Machine Learning Researcher

Raydar; San Francisco (CA); On-site; USD 200,000 - 300,000; Full time

Benefits offered by this job

Equity

Health, medical and dental coverage

Job summary

Raydar is recruiting for a senior research role in San Francisco, leading research from hypothesis to working model and tying offline experiments to real-world results. You will work across model training, data pipelines and evaluation design, partnering with domain specialists to turn findings into robust systems.

The ideal candidate has hands-on experience training large models and a track record of turning unclear questions into production-ready solutions, while mentoring colleagues and

Qualifications

• Proven track record building and refining large neural networks with clear results.
• Mastery of a core ML technique (RL, LM scaling, data generation, or world models).
• Fluent Python-based deep learning coding skills.
• Hands-on experience with distributed training frameworks.
• Ability to design repeatable tests and guard against shortcuts.
• History of bringing open-ended research to production-grade models.
• PhD studies count toward experience; 5–10 years in research/engineering preferred.
• Ability to quickly learn unfamiliar technical domains.

Responsibilities

• Modify and train large models from base-training to instruction tuning.
• Build RL methods that learn from real-world outcomes.
• Design synthetic-data pipelines with provenance and quality controls.
• Create benchmarks to generalize to new problems and relate offline to real-world performance.
• Run and troubleshoot large distributed training jobs and optimize costs.
• Analyze learned models for prediction, search, and planning with strong baselines.
• Collaborate with domain specialists and engineers to prioritize improvements.
• Help set research agenda, raise engineering practices, and share work via papers/open-source.

Skills

Large NN development

Core ML technique mastery

Python programming

Distributed training

Experiment tooling

Research-to-production

Experience 5-10 years

Rapid learning

Education

PhD or doctoral study counted toward experience

Tools

FSDP

DeepSpeed

Megatron

Job description

About the company

Our client is a fast-growing chemicals company.

The role

Raydar is recruiting for this role on behalf of our client. Lead research work from early hypothesis to working model, tying gains from offline experiments to results seen in practice. Work across model training, data pipelines and evaluation design, partnering with domain specialists to turn findings into better models. Well suited to a senior researcher who has trained large models hands-on and has turned unclear questions into systems that others rely on.

What you'll do

• Modify and train large models, from additional base-training passes to instruction tuning, selecting data blends and objectives suited to specialized data.
• Build reinforcement learning methods that learn from real-world outcomes despite delayed, noisy and limited feedback.
• Design synthetic-data and knowledge-distillation pipelines with careful provenance tracking, filtering and quality controls.
• Create benchmarks and evaluations that test generalization to unfamiliar problems and connect offline results to practical performance.
• Run and troubleshoot large distributed training jobs, improving throughput, checkpointing and the cost of serving models.
• Study learned models for prediction, search and planning using controlled ablations and strong baselines.
• Work with domain specialists and product engineers to set priorities, analyze failures and turn results into model improvements.
• Help set the research agenda, raise engineering practice, mentor colleagues and share work through papers or open-source releases where suitable.

Requirements

What we're looking for

• Proven experience building and refining large neural networks, with a clear account of what drove results and what did not.
• Mastery of a core technique, whether learning from rewards, scaling language models, generating training data or planning with learned world models.
• Fluent coding skills for research in a Python-based deep learning stack.
• Hands-on experience with distributed training frameworks such as FSDP, DeepSpeed or Megatron.
• Skill in building repeatable tests that catch shortcuts, such as leaked test data, gamed rewards or shifts in input patterns.
• A history of carrying open-ended research questions all the way to production-grade models that others depend on.
• Five to ten years in research or engineering roles, with doctoral study counting toward that experience.
• Ability to learn an unfamiliar technical domain quickly.

Bonus points

• Experience with scientific data, multimodal data or experimental science.
• Familiarity with learning from fixed datasets, judging model confidence, choosing which experiments to run next, or planning when the model is imperfect.
• Skill in making models faster and cheaper to run, such as through weight compression or lower-precision arithmetic.
• A record of papers, public code or shipped systems that show inventive problem-solving and solid delivery.

Benefits

Compensation and benefits

• Base salary: USD 200,000 to 300,000 per year
• Equity
• Health, medical and dental coverage

Location and work model

• San Francisco, CA, United States
• On-site
• Full-time