machine engineer

ATG Entertainment

<div></div><div><div><div><ul><li><h4 class="jobSectionHeader">Salary</h4>
<div>$110,000k - $130,000k</div>
</li><li><h4 class="jobSectionHeader">Hours</h4>
<div>Various</div>
</li><li><h4 class="jobSectionHeader">Type</h4>
<div>Full Time</div>
</li><li><h4 class="jobSectionHeader">Location</h4>
<div>1419 Basin Street 
New Orleans 
Louisiana 
70116</div></li><li> 
<ul><li><div>No Files</div></li></ul>
</li></ul></div><div></div><div>POSITION: Machine Learning Engineer
LOCATION: Remote (San Antonio, Detroit, New Orleans)
HOURS: Full-Time, Non-Union. 40hrs/week
COMPENSATION: $110k - $130k (Commensurate with Experience)
ABOUT THE POSITION
As a Machine Learning Engineer, you will build production foundations that allow our machine learning capabilities to run reliably at scale across the US and UK. This role focuses on the engineering required to operationalize machine learning &mdash; including low‑latency serving, API integration, data pipelines for inference, monitoring, and robust deployment practices &mdash; so that model outputs can be activated in customer‑facing channels. 
You will deliver the real‑time serving layer that connects our data platform (e.g., Snowflake) with activation platforms such as Iterable. You will design and maintain the architecture, services, and workflows that move us from offline modelling to always‑on personalization, supporting CRM first and expanding to other channels over time. 
PEOPLE, PLACES, AND THINGS<ul><li>
Contribute to designing, scaling/building, evaluating, integrating, and refining machine learning models.
</li><li>Collaborate with Marketing and Engineering across the US and UK to define how machine learning underpins personalized customer communications.
</li><li>Build reusable science infrastructure, design and build production-grade measurement systems and establish robust evaluation frameworks.</li></ul> 
EXPERIENCE AND SKILLS
Essential<ul><li>
3+ years&rsquo; experience as an ML Engineer delivering production ML systems (models + pipelines) at scale.</li><li>
Degree in a quantitative field in Computer Science, Machine Learning, Operations Research (or equivalent technical background with demonstrated impact).</li><li>
Proficient in Python and modern ML tooling, with solid software engineering practices (testing, packaging, code quality).</li><li>
Hands‑on experience with AWS and cloud-native patterns (e.g., Lambda, S3, IAM/security, event-driven architectures, monitoring).</li><li>
Experience designing inference architectures (batch + low‑latency / near‑real‑time), including storage, caching, queues/streaming, and performance tuning.</li><li>
Familiar with orchestration and data pipelines for features/inference (e.g., Airflow) and modern data stacks (e.g., Snowflake/dbt), including API-based integrations.</li><li>
Familiar with MLOps foundations: model/version management, feature stores, monitoring/drift, and production experimentation (A/B assignment, logging, guardrails).</li><li>
Bonus: experience activating ML outputs in customer engagement platforms (e.g., Iterable) and real-time personalization patterns</li></ul>
BENEFITS<ul><li>
Medical, Dental and Vision Insurance
</li><li>401k Match</li><li>
Paid Vacation &amp; Holidays</li></ul> 
ATG Entertainment: Passion Behind Performance 
ATG Entertainment is a world leader in live entertainment. Our portfolio of venues includes historic theatres, studio theatres, cinemas, conference spaces, and modern live music arenas. ATG Entertainment own, operate or program 64 of the world&rsquo;s most iconic venues across the UK, the US and Germany entertaining over 18 million audience members each year. 
ATG Entertainment: IDEA Mission Statement
At ATG Entertainment, our commitment to inclusion, diversity, equity, and access (IDEA) is reflected in our IDEA mission statement: A stage for everyone. Our stages are a platform for compelling stories &ndash; stories that are for all, by all, and of all. We shine our spotlight on our differences and believe that understanding and celebrating these differences makes us better global citizens. We are committed to strengthening the sense of belonging by ensuring diversity and equity in everything we do. We strive to make our venues beacons of these ideals in our communities. Onstage and off, we hold ourselves accountable for nurturing an inclusive culture, one in which everyone can bring their authentic selves, and everyone feels they belong. At ATG Entertainment, we provide a stage for everyone.</div></div></div>

Machine Learning Engineer

Spotify

<div>
 The Music Promotion team is building products that allow creators to promote their work to reach new audiences and create lasting connections with their fans. We&rsquo;re looking for a Machine Learning Engineer to help us build systems that more accurately understand the performance that promotion can have, giving customers actionable insights for building their promotion strategies, whether it&rsquo;s a DIY artist or an industry-facing partner.
 
