Machine Learning Research Engineer
Profluent · Emeryville, California, United States; Hybrid (2-3 days on-site)
Posted about 2 months ago
or apply directly on Profluent's site. We never take the application ourselves.
Is this posting real?
- This role has been open
- 63 days Profluent's roles stay open a median of 63 days
- Reposted
- No
- Salary listed
- No 0% of Profluent's roles list one
- Ghost-job risk at Profluent
- high 12 stale, 0 reposted of 12 open
- Hiring momentum
- 17 roles opened in the last 90 days ↑ up vs. the prior 90 days
- Last confirmed on the employer's board
- 2026-10-05
Measured from postings appearing on and disappearing from Profluent's own greenhouse board since 2026-08-03. Full hiring picture for Profluent.
About this role
As a Machine Learning Research Engineer at Profluent, you will focus on building and optimizing large-scale generative models for protein design. Your responsibilities include developing user-friendly pipelines for model fine-tuning, creating scalable ETL pipelines for processing protein data, and collaborating with scientists to bring research ideas into production. This role offers significant ownership of the ML stack within a fast-paced engineering team.
- benefits
- 3/5
- freshness
- 1/5
- career value
- 5/5
- role clarity
- 5/5
- pay transparency
- 0/5
Scored from the posting itself — how clearly the role is described, how much it says about pay and benefits, and how recently it was listed. Not a judgement of Profluent as an employer.
What you need
- BS or MS in Computer Science, Machine Learning, or a related field
- 3+ years of hands-on experience building and training ML models in PyTorch
- Strong Python and software engineering fundamentals, including testing, code quality, and version control
- Experience profiling, benchmarking, and optimizing ML model training and inference
- Experience implementing or optimizing transformer-based architectures
- Familiarity with cloud infrastructure and containerization (GCP, AWS, Azure, Kubernetes, Docker)
Nice to have
- Familiarity with protein language models or computational biology
- Experience with GPU-level optimization (CUDA, Triton)
- Experience with distributed training (DDP, FSDP, multi-node GPU clusters)
- Experience with databases and data processing pipelines
- Experience orchestrating multi-step ML workflows
What you get
- High-growth opportunity with meaningful impact on the future of protein design
- Competitive compensation package with equity participation
- 401(k) with a strong employer match
- Comprehensive benefits including health/dental/vision insurance
- Generous PTO policy and commitment to work-life balance
- Professional development opportunities in a cutting-edge field at the intersection of AI and biology
Worth weighing
- No specific mention of remote work flexibility beyond hybrid model
- The role involves significant ownership, which may require a high level of independence
- Focus on protein design may limit exposure to other areas of machine learning
Summarised from Profluent's posting. Read the full original.
Listed by Profluent on their greenhouse job board, last confirmed open on 2026-10-05. PitchMeAI is not the employer.
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