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

ExxonMobil

<div>About us 
At ExxonMobil, our vision is to lead in energy innovations that advance modern living and a net-zero future. As one of the world&rsquo;s largest publicly traded energy and chemical companies, we are powered by a unique and diverse workforce fueled by the pride in what we do and what we stand for. 
The success of our Upstream, Product Solutions and Low Carbon Solutions businesses is the result of the talent, curiosity and drive of our people. They bring solutions every day to optimize our strategy in energy, chemicals, lubricants and lower-emissions technologies. 
We invite you to bring your ideas to ExxonMobil to help create sustainable solutions that improve quality of life and meet society&rsquo;s evolving needs. Learn more about our What and our Why and how we can work together.<div><div><div><h2 class="jobSectionHeader">
What role you will play in our team</h2></div><div> 
Operations Research Engineers function as internal consultants to support improved decision-making by developing models using operations research methods, such as mathematical optimization and stochastic simulation. They will work in a team environment to apply their technical skills, project management, and business domain knowledge to develop technological and analytics solutions in partnership with business units to influence and improve key business decisions.
The Operations Research Engineer is expected to work with global, cross-functional stakeholders to understand business problems (both commercially and technically), translate them into mathematical problems, and apply operations research methods to create solutions for decision support. This role will primarily work on business problems related to supply chain and commercial functions, especially for Fuels, Lubes, and Chemical products.</div></div><div><div><h2 class="jobSectionHeader">
What you will do</h2></div><div> 
<ul><li>
Participate in the scoping of business problems to apply operations research methods</li><li>
Work with key contacts to identify and translate data requirements which represent the system and constraints to be modeled</li><li>
Develop custom mathematical optimization and/or stochastic simulation models by applying the following: linear programming, mixed-integer programming, system dynamics simulation, and/or discrete event simulation</li><li>
Proactively drive cross-functional alignment across organizations (as needed) to validate model solution quality</li><li>
Create documentation to explain how the operations research methods are applied, and conduct reviews with senior technical professionals to ensure technical quality</li><li>
Ensure effective and efficient pace of work by managing project timelines, coordinating project team members, and providing timely updates to stakeholders</li><li>
Work effectively within cross-functional, global teams (including business domain experts, product owners, decision science engineers, and IT) to design, develop, deploy, and sustain operations research-based solutions that are scalable and with commercial-grade quality</li></ul></div></div><div><div><h2 class="jobSectionHeader">
Skills and Qualifications</h2></div><div> 
<ul><li>
Master&rsquo;s degree or Ph. D. preferred in Industrial Engineering or Operations Research or Applied Mathematics or relevant Engineering field with specialization in Operations Research methods (mathematical optimization and/or stochastic simulation)</li><li>
Strong analytical skills with 3+ years&rsquo; experience in applying operations research modeling methods for large-scale problems to obtain commercial-grade quality solutions</li><li>
Ability to translate general requirements and long-term deadlines into shorter-term work tasks, self-direct work, and monitor progress</li><li>
Ability to solicit inputs from subject matter experts (SMEs) across technical and business domains and engage with cross-functional teams to drive results</li><li>
Ability to view complex subjects from both high-level and detailed perspectives, and communicate appropriately with different audiences</li><li>
Competent in developing mathematical optimization models using mathematical modeling languages such as AIMMS, GAMS, AMPL, Pyomo, Gurobipy, etc.</li><li>
Competent in developing system dynamics simulation models using software such as Stella Architect, VenSim, AnyLogic, etc.</li><li>
Familiarity with Python, especially for developing and deploying operations research solutions</li><li>
Experience with source code version control (Git)</li><li>
Strong written and verbal communication skills</li></ul></div></div><div><div><h2 class="jobSectionHeader">
Your benefits</h2></div><div> 
An ExxonMobil career is one designed to last. Our commitment to you runs deep our employees grow personally and professionally, with benefits built on our core categories of health, security, finance and life. We offer you: 
<ul><li>
Competitive compensation</li><li>
Medical plans, maternity leave and benefits, life, accidental death and dismemberment benefits</li><li>
Retirement benefits</li><li>
Global networking &amp; cross-functional opportunities</li><li>
Annual vacations &amp; holidays</li><li>
Day care assistance program</li><li>
Training and development program</li><li>
Tuition assistance program</li><li>
Workplace flexibility policy</li><li>
Relocation program</li><li>
Transportation facility</li></ul> 
Please note benefits may change from time to time without notice, subject to applicable laws. The benefits programs are based on the Company&rsquo;s eligibility guidelines.</div></div><div><div><h2 class="jobSectionHeader">
Stay connected with us</h2></div><div> 
<ul><li>
Learn more about ExxonMobil in India, visit ExxonMobil India and Energy Factor India.</li><li>
Follow us on LinkedIn and Instagram
</li><li>Like us on Facebook</li><li>
Subscribe our channel at YouTube</li></ul></div></div><div><div><h2 class="jobSectionHeader">
EEO Statement</h2></div><div> 
ExxonMobil is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, age, national origin or disability status.</div></div><div><div><h2 class="jobSectionHeader"> 
Business solicitation and recruiting scams</h2></div><div> 
ExxonMobil does not use recruiting or placement agencies that charge candidates an advance fee of any kind (e.g., placement fees, immigration processing fees, etc.). Follow the LINK to understand more about recruitment scams in the name of ExxonMobil.</div></div></div> 
Nothing herein is intended to override the corporate separateness of local entities. Working relationships discussed herein do not necessarily represent a reporting connection, but may reflect a functional guidance, stewardship, or service relationship. 
Exxon Mobil Corporation has numerous affiliates, many with names that include ExxonMobil, Exxon, Esso and Mobil. For convenience and simplicity, those terms and terms like corporation, company, our, we and its are sometimes used as abbreviated references to specific affiliates or affiliate groups. Abbreviated references describing global or regional operational organizations and global or regional business lines are also sometimes used for convenience and simplicity. Similarly, ExxonMobil has business relationships with thousands of customers, suppliers, governments, and others. For convenience and simplicity, words like venture, joint venture, partnership, co-venturer, and partner are used to indicate business relationships involving common activities and interests, and those words may not indicate precise legal relationships.</div>