 
 As an ML Engineer, you will help execute on strategies for understanding the factors that play a role in the performance of promoted tracks across the globe. You&rsquo;ll build data-driven solutions, as well as effective online and offline strategies to efficiently iterate and evaluate model approaches. You&rsquo;ll have access to a growing list of datasets, features and ML infrastructure to continually experiment and improve the model-based approach.
 
 
 What You'll Do
 
 
 <ul>
 <li>
 Contribute to the design, build, evaluation, shipping, and refinement of systems that improve Spotify&rsquo;s promotional performance with hands-on ML development
 </li>
 <li>
 Collaborate with a multidisciplinary team to optimize machine learning models for production use cases, ensuring they are highly efficient, scalable, and consistently meet well-defined success criteria
 </li>
 <li>
 Influence the technical design, architecture, and infrastructure decisions to support new and diverse machine learning architectures.
 </li>
 <li>
 Work with Data and ML Engineers to support transitioning machine learning models from research and development into production
 </li>
 <li>
 Implement and monitor model success metrics, diagnose issues, and contribute to an on-call schedule to maintain production stability.
 
 
 </li>
 </ul>
 Who You Are
 
 
 <ul>
 <li>
 You have experience implementing ML systems at scale in Java, Scala, Python or similar languages as well as experience with ML frameworks such as TensorFlow, PyTorch, etc.
 </li>
 <li>
 You have an understanding of how to bring machine learning models from research to production and are comfortable working with innovative, cutting-edge architectures.
 </li>
 <li>
 You have a collaborative mindset, enjoy working closely with research scientists, machine learning engineers, and data engineers to innovate and improve models.
 </li>
 <li>
 You have experience in optimizing machine learning models for production use cases
 </li>
 <li>
 You preferably have experience with data pipeline tools like Apache Beam, Scio, and cloud platforms like GCP
 </li>
 <li>
 You have some exposure to causal ML models, including things like counterfactuals.
 </li>
 <li>
 You are familiar with creating model success metric dashboards, diagnosing production issues, and are willing to take part in an on-call schedule to maintain performance.
 
 
 </li>
 </ul>
 Where You'll Be
 
 
 We offer you the flexibility to work where you work best! For this role, you can be within the EST time zone as long as we have a work location.
 
 
 The United States base range for this position is $170,000 - $212,000 plus equity. The benefits available for this position include health insurance, six month paid parental leave, 401(k) retirement plan, monthly meal allowance, 23 paid days off, 13 paid flexible holidays, paid sick leave. These ranges may be modified in the future.
 
 
 Spotify is an equal opportunity employer. You are welcome at Spotify for who you are, no matter where you come from, what you look like, or what&rsquo;s playing in your headphones. Our platform is for everyone, and so is our workplace. The more voices we have represented and amplified in our business, the more we will all thrive, contribute, and be forward-thinking! So bring us your personal experience, your perspectives, and your background. It&rsquo;s in our differences that we will find the power to keep revolutionizing the way the world listens.
 
 
 At Spotify, we are passionate about inclusivity and making sure our entire recruitment process is accessible to everyone. We have ways to request reasonable accommodations during the interview process and help assist in what you need. If you need accommodations at any stage of the application or interview process, please let us know - we&rsquo;re here to support you in any way we can.
 
 
 Spotify transformed music listening forever when we launched in 2008. Our mission is to unlock the potential of human creativity by giving a million creative artists the opportunity to live off their art and billions of fans the chance to enjoy and be passionate about these creators. Everything we do is driven by our love for music and podcasting. Today, we are the world&rsquo;s most popular audio streaming subscription service.
</div>

Richard, Wayne & Roberts

<div>
 
 A leading organization is seeking a
 
 Machine Learning Engineer
 
 to design, build, and productionize advanced analytics solutions across machine learning, deep learning, and generative AI. This role will focus on developing scalable pipelines, optimizing model performance, and deploying solutions in production environments using modern data platforms such as Databricks and PySpark.
 
