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Applied ML Researcher

extra holidays - extra parental leave

Remote:

Full Remote

Contract:

Full time

Work from:

California (USA)

Offer summary

Qualifications:

Strong understanding of machine learning principles, Proficiency in Python & deep learning, Experience with LLMs & big data.

Key responsabilities:

Conduct research on advanced ML techniques for LLM optimization
Collaborate with stakeholders for LLM alignment
Formulate strategies for LLM alignment with product goals
Gather requirements from diverse teams for LLM refinement

Grid Dynamics Large https://www.griddynamics.com/

1001 - 5000 Employees

See more Grid Dynamics offers

Job description

Description

Position at Grid Dynamics

Our customer is an American multinational technology company headquartered in San Ramon, California. Our customer is one of the world's largest technology companies based in Silicon Valley with operations all over the world. On this project, we are working with bleeding-edge big data technologies to develop a high-performance data analytics platform, which handles petabytes of data. Enhance fleet health data for evolving ML machine deployments through innovations like PRISM, SHAG, and Dark Sky. Your role involves refining big data pipelines, creating new ones, and ensuring code reliability to optimize spare part logistics and minimize shortages. Make a direct impact on the efficiency and reliability of our cutting-edge data center operations.

Responsibilities :

Conduct in-depth research on advanced techniques such as Supervised Fine Tuning (SFT) and Reinforcement Learning with Human Feedback (RLHF) to optimize the alignment of Language Models (LLMs).
Collaborate effectively with both technical and non-technical stakeholders, ensuring a seamless alignment of LLMs for specific use cases.
Formulate and implement robust strategies to guarantee LLM alignment with product feature goals, with a specific emphasis on preventing hallucinations.
Systematically gather requirements from diverse teams, including data engineering, product features, research, and evaluation, to develop sophisticated modeling, prompting, and data transformation techniques for LLM refinement.

Requirements:

Strong understanding of machine learning principles, especially in the context of LLMs.
Knowledgeable about different LLM approaches for aligning LLMs (SFT, RLHF, etc.).
Capability to own and pursue a research agenda, including selecting impactful research problems and autonomously executing projects.
More than 5 years of proficiency in Python, including machine learning packages such as Jax/Tensorflow or PyTorch.
Experience in building deep learning solutions at scale.
BS/BA or an equivalent degree in computer science or a related field (PhD preferred).

What we offer:

Opportunity to work on cutting-edge projects.
Work with a highly motivated and dedicated team.
Competitive salary.
Flexible schedule.
Benefits package, including but not limited to: medical, dental, vision, etc.
Corporate social events.
Professional development opportunities.

NB:

Placement and Staffing Agencies need not apply. We do not work with C2C at this time.

At this moment, we are not able to process H1B transfers. Applicants with CPT and OPT visas are welcome to apply.

About Us:

Grid Dynamics (Nasdaq:GDYN) is a digital-native technology services provider that accelerates growth and bolsters competitive advantage for Fortune 1000 companies. Grid Dynamics provides digital transformation consulting and implementation services in omnichannel customer experience, big data analytics, search, artificial intelligence, cloud migration, and application modernization. Grid Dynamics achieves high speed-to-market, quality, and efficiency by using technology accelerators, an agile delivery culture, and its pool of global engineering talent. Founded in 2006, Grid Dynamics is headquartered in Silicon Valley with offices across the US, UK, Netherlands, Mexico, and Central and Eastern Europe.

To learn more about Grid Dynamics, please visit www.griddynamics.com. Follow us on Facebook, Twitter, and LinkedIn.