9 months ago

Senior / Engineer II, AI Lab Research Engineer

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 15 hours agoStatus: Live

Job Summary

Research Engineer role focused on building agentic AI systems for scientific domains, enabling sequential decision-making and multi-step reasoning across biology and materials science. You will work with LLMs, agent architectures, and modern ML frameworks to create scalable, lab-integrated AI solutions, collaborate across disciplines, and contribute to benchmarks, reliability, and take part in domain-specific experimentation and evaluation.

Location: Cambridge, San Francisco

Workplace: Hybrid

Employment Type: Full time

Job Function: Research & Scientific (R&D)

Key Responsibilities

•Develop and refine agentic AI systems for scientific workflows, enabling sequential decision-making and multi-step reasoning in lab settings
•Design and implement workflow/code generation pipelines translating natural language intents into executable steps for lab instruments
•Create, maintain, and evaluate benchmarks, test suites, and telemetry to measure capability and progress toward scientific goals
•Collaborate with researchers across disciplines to integrate simulations and real lab systems into AI solutions
•Contribute to publications, open-source projects, and domain-specific evaluation harnesses for AI in science

Pay and Benefits

Salary: USD 148,000 - 240,000 annually

Equity and Bonus:Equity

Key Requirements

•PhD or Masters in a quantitative discipline (e.g., Computer Science, Physics, Mathematics, Engineering) with a strong background in machine learning and one domain of science (e.g. biology or materials science)
•Strong grasp of LLMs and agent architectures (planning, tool use, structured function calling, code generation) and how to adapt them to domains
•Proficiency in modern ML frameworks (e.g., PyTorch, TensorFlow, JAX) and experience implementing scalable solutions for complex tasks
•Comfort collaborating across disciplines and interfacing with simulations and real lab systems
•Excellent collaboration and communication across teams in a research environment

Experience:AIMachine learningScience

Education:Master's

Skills:LLMsAgent architecturesMachine learningPyTorchTensorFlowJAX

Languages:English

Tech Stack:PyTorchTensorFlowJAXLLMsAgent architectures

Company Brief

Lila Sciences

Industry: Biotech

Website

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Lila Sciences

9 months ago

Senior / Engineer II, AI Lab Research Engineer

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 15 hours agoStatus: Live

Job Summary

Location: Cambridge, San Francisco

Workplace: Hybrid

Employment Type: Full time

Job Function: Research & Scientific (R&D)

Key Responsibilities

•Develop and refine agentic AI systems for scientific workflows, enabling sequential decision-making and multi-step reasoning in lab settings
•Design and implement workflow/code generation pipelines translating natural language intents into executable steps for lab instruments
•Create, maintain, and evaluate benchmarks, test suites, and telemetry to measure capability and progress toward scientific goals
•Collaborate with researchers across disciplines to integrate simulations and real lab systems into AI solutions
•Contribute to publications, open-source projects, and domain-specific evaluation harnesses for AI in science

Pay and Benefits

Salary: USD 148,000 - 240,000 annually

Equity and Bonus:Equity

Key Requirements

•PhD or Masters in a quantitative discipline (e.g., Computer Science, Physics, Mathematics, Engineering) with a strong background in machine learning and one domain of science (e.g. biology or materials science)
•Strong grasp of LLMs and agent architectures (planning, tool use, structured function calling, code generation) and how to adapt them to domains
•Proficiency in modern ML frameworks (e.g., PyTorch, TensorFlow, JAX) and experience implementing scalable solutions for complex tasks
•Comfort collaborating across disciplines and interfacing with simulations and real lab systems
•Excellent collaboration and communication across teams in a research environment

Experience:AIMachine learningScience

Education:Master's

Skills:LLMsAgent architecturesMachine learningPyTorchTensorFlowJAX

Languages:English

Tech Stack:PyTorchTensorFlowJAXLLMsAgent architectures

Company Brief

Lila Sciences

Industry: Biotech

Website