 
 
 Key Responsibilities:
 
 
 <ul>
 <li>
 Partner with business stakeholders to define and deliver ML/AI solutions
 </li>
 <li>
 Build, scale, and optimize model training and serving pipelines
 </li>
 <li>
 Improve model performance through feature engineering, tuning, and observability
 </li>
 <li>
 Deploy and integrate ML models into production with a focus on reliability and scalability
 </li>
 <li>
 Collaborate with data engineers and data scientists to translate business needs into technical solutions
 </li>
 <li>
 Drive best practices in architecture, code quality, and CI/CD pipelines
 </li>
 </ul>
 
 
 Qualifications:
 
 
 <ul>
 <li>
 7+ years of experience in machine learning engineering, data science, or software engineering
 </li>
 <li>
 Strong programming skills in Python, SQL, or Scala
 </li>
 <li>
 Experience with Databricks, PySpark, and cloud platforms (AWS, Azure, etc.)
 </li>
 <li>
 Hands-on experience with feature engineering, model optimization, and scalable data pipelines
 </li>
 <li>
 Bachelor&rsquo;s degree in a quantitative field (Master&rsquo;s preferred)
 </li>
 </ul>
 
 
 
 
 Location: Arlington, VA - Relocation assistance within the US
 
 
 No sponsorship now or in the future
 
 
 No 3rd party (C2C) - This is a direct hire with benefits opportunity
 
 
 LI- #HP-1
 
</div>

Shields Group Search

<div>
 
 
 Machine Learning Engineer: LLM Interpretability &amp; Systems
 
 
 
 
 Location:
 
 San Francisco, CA - 5 days a week onsite
 
 
 
 
 
 
 About Our Client &amp; The Mission
 
 
 
 We are partnering with a San Francisco-based AI company pushing open-source Large Language Models beyond what most teams think is possible. They do not just prompt models. They open the black box. By leveraging cutting-edge interpretability techniques (XAI), they manipulate model internals to push open-source models to rival and beat Frontier models in both capability and cost-efficiency.
 
 
 
 
 
 
 They are primarily looking for a highly driven Machine Learning Engineer who can take complex, cutting-edge XAI research and translate it into robust, deployable production systems.
 
 
 
 
 
 
 
 The Role
 
 
 
 This is not a prompt engineering or RAG-wrapper position. You will be operating deep within the stack, interacting directly with model weights, activations, and architectures. Your mandate is simple but exceptionally difficult: state exactly how a model can be improved for a specific purpose using interpretability, and then build the system to do it.
 
 
 
 
 
 
 What You Will Do:
 
 
 <ul>
 <li>
 
 Translate Research to Production:
 
 Take and improve the latest papers in mechanistic interpretability, activation engineering, and representation engineering and turn them into scalable production code.
 </li>
 <li>
 
 Model Surgery:
 
 Apply techniques like control vectors, activation patching, and feature extraction to fundamentally alter and enhance model capabilities.
 </li>
 <li>
 
 Maximize Open-Source:
 
 Squeeze every ounce of performance out of open-weight models to make them hyper-efficient and highly competitive against closed-source Frontier models.
 </li>
 <li>
 
 Build the Pipeline:
 
 Design and implement the infrastructure required to deploy these modified, highly-optimized models into production environments.
 </li>
 </ul>
 
 
 
 
 
 Who You Are
 
 
 <ul>
 <li>
 
 Deep Technical Fundamentals:
 
 You possess a rigorous understanding of Transformer architectures, PyTorch internals, and the mathematical foundations of deep learning.
 </li>
 <li>
 
 Beyond the API:
 
 You have hands-on experience training, fine-tuning, and dissecting LLMs. You understand the difference between superficial augmentation and structural model improvement.
 </li>
 <li>
 
 High Agency &amp; Conviction:
 
 You do not wait for tasks to be assigned. You identify bottlenecks, propose deeply technical solutions, and execute them with absolute conviction.
 </li>
 <li>
 
 Mission-Obsessed:
 
 You are fundamentally aligned with the mission to democratize state-of-the-art AI capabilities through open-source optimization.
 </li>
 </ul>
 
 
 
 
 
 The Anti-Persona (Who this role is NOT for)
 
 
 
 If your primary experience involves calling the OpenAI API, chaining together prompts with LangChain, or building standard RAG pipelines without touching the underlying model infrastructure, this role is not a fit. We need someone who can go into the matrix, not just interact with the interface.
 