Operations Research Engineer 1

Cartesia

<div><h1 class="jobSectionHeader">About Cartesia</h1>
Our mission is to build the next generation of AI: ubiquitous, interactive intelligence that runs wherever you are. Today, not even the best models can continuously process and reason over a year-long stream of audio, video and text&mdash;1B text tokens, 10B audio tokens and 1T video tokens&mdash;let alone do this on-device.
We're pioneering the model architectures that will make this possible. Our founding team met as PhDs at the Stanford AI Lab, where we invented State Space Models or SSMs, a new primitive for training efficient, large-scale foundation models. Our team combines deep expertise in model innovation and systems engineering paired with a design-minded product engineering team to build and ship cutting edge models and experiences.
We're funded by leading investors at Index Ventures and Lightspeed Venture Partners, along with Factory, Conviction, A Star, General Catalyst, SV Angel, Databricks and others. We're fortunate to have the support of many amazing advisors, and 90+ angels across many industries, including the world's foremost experts in AI.<h1 class="jobSectionHeader">
About The Role</h1>
To build truly global AI, our models must be trained on data that reflects the world's diversity of languages and cultures. We are searching for a Research Engineer to own the quality and coverage of the data behind our models. You will be our in-house expert on global data, ensuring our models perform exceptionally well across dozens of languages. You have a keen eye for linguistic nuance, and a passion for building inclusive and representative datasets at scale.<h2 class="jobSectionHeader">
Your Impact</h2><ul><li>
Design and build large-scale datasets for model training, and run controlled modeling experiments to measure their impact on model performance and behavior.</li><li>
Build evaluations of speech models, both via manual annotation and at scale with automated metrics.</li><li>
Implement techniques for steering data generation to improve model intelligence through data and mitigate bias.</li><li>
Build automated quality control systems to validate and filter generated data</li><li>
Partner with product teams to ensure support for key languages and markets.</li></ul><h2 class="jobSectionHeader">
What You Bring</h2><ul><li>
Experience building or working with large multilingual datasets</li><li>
Experience with generative models (speech, text, or multimodal).</li><li>
Ability to help guide human annotation and evaluation across multiple languages.</li><li>
Strong applied ML background with a focus on data-centric approaches.</li><li>
Excitement for building scalable systems that bridge research and production.</li></ul><h2 class="jobSectionHeader">
What We Offer</h2>
Lunch, dinner and snacks at the office.
Stipend to cover medical insurance
Your own personal Yoshi.<h2 class="jobSectionHeader">
Our Culture</h2>
We&rsquo;re an in-person team based out of San Francisco. We love being in the office, hanging out together, and learning from each other every day.
We ship fast. All of our work is novel and cutting edge, and execution speed is paramount. We have a high bar, and we don&rsquo;t sacrifice quality or design along the way.
We support each other. We have an open &amp; inclusive culture that&rsquo;s focused on giving everyone the resources they need to succeed.
Compensation Range: ₹70L - ₹90L</div>

Research Engineer, Data (India)

ALPHATEC SPINE, INC.

