business objectives while pushing the boundaries of next-gen AI automation. ________________________________________ Key Responsibilities 1. Architecting &amp; Scaling Agentic AI Solutions &bull; Design and develop multi-agent AI systems using LangGraph for workflow automation, complex decision-making, and autonomous problem-solving. &bull; Build memory-augmented, context-aware AI agents capable of planning, reasoning, and executing tasks across multiple domains. &bull; Define and implement scalable architectures for LLM-powered agents that seamlessly integrate with enterprise applications. 2. Hands-On Development &amp; Optimization &bull; Develop and optimize agent orchestration workflows using LangGraph, ensuring high performance, modularity, and scalability. &bull; Implement knowledge graphs, vector databases (Pinecone, Weaviate, FAISS), and retrieval-augmented generation (RAG) techniques for enhanced agent reasoning. &bull; Apply reinforcement learning (RLHF/RLAIF) methodologies to fine-tune AI agents for improved decision-making. 3. Driving AI Innovation &amp; Research &bull; Lead cutting-edge AI research in Agentic AI, LangGraph, LLM Orchestration, and Self-improving AI Agents. &bull; Stay ahead of advancements in multi-agent systems, AI planning, and goal-directed behavior, applying best practices to enterprise AI solutions. &bull; Prototype and experiment with self-learning AI agents, enabling autonomous adaptation based on real-time feedback loops. 4. AI Strategy &amp; Business Impact &bull; Translate Agentic AI capabilities into enterprise solutions, driving automation, operational efficiency, and cost savings. &bull; Lead Agentic AI proof-of-concept (PoC) projects that demonstrate tangible business impact and scale successful prototypes into production. 5. Mentorship &amp; Capability Building &bull; Lead and mentor a team of AI Engineers and Data Scientists, fostering deep technical expertise in LangGraph and multi-agent architectures. &bull; Establish best practices for model evaluation, responsible AI, and real-world deployment of autonomous AI agents.</li></ul></div>

Senior AI/ML Engineer – R01560293

People First HR Consultancy

Senior Design Engineer- prefers ARCHITECT OR CIVIL ENGINEER (Head of Design Department) &ndash; experience in modular prefabricated industries with at least 10 years of experience in design control, management and coordination within the engineering team and the client.Job Type: Full-time

Senior Design Engineer

Anotech

<div>
 
 
 Senior Project Engineer
 
 
 
 📍 Sohar, Oman
 
 
 
 
 
 🔎 Role Overview
 
 
 Reporting directly to the Package Manager, the Senior Project Engineer will be responsible for overseeing and coordinating all assigned area activities across the full EPCIC lifecycle &mdash; from procurement through construction, installation, pre-commissioning, and commissioning.
 
 
 The role requires strong leadership, proactive schedule management, and close coordination with contractors, engineering disciplines, QA/QC, procurement, and commissioning teams to ensure safe and timely project execution.
 
 
 
 
 
 🛠 Key Responsibilities
 
 <ul>
 <li>
 Demonstrate exemplary HSE leadership and ensure compliance with project safety standards.
 </li>
 <li>
 Manage assigned scope in line with project specifications, contractual requirements, and statement of requirements.
 </li>
 <li>
 Coordinate EPCIC contractor activities to ensure smooth execution of subcontracts and equipment packages.
 </li>
 <li>
 Monitor project schedule and key milestones; anticipate risks and propose mitigation plans.
 </li>
 <li>
 Oversee fabrication, integration, installation, pre-commissioning, and commissioning activities.
 </li>
 <li>
 Ensure proper technical coordination within the PMT and with discipline leads.
 </li>
 <li>
 Follow up procurement activities to meet Required-On-Site (ROS) dates.
 </li>
 <li>
 Interface with QA/QC and commissioning teams for seamless phase transitions.
 </li>
 <li>
 Support brownfield integration and SIMOPS coordination with other stakeholders.
 </li>
 <li>
 Promote a &ldquo;no change&rdquo; mindset to protect budget, schedule, safety, and quality.
 </li>
 <li>
 Lead and participate in weekly/monthly contractor meetings.
 </li>
 </ul>
 
 
 
 
 🎓 Qualifications &amp; Experience
 
 <ul>
 <li>
 Bachelor&rsquo;s degree in Engineering (or equivalent)
 </li>
 <li>
 Minimum 15 years&rsquo; experience in offshore oil &amp; gas projects
 </li>
 <li>
 At least 5 years&rsquo; experience as Project/Area Engineer within an Operator&rsquo;s team
 </li>
 <li>
 10+ years&rsquo; experience in large multinational project taskforce environments
 </li>
 <li>
 Strong background in:
 </li>
 <li>
 Offshore facilities
 </li>
 <li>
 Structural &amp; piping construction
 </li>
 <li>
 Pre-commissioning and commissioning coordination
 </li>
 <li>
 Familiarity with international oil &amp; gas specifications and standards
 </li>
 <li>
 Fluent English
 </li>
 <li>
 Strong leadership and communication skills
 </li>
 <li>
 Computer literate
 </li>
 <li>
 Valid H2S OPITO and T-BOSIET certifications
 </li>
 </ul>
 
 
</div>

Senior Project Engineer

<div><h1 class="jobSectionHeader">Senior AI/ML Engineer - R01558791</h1>
<div>Senior Data Engineer</div><h6 class="jobSectionHeader">
Primary Skills</h6><ul><li>
Hypothesis Testing, T-Test, Z-Test, Regression (Linear, Logistic), Python/PySpark, SAS/SPSS, Statistical analysis and computing, Probabilistic Graph Models, Great Expectation, Evidently AI, Forecasting (Exponential Smoothing, ARIMA, ARIMAX), Tools(KubeFlow, BentoML), Classification (Decision Trees, SVM), ML Frameworks (TensorFlow, PyTorch, Sci-Kit Learn, CNTK, Keras, MXNet), Distance (Hamming Distance, Euclidean Distance, Manhattan Distance), R/ R Studio
</li></ul><h6 class="jobSectionHeader">Specialization</h6><ul><li>
Data Science Advanced: Data Specialist
</li></ul><h6 class="jobSectionHeader">Job requirements</h6><ul><li>
Agentic AI</li></ul></div>

Senior AI/ML Engineer – R01558791

<div><h1 class="jobSectionHeader">Senior Data Scientist - R01560980</h1>
<div>Senior Data Scientist</div><h6 class="jobSectionHeader">
Primary Skills</h6><ul><li>
Hypothesis Testing, T-Test, Z-Test, Regression (Linear, Logistic), Python/PySpark, SAS/SPSS, Statistical analysis and computing, Probabilistic Graph Models, Great Expectation, Evidently AI, Forecasting (Exponential Smoothing, ARIMA, ARIMAX), Tools(KubeFlow, BentoML), Classification (Decision Trees, SVM), ML Frameworks (TensorFlow, PyTorch, Sci-Kit Learn, CNTK, Keras, MXNet), Distance (Hamming Distance, Euclidean Distance, Manhattan Distance), R/ R Studio
</li></ul><h6 class="jobSectionHeader">Specialization</h6><ul><li>
Data Science Advanced: AI/ML Engineer
</li></ul><h6 class="jobSectionHeader">Job requirements</h6><ul><li>
Senior Data Scientist-Classic ML</li></ul></div>

Senior Data Scientist – R01560980

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