 
 
 
 
 
 What We Offer
 
 
 <ul>
 <li>
 
 Aggressive Equity &amp; Compensation:
 
 Highly competitive salaries and significant equity stakes to incentivize the hard work required. $180-260K, 0.5-1% equity
 </li>
 <li>
 
 Impact:
 
 Pioneering work at the intersection of XAI and production engineering, directly shaping the capabilities of the core product.
 </li>
 <li>
 
 Autonomy:
 
 A high-trust environment where technical conviction is valued and supported.
 </li>
 </ul>
</div>

Vilya

<div>
 Vilya is a computational biotechnology company creating a novel class of medicines to precisely target disease biology. We believe computational approaches are an integral, if not foundational, component of drug discovery and development. Our platform is built on ground-breaking research in advanced computational approaches and taps into uncharted chemical space to design new molecular structures not found in nature.
 
 
 We are harnessing the power of our platform to go after previously impossible targets in an array of indications. Vilya&rsquo;s ultimate goal is to solve some of the most challenging unmet medical needs that exist today.
 
 
 Our Vision: Harness a revolution in technology and biology to better human health
 
 
 Our Mission: Build an independent, leading biotech company founded on intelligent drug design to cure the incurable
 
 
 We think from atoms to bits &ndash; and everywhere in between; now, we are seeking a highly-motivated, creative, and knowledgeable Machine Learning Engineer to help us build a highly-scalable computational drug discovery platform.
 
 
 Vilya is a team-first organization, and we believe onsite work is essential to how we collaborate, move quickly, and build a strong culture. This is an onsite position in our Seattle office. Join us and help make a difference for patients!
 
 
 RESPONSIBILITIES:
 
 
 <ul>
 <li>
 Develop and maintain the infrastructure to support machine learning workflows for drug discovery at scale.
 </li>
 <li>
 Implement and optimize algorithms for data processing, model training and model deployment.
 </li>
 <li>
 Collaborate with and support the computational scientists at Vilya to help build a world-class computational macrocycle drug discovery pipeline.
 </li>
 <li>
 Participate in the development and enhancement of our software platform to ensure that our codebase is reliable, efficient, secure, and scalable.
 
 
 </li>
 </ul>
 BASIC QUALIFICATIONS:
 
 
 <ul>
 <li>
 1+ year of experience developing and maintaining ML pipelines
 </li>
 <li>
 Understanding of general software development concepts, including git, containers, testing, cloud computing, etc.
 </li>
 <li>
 Experience with modern deep learning frameworks, like PyTorch or JAX
 </li>
 <li>
 Experience with Python scientific computing tools, such as numpy, scipy, pandas, etc.
 </li>
 <li>
 Experience working with big data pipelines
 </li>
 <li>
 Experience working within Linux environments
 </li>
 <li>
 Ability to effectively communicate with a team of diverse backgrounds
 </li>
 <li>
 B.S. in computer science (or equivalent industry experience)
 
 
 </li>
 </ul>
 PREFERRED QUALIFICATIONS:
 
 
 <ul>
 <li>
 A solid background in machine learning, including linear algebra, statistics, and calculus
 </li>
 <li>
 Experience with modern generative modeling frameworks, such as flow matching and diffusion
 </li>
 <li>
 Familiarity with techniques for modeling point-clouds or graphs (GNNs, equivariant transformers, etc.)
 </li>
 <li>
 Familiarity with basic cheminformatics libraries and tools (RDKit, OpenBabel, PyMol, etc.)
 </li>
 <li>
 An interest in drug discovery and working with biochemical data
 
 
 </li>
 </ul>
 VILYA BENEFITS:
 
 
 <ul>
 <li>
 Opportunity to work in a disruptive startup with a talented, experienced, and dedicated team
 </li>
 <li>
 401(k) plan with employer matching for contributions
 </li>
 <li>
 Stock options
 </li>
 <li>
 Excellent medical, dental, and vision coverage
 </li>
 <li>
 Open, flexible vacation policy
 </li>
 <li>
 Support to attend professional conferences meaningful to your career growth
 </li>
 <li>
 Monthly cell phone and internet stipend
 </li>
 <li>
 Monthly wellness and commuter stipend
 