<div>Contribute to the advancement of ATEC&rsquo;s technologies by supporting functional biomechanics research, clinical validation and scientific evidence generation efforts. Working cross-functionally, this role will support study design and protocol execution, management and analysis of complex multi-modal datasets, and translation of findings into clear technical documentation and scientific communications that demonstrate product value and clinical impact.
Essential Duties and Responsibilities
<ul><li>Contribute to the development and validation of workflows for emerging objective functional measurement modalities, e.g., wearable sensors, electromyography, computer vision, force plates.</li></ul><ul>
<li>Partner cross-functionally to identify evidence gaps and align research studies with product and business objectives.</li>
<li>Assist with defining and executing performance testing strategy for clinical validation of ATEC products, including imaging, navigation and robotics technologies.</li>
<li>Support the design and execution of clinical research protocols evaluating functional biomechanical assessment methodologies and enabling technology performance.</li>
<li>Organize and manage large datasets data from research studies, ensuring accuracy, traceability, and proper documentation.</li>
<li>Compile, critically analyze, and interpret research data across multiple data modalities.</li>
<li>Prepare clear, complete and accurate technical engineering documentation.</li>
<li>Contribute to the dissemination of scientific findings through presentation and writing for both internal and external audiences, including interim reports, conference abstracts, presentations, manuscripts, product/procedural training and collateral; assist investigators or customers in the preparation and delivery of research results.</li>
<li>Collaborate closely with other members of the Scientific Affairs team, through meetings and interactions to gather technical and clinical support as well as to ensure alignment of the research activities within the team.</li>
<li>Work on problems of moderate scope where analysis of situations or data requires a review of a variety of factors.</li>
<li>Exercise judgment within defined procedures and practices to determine appropriate action.</li>
<li>Normally receives general instructions on routine work, detailed instructions on new projects or assignments.</li>
</ul>
Requirements
The requirements listed below are representative of the knowledge, skill, and/or ability required. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.<ul>
<li>
Experience with data‑processing tools and analytical workflows, including scripting and automation (MATLAB, Python, Excel).</li>
<li>Superior problem-solving skills with a solid understanding of scientific research and analytical methods.</li>
<li>Knowledge of standards and regulatory requirements at intersection of clinical data and medical imaging; FDA CDRH guidance documents,</li>
<li>Knowledge, understanding, and application of the conduct of clinical investigations involving humans in accordance ICH/GCP, US Code of Federal Regulations (CFR), and the ethical principles that guide clinical research consistent with the principles of the Nuremberg Code, the Belmont Report and the Declaration of Helsinki;</li>
<li>Strong technical writing skills with ability to communicate results to internal and external customers.</li>
<li>Knowledge of orthopedics research, particularly spine, is a plus;
</li><li>Familiarity with electronic data capture systems (EDC, EMR), data analysis, and data visualization.</li>
<li>Experience in technical writing, peer-reviewed literature retrieval and publishing.</li>
<li>Detail-oriented, resourceful problem solver, with effective organizational skills.</li>
<li>Ability to work in a fast-paced environment.</li>
</ul>
Education and Experience<ul><li>
Master of Science degree in biomedical engineering with at least 2 years of experience</li></ul> 
For roles based in the United States that require access to hospital facilities, must be eligible for and maintain credentials at all required hospitals, including meeting any applicable physical requirements or vaccination requirements (including the COVID-19 vaccine, as applicable).
ATEC is committed to providing equal employment opportunities to its employees and applicants without regard to race, color, religion, national origin, age, sex, sexual orientation, gender identity, gender expression, or any other protected status in accordance with all applicable federal, state or local laws. Further, ATEC will make reasonable accommodations that are necessary to comply with disability discrimination laws.
Salary Range
Alphatec Spine, Inc. complies with state and federal wage and hour laws and compensation depends upon candidate&rsquo;s qualifications, education, skill set, years of experience, and internal equity. $75,000 to $90,000 Full-Time Annual Salary</div>

Clinical Research Engineer II

Northrop Grumman

<div>
 RELOCATION ASSISTANCE: Relocation assistance may be available
 
 
 CLEARANCE REQUIRED FOR START: Yes
 
 
 CLEARANCE TYPE: Secret
 
 
 TRAVEL: Yes, 10% of the Time
 
 
 
 Description
 
 
 
 At Northrop Grumman, our employees have incredible opportunities to work on revolutionary systems that impact people's lives around the world today, and for generations to come. Our pioneering and inventive spirit has enabled us to be at the forefront of many technological advancements in our nation's history - from the first flight across the Atlantic Ocean, to stealth bombers, to landing on the moon. We look for people who have bold new ideas, courage and a pioneering spirit to join forces to invent the future, and have fun along the way. Our culture thrives on intellectual curiosity, cognitive diversity and bringing your whole self to work &mdash; and we have an insatiable drive to do what others think is impossible. Our employees are not only part of history, they're making history.
 
