Machine Learning Researcher

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