 
 </li>
 </ul>
 Salary range for Seattle, WA : $154,000 USD - $174,000 USD
 
 
</div>

Harvard University

<div></div><div><div><div><div><div><div>March 30, 2026
</div></div><div><div><div>002616SR</div></div></div></div></div></div></div>
 <div><div><div>Company Description 
By working at Harvard University, you join a vibrant community that advances Harvard's world-changing mission in meaningful ways, inspires innovation and collaboration, and builds skills and expertise. We are dedicated to creating a diverse and welcoming environment where everyone can thrive.
Why join Harvard Medical School?
Harvard Medical School's mission is to nurture a diverse, inclusive community dedicated to alleviating suffering and improving health and well-being for all through excellence in teaching and learning, discovery and scholarship, and service and leadership.
You&rsquo;ll be at the heart of biomedical discovery, education, and innovation, working alongside world-renowned faculty and a community dedicated to improving human health. This is more than a job - it&rsquo;s an opportunity to shape the future of medicine. 
Job Description 
The Core for Computational Biomedicine (CCB) in the Department of Biomedical Informatics (DBMI) at Harvard Medical School (HMS) is looking for a Machine Learning Engineer with advanced expertise to lead development of large language models (LLMs) to advance CCB&rsquo;s mission to leverage data and computation to transform research and education, and to improve health outcomes. CCB provides computational and analytic resources to advance scientific discovery within HMS through its multi-disciplinary team of computational and quantitative scientists who work on collaborative projects both within the center and with members of the HMS community. The selected candidate will play a pivotal role in advancing the center's mission to harness the power of computational techniques in the field of medicine. By developing medical LLMs, the engineer will contribute to educating the next generation of medical students and enhancing clinical decision-making processes. 
 
Key Responsibilities:<ul><li>
Develop, implement, and optimize medical large language models tailored to the needs of medical education and clinical decision support.</li><li>
Collaborate with interdisciplinary teams comprising biologists, clinicians, and data scientists to understand domain-specific requirements and translate them into computational solutions.</li><li>
Stay updated with the latest advancements in deep learning and machine learning to ensure the models developed are state-of-the-art.</li><li>
Develop infrastructures for data transformation and ingestion.</li><li>
Build AI models that make predictions based on large quantities of data.</li><li>
Explain the usefulness of the AI models created to stakeholders.</li><li>
Transform machine learning models into APIs to interact with other applications.</li><li>
Use expert knowledge to lead research AI and data science projects.</li></ul> 
Qualifications 
Basic Qualifications:
<ul><li>Minimum of seven years&rsquo; post-secondary education or relevant work experience.</li></ul> 

Additional Qualifications and Skills:
<ul><li>A Master's or PhD in Computer Science, Computational Biology, or a related field is strongly preferred.</li><li>
Minimum of 3 years of hands-on experience in developing complex deep learning solutions to tackle scientific challenges.</li><li>
Proficiency with the Python deep learning software stack, particularly expertise in PyTorch, Numpy, and related packages.</li><li>
Experience handling and processing large and diverse datasets, especially medical texts, journals, or electronic health records.</li><li>
Ability to collaborate effectively with non-technical stakeholders, such as doctors and medical researchers.</li><li>
Experience with experiment tracking and project management tools, notably frameworks like Weights &amp; Biases.</li><li>
Prior experience in fine-tuning large language models for specific tasks.</li><li>
Demonstrated experience in optimizing deep learning models for better performance and efficiency.</li><li>
Understanding of biology and/or medicine to bridge the gap between pure machine learning and its applications in the medical field.</li><li>
A track record of publications in technical conferences or journals.</li></ul> 
Additional Information<ul><li> 
Standard Hours/Schedule: 35 hours per week</li><li>
Visa Sponsorship Information: Harvard University is unable to provide visa sponsorship for this position.</li><li>
Pre-Employment Screening: Identity, Education, Criminal</li><li>
Other Information: Please note that we are currently conducting a majority of interviews and onboarding remotely and virtually. We appreciate your understanding.</li><li>
Staying Informed About Your Application: Due to the high volume of applications, we may not always be able to reach out right away, but you can track your status anytime through the Careers@Harvard portal.</li></ul>
#LI-DK1<div></div>
Work Format Details
This position has been determined by school or unit leaders that some of the duties and responsibilities can be effectively performed at a non-Harvard location. The work schedule and location will be set by the department at its discretion and based upon operational needs. When not working at a Harvard or Harvard-designated location, employees in hybrid positions must work in a Harvard registered state in compliance with the University&rsquo;s Policy on Employment Outside of Massachusetts. Additional details will be discussed during the interview process. Certain visa types and funding sources may limit work location. Individuals must meet work location sponsorship requirements prior to employment.

Salary Grade and Ranges
This position is salary grade level 060. Please visit Harvard's Salary Ranges to view the corresponding salary range and related information.