 
 Northrop Grumman Advanced Weapons Business Unit is currently seeking a
 
 Missile Operations Research Engineer 2
 
 for our Advanced Programs Software and Operational Analysis team in Northridge, CA. The Missile Operations Research Engineer will be responsible for developing models, performing data analysis, communicating findings to internal and external stakeholders, and training and mentoring junior analysts in the same skill sets.
 
 
 
 This position is required to be full-time, on-site in the
 
 
 Northridge, CA location.
 
 
 No remote/telework arrangement is available for this position. Applicants must have an active US Government DoD Secret security clearance with ability to get cleared for SAP
 
 .
 
 
 Our work influences the investment decisions of our senior leadership and customer program offices. The Operations Research discipline is a blend of many other fields (e.g. Systems Engineering, Aerospace Engineering, Software Engineering, etc.) and frequently collaborates across functional disciplines. The Operations Research team is responsible for evaluating the merit of new technologies and military operations in future conflicts. In this role, you will be a member of a team that performs the following tasks: simulation tool and infrastructure development, model development, scenario creation, designing focused vignette threads, and performing analysis. The analysis includes a wide variety of system types and spans multiple domains (friendly and adversary; ground, maritime, air and space). You will work with technical teams and leadership to develop standalone and/or distributed simulation tools/infrastructure and to perform analysis at the engagement, mission, and/or campaign levels.
 
 
 
 Duties And Responsibilities May Include
 
 
 
 <ul>
 <li>
 Develop and apply advanced software and systems methods that support the investigation, analysis and solution of complex and advanced technical problems.
 </li>
 <li>
 Develop and use tools that support the visualization of the battlespace at a mission or engagement level
 </li>
 <li>
 Use, develop, and modify tools to support data analysis of simulation or flight test results
 </li>
 <li>
 Model operational environments, performs trade studies via computer simulation, and recommends alternative architectures
 </li>
 <li>
 Develop software to model behavior of systems in real-time and constructive simulations
 </li>
 <li>
 Develop, integrate, and use advanced graphical user interfaces and visualization tools
 </li>
 <li>
 Perform mission simulation using government or industry standard tools to determine mission effectiveness
 </li>
 <li>
 Perform engagement simulation using standard tools, MATLAB, Simulink and others.
 </li>
 <li>
 Perform modeling and simulation studies that optimize mission performance for weapon systems across the air, land, sea, space and cyber domains
 </li>
 <li>
 Perform modeling, simulation, and analysis tasks assessing system survivability, system effectiveness, and overall system performance in engagement / mission-level scenarios.
 </li>
 <li>
 Develop performance models and algorithms, threat models and command and control models for new and/or existing system simulation frameworks.
 </li>
 <li>
 Solicit input from various stakeholders, synthesize data, and communicate results to various levels of leadership within the organization
 </li>
 <li>
 Participate in Agile engineering project frameworks
 
 
 </li>
 </ul>
 
 Basic Qualifications For An Operations Research Engineer 2
 
 
 
 <ul>
 <li>
 BS in Computer Science, Engineering, Math, or Physics and 2+ years modeling, simulation, and analysis of Aerospace Systems, Systems Analysis, and Design
 </li>
 <li>
 Familiar with AFSIM development / analyzing including making and validating movers, creating observers and post-processors, and performing large Design of Experiments (DoE) studies
 </li>
 <li>
 Familiar with creating compelling OV-1s, data visualization, and other briefing materials to explain simulation techniques, assumptions, and insights to a variety of audiences
 </li>
 <li>
 Familiar executing technology development and EMD contracts with DoD customers
 </li>
 <li>
 Familiar with identifying Technical Performance Metrics (TPMs) and developing study plans from Statements of Work (SOWs) and Statements of Objectives (SOOs)
 </li>
 <li>
 Active US Government DoD Secret security clearance with ability to get cleared for SAP
 
 
 </li>
 </ul>
 
 Preferred Qualifications
 
 
 
