Founding AI Research Engineer — Quantitative ML for Defense

Acceler8 Talent

San Francisco (CA)

On-site

USD 150,000 - 240,000

Full time

14 days+

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Job summary

Acceler8 Talent is partnering with a fast-growing defence AI startup to hire a Founding Research Engineer. You will build quantitative prediction and agent-learning systems designed to detect and conduct a novel class of AI-powered cyber operation.

If you are a quantitative researcher, developer or applied ML engineer with strong empirical judgement and hands-on coding ability, this role is for you. Experience with noisy time-series, signal processing or behavioural modelling is highly valued,

Qualifications

  • Quantitative researcher, developer or applied ML engineer.
  • Strong empirical judgement and hands-on coding ability.
  • Experience with noisy time-series, signal processing or behavioural modelling.
  • Personally owned models from hypothesis through deployment.
  • Comfortable with both statistical methods and deep learning.
  • High-agency and effective in ambiguous environments.
  • Cybersecurity experience is not required.

Responsibilities

  • PB-scale multimodal time-series data refreshing in under a second.
  • Building streaming and data-capture infrastructure.
  • Creating useful representations from noisy, unstructured signals.
  • Modelling actor behaviour using statistical models, deep learning and transformers.
  • Training agents against sparse, long-horizon rewards.

Skills

Quantitative researcher
Applied ML engineer
Strong coding ability
Time-series analytics
Statistical methods
Deep learning

Job description

Acceler8 Talent is partnering with a fast-growing defence AI startup to hire a Founding Research Engineer. You will build quantitative prediction and agent-learning systems designed to detect and conduct a novel class of AI-powered cyber operation.

If you are a quantitative researcher, developer or applied ML engineer with strong empirical judgement and hands-on coding ability, this role is for you. Experience with noisy time-series, signal processing or behavioural modelling is highly valued,

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