Benefits
Harvard offers a comprehensive benefits package that is designed to support a healthy work-life balance and your physical, mental and financial wellbeing. Because here, you are what matters. Our benefits include, but are not limited to:<ul><li>
Generous paid time off including parental leave</li><li>
Medical, dental, and vision health insurance coverage starting on day one</li><li>
Retirement plans with university contributions</li><li>
Wellbeing and mental health resources</li><li>
Support for families and caregivers</li><li>
Professional development opportunities including tuition assistance and reimbursement</li><li>
Commuter benefits, discounts and campus perks</li></ul>
Learn more about these and additional benefits on our Benefits &amp; Wellbeing Page.

EEO/Non-Discrimination Commitment Statement
Harvard University is committed to equal opportunity and non-discrimination. We seek talent from all parts of society and the world, and we strive to ensure everyone at Harvard thrives. Our differences help our community advance Harvard's academic purposes.
Harvard has an equal employment opportunity policy that outlines our commitment to prohibiting discrimination on the basis of race, ethnicity, color, national origin, sex, sexual orientation, gender identity, veteran status, religion, disability, or any other characteristic protected by law or identified in the university's non-discrimination policy. Harvard's equal employment opportunity policy and non-discrimination policy help all community members participate fully in work and campus life free from harassment and discrimination.</div></div></div>

Stealth Startup

<div>
 
 The Culture We Are Building
 
 <ul>
 <li>
 We are assembling a lean group of generalists. While we welcome experience, we're primarily looking for creative, curious problem solvers with a keen interest in open-ended, technically challenging problems.
 </li>
 <li>
 We are looking to hire in-person in San Francisco. We believe that having a community is critical for learning, growth, and camaraderie. We will pay generously for relocation assistance.
 </li>
 <li>
 We want to invest heavily in our team's growth. We will happily sponsor conference attendance and encourage projects and research. If there's something that will help you grow as an engineer, researcher, or designer, we actively want to support it.
 </li>
 <li>
 We believe in compensating exceptional talent exceptionally well. We are an early stage company, but are committed to working with candidates we're excited about to ensure they are excited too.
 </li>
 <li>
 We have an academic as well as competitive spirit. We enjoy thought exercises, math puzzles, intellectual inquiry, and vigorous debates.
 </li>
 </ul>
 
 
 
 
 Role: Machine Learning Engineer
 
 
 Design, train, and ship models that guide reviewers to the most important changes, resolve merge conflicts more safely, and make diffs understandable through semantic grouping and summarization. You'll combine retrieval, code‑aware modeling, and small‑model techniques to deliver low‑latency, cost‑effective ML in production.
 
 
 Responsibilities
 
 <ul>
 <li>
 -Train and fine‑tune models that predict the next location a reviewer should focus on: ranking files, hunks, and comments
 </li>
 <li>
 -Build ML‑assisted merge conflict resolution: align and reconcile hunks using code context/AST, propose safe resolutions, and explain changes
 </li>
 <li>
 -Develop semantic change grouping and diff visualization: detect refactors/moves, cluster related edits across files, and generate concise summaries
 </li>
 <li>
 -Evaluate retrieval and RAG for pull requests: retrieve relevant context, discussions, and history to improve grouping, prioritization, and summarization
 </li>
 <li>
 -Design offline/online evaluation: labeled datasets, ranking metrics (e.g., MAP/NDCG), A/B experiments, and human‑in‑the‑loop feedback
 </li>
 <li>
 -Operate efficient inference: apply small‑model techniques (LoRA, supervised fine‑tuning, distillation, quantization)
 </li>
 </ul>
 
 Qualifications
 
 <ul>
 <li>
 -Strong background in deep learning research and implementation, with experience in modern architectures and concepts (transformers, MoE, reasoning, etc.)
 </li>
 <li>
 -Track record publishing results at top-tier conferences (NeurIPS, ICML, ICLR)
 </li>
 <li>
 -Expertise in Python and ML frameworks (PyTorch, JAX)
 </li>
 <li>
 -Experience with MLOps and model optimization (ONNX, TensorRT, serving infrastructure)
 </li>
 <li>
 -Familiarity with cost‑effective techniques: LoRA, supervised fine‑tuning, distillation, and quantization for small/efficient models
 </li>
 <li>
 -Familiarity with systems programming across the stack: Rust/C/C++, CUDA/GPU optimization, and kernel-level performance tuning
 </li>
 <li>
 -Experience with reinforcement learning, particularly in systems optimization and decision-making tasks
 </li>
 </ul>
 
 
</div>

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