 <ul>
 <li>
 Comfortable with programming languages such as C/C++.
 </li>
 <li>
 Software development skills with the ability to manage computer resources for data throughput and distributed systems.
 </li>
 <li>
 Experience developing and using analysis tools such as MATLAB, Python, VBA, or similar tools
 </li>
 <li>
 Experience developing in multiple operating systems as well as integrating systems across OS boundaries. (Windows/Linux)
 </li>
 <li>
 Debugging skills, ability to use common debuggers, compilers and command line execution.
 </li>
 <li>
 Ability to develop shell scripts for program execution
 </li>
 <li>
 Knowledge of and experience with software engineering development lifecycle processes
 </li>
 <li>
 Experience with version control tools (Bitbucket, GitHub, or Gitlab, etc.)
 </li>
 <li>
 Experience integrating custom plug-ins to AFSIM
 </li>
 <li>
 Experience with working with and programming distributed simulation protocols such as DIS or HLA
 </li>
 <li>
 Experience performing modeling, simulation, and analysis tasks assessing system survivability, system effectiveness, and overall system performance in engagement / mission-level scenarios.
 </li>
 <li>
 Knowledge of RF Threat Modeling, of Electronic Attack techniques and modeling, guided missile design
 </li>
 <li>
 Knowledge of any of the following simulation frameworks: AFSIM, Suppressor, Brawler, JWS, AJEM, ESAMS, NGTS, EADSIM, TACZAAM, ITASE
 </li>
 <li>
 Familiarity with Agile, MBSE, and / or system architecture frameworks
 </li>
 <li>
 Effective project management skills and good communication techniques
 
 
 </li>
 </ul>
 Primary Level Salary Range: $91,800.00 - $137,600.00
 
 
 The above salary range represents a general guideline; however, Northrop Grumman considers a number of factors when determining base salary offers such as the scope and responsibilities of the position and the candidate's experience, education, skills and current market conditions.
 
 
 Depending on the position, employees may be eligible for overtime, shift differential, and a discretionary bonus in addition to base pay. Annual bonuses are designed to reward individual contributions as well as allow employees to share in company results. Employees in Vice President or Director positions may be eligible for Long Term Incentives. In addition, Northrop Grumman provides a variety of benefits including health insurance coverage, life and disability insurance, savings plan, Company paid holidays and paid time off (PTO) for vacation and/or personal business.
 
 
 The application period for the job is estimated to be 20 days from the job posting date. However, this timeline may be shortened or extended depending on business needs and the availability of qualified candidates.
 
 
 Northrop Grumman is an Equal Opportunity Employer, making decisions without regard to race, color, religion, creed, sex, sexual orientation, gender identity, marital status, national origin, age, veteran status, disability, or any other protected class. For our complete EEO and pay transparency statement, please visit http://www.northropgrumman.com/EEO. U.S. Citizenship is required for all positions with a government clearance and certain other restricted positions.
 
 
</div>

Missile Operations Research Engineer Level 2 (AHT) - R10222789

Innodata

<div>Who we are:
Innodata (NASDAQ: INOD) is a leading data engineering company. With more than 2,000 customers and operations in 13 cities around the world, we are the AI technology solutions provider-of-choice to 4 out of 5 of the world&rsquo;s biggest technology companies, as well as leading companies across financial services, insurance, technology, law, and medicine.
By combining advanced machine learning and artificial intelligence (ML/AI) technologies, a global workforce of subject matter experts, and a high-security infrastructure, we&rsquo;re helping usher in the promise of clean and optimized digital data to all industries. Innodata offers a powerful combination of both digital data solutions and easy-to-use, high-quality platforms.
Our global workforce includes over 3,000 employees in the United States, Canada, United Kingdom, the Philippines, India, Sri Lanka, Israel and Germany. We&rsquo;re poised for a period of explosive growth over the next few years.
Position Summary:
Innodata is expanding its team of technical experts in LLM training, post-training, and evaluation systems. As an AI/ML Research Engineer, LLM Training &amp; Evaluation, you will build and optimize the technical foundations that power model improvement for foundation model builders and leading labs.
This role is ideal for someone who has hands-on experience fine-tuning and evaluating large language models (and ideally multimodal models), and who can bridge research and engineering in real-world customer environments. You will work closely with Language Data Scientists, Applied Research Scientists, data engineers, and client technical stakeholders to design and implement robust training/evaluation pipelines using both human-in-the-loop and AI-augmented methods.
The ideal candidate brings a strong computer science / machine learning engineering background, experience with modern LLM post-training workflows, and the ability to engage credibly with technical counterparts at leading AI organizations.
Who We&rsquo;re Looking For:
You have at least 2-3 years of relevant experience in machine learning engineering, applied ML systems, or research engineering, with substantial hands-on work in LLMs and multimodal foundation models. You have built, adapted, or optimized model training and evaluation pipelines, and you understand the practical realities of experimentation at scale: reproducibility, debugging, metrics quality, and iteration speed.
You are comfortable operating in ambiguous, high-complexity environments and can move from problem framing to implementation. You can collaborate effectively with both researchers and engineers, and you are credible in technical conversations with sophisticated customer stakeholders (e.g., AI researchers, ML engineers, technical product leads).
You bring a builder mindset and strong engineering judgment, while also understanding that evaluation quality and data quality are central to model improvement. You are excited to partner with human evaluation experts and language data scientists to create integrated post-training and evaluation systems.
Tell Me More:
As an AI/ML Research Engineer, LLM Training &amp; Evaluation, you will design and implement the pipelines and tooling that connect data, evaluation, and post-training. You will help customers and internal teams move from evaluation findings to measurable model improvements.
Your work may include building fine-tuning workflows (e.g., supervised fine-tuning and preference-based optimization), integrating evaluation harnesses into model development loops, improving experiment reliability and throughput, and supporting advanced evaluation scenarios such as long-context, cross-modal, and dynamic multi-turn interactions.
You will also contribute to Innodata&rsquo;s internal R&amp;D efforts, including benchmark datasets, evaluation frameworks, and reusable infrastructure for model assessment and post-training experimentation.
Responsibilities:<ul><li>
Lead or co-lead technically complex ML engineering projects from initial customer discussions through implementation and delivery</li><li>
Design, build, and improve LLM training and post-training pipelines, including data ingestion, preprocessing, fine-tuning, evaluation, and experiment tracking</li><li>
Implement and optimize evaluation systems for LLMs and multimodal models, including offline benchmarks and task-specific test harnesses</li><li>
Integrate human-in-the-loop and AI-augmented evaluation signals into model development workflows</li><li>
Build robust infrastructure and tooling for reproducible experimentation, metrics logging, and regression monitoring</li><li>
Diagnose model behavior and pipeline failures, including data issues, training instability, metric inconsistencies, and evaluation drift</li><li>
Collaborate with Language Data Scientists and Applied Research Scientists to translate evaluation frameworks into executable systems</li><li>
Work closely with customer technical stakeholders to understand goals, constraints, and success criteria; propose and implement technically sound solutions</li><li>
Contribute to internal research and platform development, including benchmark frameworks, evaluation tooling, and post-training workflow improvements</li><li>
Contribute to best practices and standards for LLM training, evaluation, and quality assurance across projects</li><li>
Mentor junior engineers and contribute to technical design reviews, documentation, and engineering rigor across the team</li></ul><ul><li>
BS/MS/PhD in Computer Science, Machine Learning, AI, Applied Mathematics, or a related quantitative technical field (MS/PhD preferred)</li><li>
2-3 years of relevant industry or research engineering experience in ML/AI systems</li><li>
Hands-on experience with LLM training / fine-tuning / post-training, including at least one of:<ul><li>
supervised fine-tuning (SFT)</li><li>
preference optimization (e.g., DPO or related methods)</li><li>
RLHF / RLAIF-style workflows</li><li>
task- or domain-adaptation of foundation models</li></ul></li><li>
Strong programming skills in Python and experience building production-quality ML code</li><li>
Experience with modern ML frameworks (e.g., PyTorch, JAX, TensorFlow) and model libraries/tooling (e.g., Hugging Face ecosystem, vLLM, distributed training stacks)</li><li>
Experience designing and implementing evaluation pipelines for LLM/ML systems, including metrics computation, dataset handling, and experiment comparisons</li><li>
Strong understanding of data pipelines and ML systems engineering, including reproducibility, observability, and debugging</li><li>
Experience with large-scale distributed ML systems and performance optimization for training/evaluation workloads (GPU/accelerator environments preferred)</li><li>
Experience with large-scale data processing and workflow orchestration in support of model training/evaluation</li><li>
Ability to collaborate directly with technical stakeholders including research scientists, ML engineers, data engineers, and customer technical leads</li><li>
Strong written and verbal communication skills, including the ability to explain complex technical tradeoffs to both technical and non-technical audiences</li></ul>
Technical Skills
ML / LLM Engineering<ul><li>
Experience training, fine-tuning, and evaluating transformer-based models</li><li>
Understanding of post-training workflows and model iteration loops</li><li>
Familiarity with inference-time considerations (latency, throughput, memory/performance tradeoffs) where relevant to evaluation or deployment</li></ul>
Evaluation &amp; Experimentation<ul><li>
Experience implementing automated evaluation pipelines and test harnesses</li><li>
Experience with experiment tracking, versioning, and reproducibility practices</li><li>
Ability to assess metric quality and ensure consistency across model comparisons</li></ul>
Software / Data Engineering<ul><li>
Proficiency in Python and strong software engineering fundamentals</li><li>
Experience with data processing pipelines, storage formats, and scalable dataset workflows</li><li>
Familiarity with CI/CD, testing, and engineering quality practices for ML systems</li></ul>
Preferred Skills<ul><li>
Experience with multimodal model training/evaluation (text + image/audio/video)</li><li>
Experience with long-context evaluation and/or model adaptation for long-context tasks</li><li>
Experience with agentic or multi-turn evaluation harnesses, tool-use simulation, or interactive environment testing</li><li>
Experience working in customer-facing technical consulting, solutions engineering, or applied research delivery</li><li>
Familiarity with LLM safety, alignment, robustness, or red-teaming evaluation approaches</li><li>
Contributions to open-source ML/LLM tooling or published technical work in relevant areas</li></ul>
How this role partners with the team
This role works closely with:<ul><li>
Language Data Scientists, who lead human evaluation design, language/data process excellence, and annotation/synthetic workflows</li><li>
Applied Research Scientists, who lead evaluation methodology, benchmarking research, and experimental design</li><li>
Data Engineers / Platform Teams, who support scalable data and infrastructure foundations</li><li>
Customer Technical Stakeholders, who rely on Innodata for expert guidance and implementation support in advanced GenAI development</li></ul>
Please be aware of recruitment scams involving individuals or organizations falsely claiming to represent employers. Innodata will never ask for payment, banking details, or sensitive personal information during the application process. To learn more on how to recognize job scams, please visit the Federal Trade Commission&rsquo;s guide at https://consumer.ftc.gov/articles/job-scams.
If you believe you&rsquo;ve been targeted by a recruitment scam, please report it to Innodata at verifyjoboffer@innodata.com and consider reporting it to the FTC at ReportFraud.ftc.gov.
#LI-NS1</div>

AI/ML Research Engineer, LLM Post-Training & Evaluation

SimpliSafe

<div>About SimpliSafe

SimpliSafe is a leading innovator in the home security industry, dedicated to making every home a safe home. With a mission to provide accessible and comprehensive security solutions, we design and build user-centric products that empower individuals and families to protect what matters most.

We believe in a collaborative and agile environment where learning and growth are continuous. Our teams are composed of talented individuals who are passionate about technology, security, and delivering exceptional customer experiences.

We're embracing a hybrid work model that enables our teams to split their time between office and home. Hybrid for us means we expect our teams to come together in our state-of-the-art office on two core days, typically Tuesday, Wednesday, or Thursday &ndash; working together in person and choosing where they work for the remainder of the week. We all benefit from flexibility and get to use the best of both worlds to get our work done.

Why are we hiring?

Well, we're growing and thriving. So, we need smart, talented, and humble people who share our values to join us as we disrupt the home security space and relentlessly pursue our mission of keeping Every Home Secure.

About the Role

We are seeking a highly motivated and experienced Computer Vision Applied Research Engineer to join our growing Edge AI team. As a key contributor, you will lead development of on-device machine learning for outdoor monitoring in the home security space. You will build and optimize computer vision models that run in real time on resource-constrained embedded devices like outdoor cameras and doorbell cameras, balancing accuracy with latency, memory, power, and reliability in challenging conditions (night, weather, motion blur, occlusions).

Responsibilities:
<ul>
<li>Lead end-to-end development of edge ML models for outdoor monitoring (e.g., person/vehicle/package detection, classification, tracking, segmentation, event understanding).</li>
<li>Architect, train, and deploy transformer-based vision models (e.g., compact ViTs, hierarchical transformers, DETR-style detectors) and hybrid CNN-transformer backbones optimized for embedded inference.</li>
<li>Drive model efficiency through resource-aware design and training, including:</li>
<li><ul><li>Architecture: Token/patch reduction, efficient attention variants, early-exit / conditional compute</li>
<li>Training: distillation from large transformer teachers to edge students</li>
<li>Compression: Quantization (PTQ/QAT), pruning, mixed precision, and operator-aware optimization</li>
</ul></li><li>Translate product requirements into model targets (accuracy, FPS, memory footprint, power/thermal) and ensure models meet budgets on doorbell/outdoor camera hardware.</li>
<li>Partner with embedded/firmware and platform teams to integrate models into production pipelines; profile bottlenecks and improve end-to-end runtime performance.</li>
<li>Define evaluation strategies tailored to outdoor edge deployments; perform failure analysis and improve long-tail robustness (nighttime, rain/snow, backlight, fast motion).</li>
<li>Set technical direction and raise engineering standards: best practices for experimentation, reproducibility, model/version management, and deployment readiness; mentor other ML engineers.</li>
</ul>
Qualifications:
<ul>
<li>8+ years in applied ML/ML engineering, including shipping production CV models.</li>
<li>Strong computer vision background with deep learning expertise across detection/classification/segmentation/tracking.</li>
<li>Hands-on experience with vision transformers and/or DETR-style architectures, including practical knowledge of efficiency trade-offs for edge deployment.</li>
<li>Demonstrated success deploying models in resource-constrained, real-time environments (embedded/mobile/IoT/edge).</li>
<li>Deep experience in model optimization: QAT/PTQ, distillation, pruning, compression, mixed precision, and hardware/runtime-aware training.</li>
<li>Proficiency in Python and PyTorch and/or TensorFlow; ability to productionize models and collaborate with systems engineers (C++ experience strongly preferred).</li>
<li>Staff-level leadership: ability to drive ambiguous initiatives, align stakeholders, and mentor engineers.</li>
</ul>
Bonus Points:
<ul>
<li>Expertise in efficient transformer techniques (e.g., attention approximations, windowed/local attention, KV caching where applicable, token merging, sparsity) and their deployment implications.</li>
<li>Experience building model &quot;ladders&quot; across multiple chipsets/device tiers with consistent KPIs and automated regression testing.</li>
<li>Experience with embedded inference tooling and runtimes (e.g., TFLite, ONNX Runtime, TensorRT) and model export/compatibility constraints.</li>
<li>Familiarity with embedded accelerators and profiling (ARM NEON, DSP/NPU toolchains), kernel/operator tuning, and real-time video pipelines.</li>
<li>Experience with long-tail data strategies (active learning, hard-negative mining) and edge reliability/telemetry feedback loops.</li>
</ul>
What Values You'll Share
<ul>
<li>Customer Obsessed - Building deep empathy for our customers, putting them at the core of our work, and developing strong, long-term relationships with them.</li>
<li>Aim High - Always challenging ourselves and others to raise the bar.</li>
<li>No Ego - Maintaining a &quot;no job too small&quot; attitude, and an open, inclusive and humble style.</li>
<li>One Team - Taking a highly collaborative approach to achieving success.</li>
<li>Lift As We Climb - Investing in developing others and helping others around us succeed.</li>
<li>Lean &amp; Nimble - Working with agility and efficiency to experiment in an often ambiguous environment.</li>
</ul>
What We Offer
<ul>
<li>A mission- and values-driven culture and a safe, inclusive environment where you can build, grow and thrive
</li><li>A comprehensive total rewards package that supports your wellness and provides security for SimpliSafers and their families (For more information on our total rewards please click here)</li>
<li>Free SimpliSafe system and professional monitoring for your home.</li>
<li>Employee Resource Groups (ERGs) that bring people together, give opportunities to network, mentor and develop, and advocate for change.</li>
</ul>
The target annual base pay range for this role is $183,300 to $268,800.

This target annual base pay range represents our good-faith estimate of what we expect to pay for this role. We use a market-based compensation approach to set our target annual base pay ranges and make adjustments annually. We carefully tailor individual compensation packages, including base pay, taking into consideration employees' job-related skills, experience, qualifications, work location, and other relevant business factors.

Beyond base pay, we offer a Total Rewards package that may include participation in our annual bonus program, equity, and other forms of compensation, in addition to a full range of medical, retirement, and lifestyle benefits. More details can be found here.

We're committed to fair and equitable pay practices, as well as pay transparency. We regularly review our programs to ensure they remain competitive and aligned with our values.

We wholeheartedly embrace and actively seek applications from all individuals, no matter how they identify. We are committed to cultivating a diverse and inclusive workplace, and we believe our work is enriched when we incorporate a multitude of perspectives, backgrounds, and experiences. We want everyone who works here to thrive and contribute to not only our mission of keeping every home secure, but also to making our workplace safe and supportive for others. If a reasonable accommodation may be needed to fully participate in the job application or interview process, to perform the essential functions of a position, or to receive other benefits and privileges of employment, please contact careers@simplisafe.com.</div>

Staff CV Applied Research Engineer, Edge AI

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